{ "edition": "2026-11-04", "name": "Rubric, edition of Wednesday 4 November 2026", "url": "https://rubric.hrih.si/editions/2026-11-04/", "published_at": "2026-11-04T06:00:00Z", "rubric_version": "2.3", "how_ranking_works": "https://rubric.hrih.si/how-ranking-works/", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "attribution": "Rubric (rubric.hrih.si)", "notice": "CC BY 4.0, Rubric (rubric.hrih.si), edition of 2026-11-04: https://rubric.hrih.si/editions/2026-11-04/" }, "models": { "jev": "jev-1.14.0", "judge_a": "claude-haiku-5-5", "judge_b": "gpt-5.6-luna", "writer": "claude-sonnet-5-5", "claim_check": "claude-sonnet-5-5 + jev-1.14.0", "summarizer": "claude-haiku-5-5" }, "fields": { "rank": "Place in the edition; since rubric 2.3 after the placement terms (see score_rank), before it the place by score", "id": "The story id in Rubric", "title": "The title as shown", "url": "The source: the original article, paper or release", "domain": "The host name of the source", "site": "The site of the source, as Rubric names it", "categories": "Topic keys; \"other\" when the story fits none", "category_names": "The topics as the site names them", "format": "The kind of piece: paper, write-up, release, analysis, tutorial, news, new project", "depth": "How far the story was checked: claim_checked, three_judges or two_judges", "published_at": "The publication time of the source, when known", "jev": "Judge 1 (jev-1.14.0): its own weighted sum of the rubric questions, 0 to 1, before penalties; null when it did not score the story", "judge_a": "Judge 2 (claude-haiku-5-5): its own weighted sum of the rubric questions, 0 to 1, before penalties; null when it did not score the story", "judge_b": "Judge 3 (gpt-5.6-luna): its own weighted sum of the rubric questions, 0 to 1, before penalties; null when it did not score the story", "score": "The published total: the mean answer of the panel per question, weighted, minus the claim-check and freshness penalties", "contested": "True when the judges disagreed on at least one question by more than the contested threshold", "flag_penalty": "Taken off the total for claims the checkers both flagged", "freshness_penalty": "Taken off the total for older work", "score_rank": "Rubric 2.3+: the place by score alone; null in editions ranked before it", "topic_penalty": "Rubric 2.3+: taken off for placement only, for stories of the same topic placed above it beyond the free ones; not part of score", "repeat_penalty": "Rubric 2.3+: taken off for placement only, for similarity to a story placed above it today or ranked in a recent edition; not part of score", "summary": "The summary of the source by Rubric, written by AI", "page": "The page of the story on Rubric, with the full breakdown", "card": "The social card of the story (PNG, 1200 × 630)", "breakdown": "JSON only: the weight of each question and the answer of each judge, with the mean and spread of the panel" }, "stories": [ { "rank": 1, "id": "st_20261104_13774", "title": "Ongoing NPM attack adminide-stack tab-padded eval(atob) with an ETH dead-drop", "url": "https://blog.greenflagged.dev/posts/2026-11-03-adminide-stack-eth-dead-drop/", "domain": "blog.greenflagged.dev", "site": "Greenflagged Blog", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-11-03T23:09:37.000Z", "jev": 0.7549, "judge_a": 0.799, "judge_b": 0.8553, "score": 0.803, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 1, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Greenflagged reports an ongoing npm compromise: on 2026-11-03 the account clockbook-app published malicious versions of @adminide-stack packages and server-hemera-mongo, each appending a tab-padded eval(atob(...)) loader to the entry file. The script reads an Ethereum wallet's latest transaction, derives a C2 IPv4 address from it, and fetches the next stage. Plain installs of utils and client-redux now resolve to bad versions. The analysis was static; stage 2 was not examined. The figures come from Greenflagged alone, with no independent source cited.", "page": "https://rubric.hrih.si/editions/2026-11-04/1/", "card": "https://rubric.hrih.si/og/2026-11-04/1.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.668, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7158, "spread": 0.113 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.86, "judge_a": 0.93, "judge_b": 0.96, "mean": 0.9167, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.57, "judge_a": 0.8, "judge_b": 0.67, "mean": 0.68, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.863, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8708, "spread": 0.05 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.9367, "spread": 0.07 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.92, "judge_b": 0.95, "mean": 0.9558, "spread": 0.078 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.93, "judge_b": 0.98, "mean": 0.9333, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.12, "judge_b": 0.03, "mean": 0.0667, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.15, "judge_b": 0.04, "mean": 0.1433, "spread": 0.2 } } }, { "rank": 2, "id": "st_20261104_13524", "title": "Tencent-owned game's CDN compromised to deliver trojan via PC launcher", "url": "https://gist.github.com/MotherSphere/d6c049a09ba8ac3085c51ee9df24a4e5", "domain": "gist.github.com", "site": "Gist", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-11-03T19:25:58.000Z", "jev": 0.688, "judge_a": 0.7945, "judge_b": 0.8605, "score": 0.781, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 2, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A GitHub Gist incident report, built from one infected machine and victims' comments, says four JavaScript files on Level Infinite's CDN, loaded by the NIKKE PC launcher, carried attacker code from Sep 27. From Oct 24 at the latest, some launches installed a remote-control agent that runs commands, reads files and takes screenshots. Three files were replaced on Nov 3; two CDN servers still served the fourth that evening. The payload was analyzed statically only, and the claims lack independent sourcing. How the attacker gained CDN write access is unknown.", "page": "https://rubric.hrih.si/editions/2026-11-04/2/", "card": "https://rubric.hrih.si/og/2026-11-04/2.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.72, "judge_b": 0.82, "mean": 0.7083, "spread": 0.235 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.8967, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.357, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.5122, "spread": 0.243 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.818, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.8592, "spread": 0.093 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.9311, "spread": 0.063 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.95, "judge_b": 0.97, "mean": 0.9725, "spread": 0.048 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.0233, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1367, "spread": 0.15 } } }, { "rank": 3, "id": "st_20261104_13740", "title": "0-click account takeover in Better Auth", "url": "https://www.strix.ai/blog/better-auth-expo-signing-oracle", "domain": "strix.ai", "site": "Strix", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T22:50:44.000Z", "jev": 0.7201, "judge_a": 0.7163, "judge_b": 0.8089, "score": 0.7484, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 3, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Security firm Strix reports a critical zero-click account takeover in @better-auth/expo, Better Auth's React Native integration. The /expo-authorization-proxy endpoint signed any caller-supplied OAuth state with the app secret, without a login. Because email-verification JWTs used the same HMAC-SHA256 secret, a forged token could change a victim's email and yield a session; the compact session cache was forgeable too, though not the jwe strategy. Fixed in version 1.7.16. The lesson for builders: avoid signing oracles and reusing one key across features.", "page": "https://rubric.hrih.si/editions/2026-11-04/3/", "card": "https://rubric.hrih.si/og/2026-11-04/3.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.64, "judge_a": 0.62, "judge_b": 0.72, "mean": 0.66, "spread": 0.1 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.8767, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.52, "judge_a": 0.6, "judge_b": 0.55, "mean": 0.5567, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.833, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8308, "spread": 0.06 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.9, "judge_a": 0.92, "judge_b": 0.9, "mean": 0.9067, "spread": 0.02 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.9, "judge_b": 0.88, "mean": 0.9225, "spread": 0.108 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9033, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.06, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.1667, "spread": 0.22 } } }, { "rank": 4, "id": "st_20261104_12625", "title": "A founding document of mechanistic interpretability [Elhage et al. 2021]", "url": "https://transformer-circuits.pub/2021/framework/index.html", "domain": "transformer-circuits.pub", "site": "transformer-circuits.pub", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T05:33:36.000Z", "jev": 0.6204, "judge_a": 0.7592, "judge_b": 0.8448, "score": 0.7414, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 5, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Anthropic researchers, led by Nelson Elhage, published a mathematical framework for reverse-engineering transformers, restricted to toy models of two layers or fewer with no MLP blocks. Rewriting attention as independent, additive heads with separate QK and OV circuits lets bigram and skip-trigram tables be read from the weights. In two-layer models, composed heads form \"induction heads\" that account for in-context learning. The authors acknowledge that MLP layers remain poorly understood, and that the results are not yet shown for large models.", "page": "https://rubric.hrih.si/editions/2026-11-04/4/", "card": "https://rubric.hrih.si/og/2026-11-04/4.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.848, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8658, "spread": 0.052 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.7967, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.623, "judge_a": 0.7, "judge_b": 0.75, "mean": 0.6911, "spread": 0.127 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.465, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.5717, "spread": 0.235 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.877, "judge_a": 0.9, "judge_b": 0.92, "mean": 0.8989, "spread": 0.043 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.935, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.9283, "spread": 0.05 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.9267, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.0233, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.2033, "spread": 0.25 } } }, { "rank": 5, "id": "st_20261104_13294", "title": "envoyproxy/envoy v1.40.0", "url": "https://github.com/envoyproxy/envoy/releases/tag/v1.40.0", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "security", "devops", "it" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-11-03T16:09:28.000Z", "jev": 0.7602, "judge_a": 0.664, "judge_b": 0.7925, "score": 0.7388, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 6, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Envoy v1.40.0 moves its build from WORKSPACE to bzlmod, so downstreams must migrate to MODULE.bazel and add Envoy's own registry. It also raises the hermetic Clang toolchain from 18 to 22 and removes AWS-LC as a selectable TLS library. The release declares HTTP/3 and QUIC stable (a claim not independently verified) and adds X25519MLKEM768 hybrid key exchange to the default TLS curves. mcp_router no longer forwards the authorization header to backends by default, and container-aware CPU detection now applies whenever --concurrency is unset.", "page": "https://rubric.hrih.si/editions/2026-11-04/5/", "card": "https://rubric.hrih.si/og/2026-11-04/5.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.743, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7075, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7567, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.597, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5722, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.913, "judge_a": 0.8, "judge_b": 0.85, "mean": 0.8542, "spread": 0.112 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.79, "judge_a": 0.75, "judge_b": 0.93, "mean": 0.8233, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.853, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8042, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9367, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.04, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.11, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0967, "spread": 0.03 } } }, { "rank": 6, "id": "st_20261104_13163", "title": "Four open coding models prompted to finish an upload URL – analysis", "url": "https://int8.io/forced-curl/", "domain": "int8.io", "site": "Int8", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T15:12:18.000Z", "jev": 0.746, "judge_a": 0.65, "judge_b": 0.8167, "score": 0.7375, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 7, "topic_penalty": 0, "repeat_penalty": 0.0037, "summary": "Int8's author forced four open 9–12B coding models (Qwen3.5-9B, GLM-4-9B, Gemma 4 12B, Nemotron-Nano-9B) to finish an upload curl URL, 100,000 times each, after Cohere's report on a Qwen agent. Qwen named Alibaba Cloud 13 times as often as GLM, but the hosts were generic and it named OpenAI almost as often, which suggests a training-data fingerprint rather than calling home. The figures are the author's own and unverified, and the 40-token cap makes counts lower bounds. Egress allowlists and no ambient cloud credentials remain the advice.", "page": "https://rubric.hrih.si/editions/2026-11-04/6/", "card": "https://rubric.hrih.si/og/2026-11-04/6.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.663, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6442, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.8533, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6911, "spread": 0.153 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.79, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.77, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8978, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8875, "spread": 0.183 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.79, "judge_a": 0.7, "judge_b": 0.98, "mean": 0.8233, "spread": 0.28 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.1967, "spread": 0.13 } } }, { "rank": 7, "id": "st_20261104_13556", "title": "Nine engineers, 1,211 PRs in a day: Lessons from building Copilot Cowork", "url": "https://commandline.microsoft.com/how-we-built-copilot-cowork-python-to-rust-migration/", "domain": "commandline.microsoft.com", "site": "Command Line", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T19:54:21.000Z", "jev": 0.7768, "judge_a": 0.5852, "judge_b": 0.8345, "score": 0.7321, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 8, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Microsoft's Command Line blog describes how nine engineers took Copilot Cowork from an empty repo on January 13 to general availability on June 16, with agents writing most of the code, then replaced the live Python runtime with Rust by October 10. It reports 1,211 merged PRs in one day and median time to first token falling from 7.9 s to 2.7 s, partly because the agent loop also moved. Practices include specs first, replayable fixtures, and checks that must fail on purpose. All figures are self-reported.", "page": "https://rubric.hrih.si/editions/2026-11-04/7/", "card": "https://rubric.hrih.si/og/2026-11-04/7.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.718, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6492, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.82, "judge_b": 0.97, "mean": 0.87, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.677, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7189, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.93, "judge_a": 0.75, "judge_b": 0.91, "mean": 0.8633, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.943, "judge_a": 0.6, "judge_b": 0.89, "mean": 0.8111, "spread": 0.343 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.963, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8542, "spread": 0.263 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.88, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2067, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1933, "spread": 0.22 } } }, { "rank": 8, "id": "st_20261104_13282", "title": "LLMjacking: The AWS, Google Cloud and Azure settings that cap stolen AI keys", "url": "https://theainewsreport.com/2026-11-03-llmjacking-stolen-ai-keys-explained.html", "domain": "theainewsreport.com", "site": "AI News Report", "categories": [ "ai", "security", "devops" ], "category_names": [ "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Tutorial", "depth": "claim_checked", "published_at": "2026-11-03T16:00:43.000Z", "jev": 0.7609, "judge_a": 0.681, "judge_b": 0.8022, "score": 0.748, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 4, "topic_penalty": 0.02, "repeat_penalty": 0.0038, "summary": "Google's Threat Intelligence Group reports that attackers using stolen cloud tokens and AI keys ran up more than $100,000 in a day, and up to $1 million at some organizations, in what it calls LLMjacking. AI News Report turns the advice into vendor settings: per-deployment tokens-per-minute quotas act within minutes, while budget alerts lag, and Bedrock invocation logging and Google Data Access logs are off by default. The figures are GTIG's own, and the LiteLLM CVE it cites could not be verified against public records.", "page": "https://rubric.hrih.si/editions/2026-11-04/8/", "card": "https://rubric.hrih.si/og/2026-11-04/8.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.72, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6633, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.84, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8467, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.767, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7156, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.935, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.925, "spread": 0.04 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.703, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.7711, "spread": 0.157 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.875, "spread": 0.215 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.79, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6533, "spread": 0.24 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.04, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.32, "spread": 0.26 } } }, { "rank": 9, "id": "st_20261104_12821", "title": "GitHub pull_request_target blocked by default December 2", "url": "https://github.blog/changelog/2026-11-02-pull_request_target-blocked-by-default-december-2/", "domain": "github.blog", "site": "The GitHub Blog", "categories": [ "programming", "security", "devops" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-11-03T11:15:33.000Z", "jev": 0.654, "judge_a": 0.6701, "judge_b": 0.8199, "score": 0.7146, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 11, "topic_penalty": 0, "repeat_penalty": 0, "summary": "GitHub will block pull_request_target events by default in public repositories on Free, Team and Enterprise Cloud starting December 2, 2026, a delay from the earlier November 2 date. Private, internal and Enterprise Server repositories are unaffected. The trigger runs with base-repo secrets, so executing fork code can leak them. Two brownouts, November 9 and 16 (16:00–18:00 UTC), will fail blocked runs. Teams can check Insights, then allow specific workflows through an Actions event policy. The claim check found no cited sources for these details.", "page": "https://rubric.hrih.si/editions/2026-11-04/9/", "card": "https://rubric.hrih.si/og/2026-11-04/9.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6425, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.74, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.67, "judge_b": 0.72, "mean": 0.63, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.923, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8908, "spread": 0.073 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.423, "judge_a": 0.7, "judge_b": 0.95, "mean": 0.6911, "spread": 0.527 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.73, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6333, "spread": 0.18 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.8933, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.1, "judge_b": 0.02, "mean": 0.0967, "spread": 0.15 } } }, { "rank": 10, "id": "st_20261104_12576", "title": "Mass attempted replication of old findings of LLMs with new models", "url": "https://zou-group.github.io/llm-claims-shelf-life/", "domain": "zou-group.github.io", "site": "zou-group.github.io", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T03:41:29.000Z", "jev": 0.69, "judge_a": 0.633, "judge_b": 0.8517, "score": 0.7249, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 9, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "A Zou group project retested 50 widely cited LLM findings on 20 models released between July 2024 and September 2026. By fitted trends, 44% of claims are expired (effect at or below a fifth of the original), 34% weakened, 16% persist and 6% strengthened. Capability failures such as fabricated citations faded fastest, while sycophancy and judge self-preference held. Expired claims lasted about 15.7 months on average. Each claim uses one minimal design, some models are substitutes, and individual models can still diverge, so findings about older models need retesting.", "page": "https://rubric.hrih.si/editions/2026-11-04/10/", "card": "https://rubric.hrih.si/og/2026-11-04/10.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.75, "judge_a": 0.55, "judge_b": 0.85, "mean": 0.7167, "spread": 0.3 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.8133, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.81, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.79, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.605, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6617, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.8611, "spread": 0.223 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.945, "judge_a": 0.6, "judge_b": 0.94, "mean": 0.8283, "spread": 0.345 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9367, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2833, "spread": 0.19 } } }, { "rank": 11, "id": "st_20261104_12769", "title": "A Big Ball of Mud", "url": "https://www.laputan.org/mud/", "domain": "laputan.org", "site": "laputan.org", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-11-03T10:20:45.000Z", "jev": 0.6288, "judge_a": 0.6162, "judge_b": 0.8257, "score": 0.6902, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 13, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Brian Foote and Joseph Yoder's 1997 PLoP paper, hosted at laputan.org, argues that the most common software architecture is the \"Big Ball of Mud,\" and that time, cost, experience, and changing requirements push even careful teams toward it. They treat it as a pattern to understand, not only an antipattern, and pair it with remedies: throwaway code, piecemeal growth, shearing layers, sweeping it under the rug, and reconstruction. The excerpt is partial, and several supporting figures and anecdotes in the full text have no cited source.", "page": "https://rubric.hrih.si/editions/2026-11-04/11/", "card": "https://rubric.hrih.si/og/2026-11-04/11.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.723, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7142, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.8, "judge_b": 0.92, "mean": 0.7567, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.903, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7944, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.675, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7317, "spread": 0.145 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.383, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5211, "spread": 0.397 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.585, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.555, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.1, "judge_b": 1, "mean": 0.6733, "spread": 0.9 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.0267, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.35, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.2233, "spread": 0.23 } } }, { "rank": 12, "id": "st_20261104_13552", "title": "Core to core is not core to core", "url": "https://jacen.li/writings/core-to-core-is-not-core-to-core/", "domain": "jacen.li", "site": "Jacen Li", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T19:55:51.000Z", "jev": 0.6123, "judge_a": 0.6143, "judge_b": 0.8114, "score": 0.6793, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 18, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Jacen Li reports that core-to-core latency on mesh-based Intel and Arm server chips depends on the physical address as well as the two cores, because each line's coherence \"home\" slice relays the transfer. Across the machines tested, best-to-worst lines differ by about 14 to 30 ns. Pinning rigtorp's SPSC queue lines to fast addresses cut round trips about 12 to 38% and shrank run-to-run variance. These figures are the author's own, from one benchmark and a limited set of machines, and not independently verified.", "page": "https://rubric.hrih.si/editions/2026-11-04/12/", "card": "https://rubric.hrih.si/og/2026-11-04/12.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.52, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5667, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.82, "judge_b": 0.93, "mean": 0.7933, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.45, "judge_b": 0.5, "mean": 0.4767, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7033, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.88, "judge_b": 0.94, "mean": 0.9344, "spread": 0.103 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.94, "judge_b": 0.98, "mean": 0.9725, "spread": 0.058 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.91, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0267, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.12, "judge_b": 0.06, "mean": 0.14, "spread": 0.18 } } }, { "rank": 13, "id": "st_20261104_13029", "title": "Modern CSS Might Make Your Front-End More Complex", "url": "https://www.smashingmagazine.com/2026/11/modern-css-might-make-your-front-end-more-complex/", "domain": "smashingmagazine.com", "site": "Smashing Magazine", "categories": [ "programming", "design" ], "category_names": [ "Programming & dev tools", "Design & UX" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-11-03T10:00:00.000Z", "jev": 0.7367, "judge_a": 0.5742, "judge_b": 0.7175, "score": 0.6761, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 19, "topic_penalty": 0, "repeat_penalty": 0, "summary": "In a Smashing Magazine essay, Gabriel Shoyombo argues that replacing JavaScript with modern CSS can shrink a diff while making the system harder to reason about. His examples: a leftover ResizeObserver class that fights a container query, container-type changing margins, floats and width, and :has() selectors outweighing existing rules. In his own Chrome test with 5,000 rows, two harmless :has() rules together restyled 5,001 elements per toggle, about 7ms, and 31ms at 20,000 rows. It is one page, one browser, and he has filed a Chromium issue.", "page": "https://rubric.hrih.si/editions/2026-11-04/13/", "card": "https://rubric.hrih.si/og/2026-11-04/13.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.608, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4858, "spread": 0.308 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.76, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.677, "judge_a": 0.5, "judge_b": 0.52, "mean": 0.5656, "spread": 0.177 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.935, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.835, "spread": 0.185 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.94, "judge_a": 0.68, "judge_b": 0.82, "mean": 0.8133, "spread": 0.26 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8758, "spread": 0.218 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.89, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0333, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1633, "spread": 0.08 } } }, { "rank": 14, "id": "st_20261104_13312", "title": "Move the Compute to the Data", "url": "https://morillocarlos.substack.com/p/move-the-compute-to-the-data", "domain": "morillocarlos.substack.com", "site": "morillocarlos.substack.com", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-11-03T16:47:53.000Z", "jev": 0.7036, "judge_a": 0.55, "judge_b": 0.761, "score": 0.6715, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 20, "topic_penalty": 0, "repeat_penalty": 0, "summary": "In a Substack post, Carlos Morillo argues that Spark beat Hadoop MapReduce mainly through DAG scheduling and by no longer writing intermediate results to triple-replicated HDFS between steps, not through in-memory caching. His evidence is Spark's 2014 100 TB sort, which ran on disk, and figures from the RDD and NSDI 2015 papers, which the claim check could not verify. Caching still helped iterative workloads. For platform builders, the suggestion is to count how often a pipeline writes data it reads only once. The central thesis is his own interpretation.", "page": "https://rubric.hrih.si/editions/2026-11-04/14/", "card": "https://rubric.hrih.si/og/2026-11-04/14.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5575, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7733, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.67, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.743, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7042, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.8633, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.96, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.9, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0433, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.32, "mean": 0.2867, "spread": 0.08 } } }, { "rank": 15, "id": "st_20261104_13001", "title": "Solar: 100× Faster Solidity", "url": "https://www.paradigm.xyz/writing/solar-100-faster-solidity", "domain": "paradigm.xyz", "site": "Paradigm", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-11-03T13:58:42.000Z", "jev": 0.6305, "judge_a": 0.612, "judge_b": 0.7454, "score": 0.6627, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 27, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Paradigm released Solar v0.3, a Rust Solidity compiler that now generates EVM bytecode and ships in Forge via forge build --use solar. By Paradigm's own benchmarks on six projects, it builds about 100× faster than solc with via-IR, and its output uses 5% less gas and is 29% smaller. It also compiled Uniswap v4 where solc hit \"Stack too deep\". Two rival compilers beat it on a few workloads. The figures are self-reported, and most lack independent sourcing.", "page": "https://rubric.hrih.si/editions/2026-11-04/15/", "card": "https://rubric.hrih.si/og/2026-11-04/15.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6933, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8067, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.483, "judge_a": 0.5, "judge_b": 0.45, "mean": 0.4778, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8167, "spread": 0.03 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.897, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8189, "spread": 0.197 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.82, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.21, "spread": 0.15 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.64, "judge_a": 0.4, "judge_b": 0.15, "mean": 0.3967, "spread": 0.49 } } }, { "rank": 16, "id": "st_20261104_13570", "title": "Sysctl settings for RDMA pods in K8s", "url": "https://oguzpastirmaci.com/posts/arp-rp-filter-and-accept-ra-for-rdma-pods-on-kubernetes/", "domain": "oguzpastirmaci.com", "site": "Under the Model", "categories": [ "devops", "it", "hardware" ], "category_names": [ "Infrastructure & DevOps", "IT & networking", "Hardware & devices" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T19:34:24.000Z", "jev": 0.6116, "judge_a": 0.583, "judge_b": 0.7802, "score": 0.6582, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 31, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Oguz Pastirmaci's write-up traces intermittent RDMA failures on 16-rail RoCE GPU nodes to Linux sysctls that are not set where Kubernetes places the interface. Interfaces take their settings from the pod namespace's conf/default. In his DRANET tests on OKE A100 nodes, setting arp_ignore=1 and arp_announce=2 fixed ARP mix-ups, and NCCL reached about 190 GB/s. For IPv6, accept_ra=2 was needed. The results come from one author's clusters, and some figures lack independent sources.", "page": "https://rubric.hrih.si/editions/2026-11-04/16/", "card": "https://rubric.hrih.si/og/2026-11-04/16.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.51, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7233, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.367, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.3789, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.748, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8158, "spread": 0.152 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.9011, "spread": 0.123 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.92, "judge_b": 0.95, "mean": 0.9567, "spread": 0.08 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0367, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1233, "spread": 0.07 } } }, { "rank": 17, "id": "st_20261104_12655", "title": "Autonomous Agents Need Mandatory Access Control", "url": "https://www.legiscope.com/blog/autonomous-agents-mandatory-access-control.html", "domain": "legiscope.com", "site": "Legiscope", "categories": [ "ai", "security", "devops" ], "category_names": [ "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-11-03T07:23:57.000Z", "jev": 0.7348, "judge_a": 0.5755, "judge_b": 0.7814, "score": 0.6972, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 12, "topic_penalty": 0.04, "repeat_penalty": 0.0039, "summary": "Legiscope proposes Agent Mandatory Access Control (AMAC), a draft profile in which an API accepts a deny-by-default policy with each agent task, returns the effective restrictions before execution, and refuses the run if any required enforcement point is missing. It draws on FreeBSD's MAC, Capsicum and jails, and on OWASP's Agent Control Standard. The author says OpenAI and Claude documentation already covers hosted network limits and permission policies, but application-executed tools remain the developer's job. AMAC is not an adopted standard, and Claude Managed Agents is documented as beta.", "page": "https://rubric.hrih.si/editions/2026-11-04/17/", "card": "https://rubric.hrih.si/og/2026-11-04/17.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.68, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6167, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.7967, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.733, "judge_a": 0.7, "judge_b": 0.73, "mean": 0.7211, "spread": 0.033 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.838, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.7892, "spread": 0.138 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.437, "judge_a": 0.4, "judge_b": 0.64, "mean": 0.4922, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.94, "judge_a": 0.72, "judge_b": 0.88, "mean": 0.8467, "spread": 0.22 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.55, "judge_b": 0.92, "mean": 0.7833, "spread": 0.37 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.08, "judge_b": 0.03, "mean": 0.0533, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1933, "spread": 0.22 } } }, { "rank": 18, "id": "st_20261104_12679", "title": "Fixes available in -proposed pocket for CVE-2026-98432", "url": "https://ubuntu.com/blog/fixes-available-in-proposed-pocket-for-cve-2026-98432", "domain": "ubuntu.com", "site": "Ubuntu", "categories": [ "security", "devops", "it" ], "category_names": [ "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-11-03T08:30:30.000Z", "jev": 0.6623, "judge_a": 0.7615, "judge_b": 0.7368, "score": 0.7201, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 10, "topic_penalty": 0.04, "repeat_penalty": 0.0329, "summary": "Canonical has put kernel fixes for CVE-2026-98432, a critical (CVSS 9.8) IPv4 TCP flaw that can allow remote code execution, into the -proposed pocket for Ubuntu 22.04, 24.04 and 26.04, with 26.10 already fixed. Older releases remain pending, and fixes reach the updates pocket only after testing on more than 2,000 certified devices. Canonical says disabling MPTCP is not a real mitigation. A blogger notes that Debian, Red Hat and SUSE shipped fixes earlier. The Anthropic-generated exploit is unreleased, and the claims here were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-11-04/18/", "card": "https://rubric.hrih.si/og/2026-11-04/18.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.765, "spread": 0.085 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.77, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.81, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.7767, "spread": 0.09 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.798, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.8658, "spread": 0.103 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.453, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6244, "spread": 0.367 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.803, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.5608, "spread": 0.402 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.76, "judge_a": 0.45, "judge_b": 0.24, "mean": 0.4833, "spread": 0.52 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.05, "judge_b": 0.12, "mean": 0.0967, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.1833, "spread": 0.07 } } }, { "rank": 19, "id": "st_20261104_13755", "title": "Postgres 19 REPACK: Built-In Online Table Bloat Cleanup", "url": "https://www.snowflake.com/en/blog/engineering/postgres-19-repack-table-bloat/", "domain": "snowflake.com", "site": "snowflake.com", "categories": [ "devops", "data" ], "category_names": [ "Infrastructure & DevOps", "Data" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T22:44:48.000Z", "jev": 0.5786, "judge_a": 0.583, "judge_b": 0.7672, "score": 0.6429, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 35, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Postgres 19 adds a built-in REPACK command. REPACK (CONCURRENTLY) rebuilds a bloated table using logical decoding, with no extension, while it stays readable and writable. In Craig Kerstiens' Snowflake blog test, a 493 MB table shrank to 228 MB in 9.4 seconds, against 2.7 seconds for the locking VACUUM FULL. The timings come from one constrained system, and the claim that the overhead ratio holds on faster hardware is unsourced. Limits include a primary key requirement, no partitioned tables, 2x disk space and incomplete MVCC safety.", "page": "https://rubric.hrih.si/editions/2026-11-04/19/", "card": "https://rubric.hrih.si/og/2026-11-04/19.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6158, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.69, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.493, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5244, "spread": 0.087 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.708, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.7958, "spread": 0.173 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.553, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.5778, "spread": 0.047 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8908, "spread": 0.193 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.06, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.25, "judge_b": 0.34, "mean": 0.33, "spread": 0.15 } } }, { "rank": 20, "id": "st_20261104_12901", "title": "The State of Python Profiling in 2026", "url": "https://p403n1x87.github.io/the-state-of-python-profiling-in-2026.html", "domain": "p403n1x87.github.io", "site": "p403n1x87.github.io", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-11-03T12:57:41.000Z", "jev": 0.7105, "judge_a": 0.5865, "judge_b": 0.6867, "score": 0.6612, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 30, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Austin's maintainer surveys Python profiling as CPython 3.15 adds Tachyon (profiling.sampling) to the standard library, built on 3.14's remote-debugging interface. Tachyon only profiles 3.15 and later, whereas py-spy lacks free-threaded support and Scalene profiles from inside the process. Austin 4.1 adds native-stack mode and asyncio task tracking. The author's own CI benchmark, on Linux x86_64 with Python 3.13, shows repeated-stack detection lifting the maximum sampling rate from 121,000 to 166,000 samples/s; the figure comes from the author and wasn't independently checked, and the gain varies with workload.", "page": "https://rubric.hrih.si/editions/2026-11-04/20/", "card": "https://rubric.hrih.si/og/2026-11-04/20.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.625, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5817, "spread": 0.125 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.79, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.597, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.5922, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.878, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8158, "spread": 0.127 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.907, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7622, "spread": 0.307 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.9058, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9167, "spread": 0.12 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2367, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2233, "spread": 0.12 } } }, { "rank": 21, "id": "st_20261104_12667", "title": "Transformers Model Integration Contracts", "url": "https://huggingface.co/blog/transformers-mic", "domain": "huggingface.co", "site": "Hugging Face Blog", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T00:00:00.000Z", "jev": 0.6915, "judge_a": 0.5393, "judge_b": 0.8324, "score": 0.6877, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 15, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Hugging Face's Transformers team describes Model Integration Contracts (MICs), a proposed standard for packaging small, CPU-friendly test fixtures of real model architectures.", "page": "https://rubric.hrih.si/editions/2026-11-04/21/", "card": "https://rubric.hrih.si/og/2026-11-04/21.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.42, "judge_b": 0.76, "mean": 0.6075, "spread": 0.34 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7633, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.573, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6278, "spread": 0.21 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.775, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7583, "spread": 0.3 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.72, "judge_b": 0.93, "mean": 0.8678, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8942, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9133, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1133, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.25, "judge_b": 0.06, "mean": 0.1567, "spread": 0.19 } } }, { "rank": 22, "id": "st_20261104_12537", "title": "Never gonna print you up", "url": "https://dixken.de/blog/never-gonna-print-you-up", "domain": "dixken.de", "site": "dixken.de", "categories": [ "security", "hardware" ], "category_names": [ "Security", "Hardware & devices" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T02:02:59.000Z", "jev": 0.6531, "judge_a": 0.6305, "judge_b": 0.7642, "score": 0.6825, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 16, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "A security researcher spent a week fuzzing and reverse engineering a Prusa MK4S 3D printer and reported 14 findings, including a remote code execution chain through the Prusa Connect command path (PFB-008, CVSS 8.1).", "page": "https://rubric.hrih.si/editions/2026-11-04/22/", "card": "https://rubric.hrih.si/og/2026-11-04/22.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.573, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6342, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8133, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.5, "judge_b": 0.5, "mean": 0.4822, "spread": 0.053 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.745, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6883, "spread": 0.145 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.967, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8822, "spread": 0.167 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8967, "spread": 0.18 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9133, "spread": 0.12 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1967, "spread": 0.15 } } }, { "rank": 23, "id": "st_20261104_13590", "title": "Formalizing Nvidia PTX in Lean to Prove GPU Kernel Correctness", "url": "https://sf-tensor.com/engineering/formalizing-ptx", "domain": "sf-tensor.com", "site": "sf-tensor.com", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-11-03T20:04:08.000Z", "jev": 0.5735, "judge_a": 0.573, "judge_b": 0.8021, "score": 0.6495, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 33, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "The paper formalizes Nvidia's PTX GPU memory model in the Lean proof assistant so that an LLM can write proofs that specific GPU kernels compute correct results, with Lean's small trusted checker verifying each proof.", "page": "https://rubric.hrih.si/editions/2026-11-04/23/", "card": "https://rubric.hrih.si/og/2026-11-04/23.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.578, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.6625, "spread": 0.31 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8067, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.49, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5233, "spread": 0.09 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.523, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.5842, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.663, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.7578, "spread": 0.247 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9233, "spread": 0.09 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9167, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1267, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.25, "judge_b": 0.14, "mean": 0.1967, "spread": 0.11 } } }, { "rank": 24, "id": "st_20261104_13325", "title": "USDB: Scaling Storage for Next-Generation Recommender Systems at ByteDance", "url": "https://arxiv.org/abs/2611.02618", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai", "devops", "data" ], "category_names": [ "AI & machine learning", "Infrastructure & DevOps", "Data" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T16:40:02.000Z", "jev": 0.6112, "judge_a": 0.4695, "judge_b": 0.7476, "score": 0.6094, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 62, "topic_penalty": 0, "repeat_penalty": 0, "summary": "USDB is ByteDance's unified storage backend for user behavior sequences that serve both streaming recommendation inference and batch training. Its CascadeLSM time-tiered design, shared replicas, partial dumps, and hybrid columnar format reuse maintenance work across workloads.", "page": "https://rubric.hrih.si/editions/2026-11-04/24/", "card": "https://rubric.hrih.si/og/2026-11-04/24.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6275, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.68, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.437, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4556, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.673, "judge_a": 0.5, "judge_b": 0.75, "mean": 0.6408, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.79, "spread": 0.31 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.945, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.9117, "spread": 0.095 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9167, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.0967, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.28, "spread": 0.18 } } }, { "rank": 25, "id": "st_20261104_12887", "title": "Fastest GitHub Actions Runners", "url": "https://runs-on.com/benchmarks/runners/", "domain": "runs-on.com", "site": "RunsOn", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-11-03T12:04:37.000Z", "jev": 0.6593, "judge_a": 0.5095, "judge_b": 0.7167, "score": 0.6285, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 45, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "A benchmark of 57 GitHub Actions runner configurations across nine providers, timing three builds and pricing each run. Namespace's 8-vCPU x86 runner finished all three builds in 5m 04s for $0.1444, while RunsOn's 8-vCPU m8a.2xlarge with tmpfs was nearly as fast at $0.0206.", "page": "https://rubric.hrih.si/editions/2026-11-04/25/", "card": "https://rubric.hrih.si/og/2026-11-04/25.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.465, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.69, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.59, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.57, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.928, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8525, "spread": 0.147 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.937, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8456, "spread": 0.237 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.87, "judge_a": 0.65, "judge_b": 0.7, "mean": 0.74, "spread": 0.22 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.8833, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.25, "judge_b": 0.04, "mean": 0.1233, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.22, "spread": 0.22 } } }, { "rank": 26, "id": "st_20261104_12508", "title": "Our organizations are being DDoSed from the inside", "url": "https://karrisaarinen.com/posts/our-organizations-are-being-ddosed-from-the-inside/", "domain": "karrisaarinen.com", "site": "Karri Saarinen", "categories": [ "ai", "product", "startups" ], "category_names": [ "AI & machine learning", "Product", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T01:39:53.000Z", "jev": 0.6246, "judge_a": 0.5325, "judge_b": 0.6402, "score": 0.599, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 75, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A CEO argues that AI-accelerated work is outpacing leadership's capacity to understand, review, and steer it, so organizations are being overwhelmed from within even though everyone is acting in good faith.", "page": "https://rubric.hrih.si/editions/2026-11-04/26/", "card": "https://rubric.hrih.si/og/2026-11-04/26.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.603, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5408, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8, "spread": 0.04 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.943, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7544, "spread": 0.343 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.718, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6658, "spread": 0.118 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.07, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.25, "spread": 0.31 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.34, "judge_a": 0.1, "judge_b": 0.24, "mean": 0.2267, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.82, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7333, "spread": 0.22 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0667, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2833, "spread": 0.29 } } }, { "rank": 27, "id": "st_20261104_13291", "title": "UEFI bootkit infected fully patched Windows machines. Is Microsoft to blame?", "url": "https://arstechnica.com/security/2026/11/uefi-bootkit-infected-fully-patched-windows-machines-is-microsoft-to-blame/", "domain": "arstechnica.com", "site": "Ars Technica: IT", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T16:03:13.000Z", "jev": 0.601, "judge_a": 0.6593, "judge_b": 0.7806, "score": 0.6803, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 17, "topic_penalty": 0.08, "repeat_penalty": 0.0029, "summary": "Ars Technica's Dan Goodin reports that the UEFI bootkit BlackX, documented by Kaspersky, infected fully patched Windows machines by exploiting the \"Baton Drop\" Secure Boot bypass (CVE-2022-21894).", "page": "https://rubric.hrih.si/editions/2026-11-04/27/", "card": "https://rubric.hrih.si/og/2026-11-04/27.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.71, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6967, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8167, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.703, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6411, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.565, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.6883, "spread": 0.235 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.49, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.6867, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7233, "spread": 0.05 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.35, "judge_b": 0.95, "mean": 0.7333, "spread": 0.6 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0333, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.57, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.3167, "spread": 0.39 } } }, { "rank": 28, "id": "st_20261104_12671", "title": "Future Maintenance of Mbed TLS", "url": "https://lists.trustedfirmware.org/archives/list/mbed-tls@lists.trustedfirmware.org/thread/YPIGCB7WN6GCHOGEGKOJXUYAP7OK527W/", "domain": "lists.trustedfirmware.org", "site": "lists.trustedfirmware.org", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T07:39:05.000Z", "jev": 0.6061, "judge_a": 0.6979, "judge_b": 0.7073, "score": 0.6705, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 21, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The Mbed TLS team has announced that its existing maintainers will stop working on the TLS and X.509 libraries from April 2027, while continuing to maintain TF-PSA-Crypto. Unless new maintainers ramp up by then, security fixes, incident response, and releases for those libraries will end.", "page": "https://rubric.hrih.si/editions/2026-11-04/28/", "card": "https://rubric.hrih.si/og/2026-11-04/28.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.695, "judge_a": 0.62, "judge_b": 0.72, "mean": 0.6783, "spread": 0.1 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.78, "judge_b": 0.82, "mean": 0.7533, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.543, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6111, "spread": 0.127 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.72, "judge_a": 0.82, "judge_b": 0.68, "mean": 0.74, "spread": 0.14 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.303, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.6678, "spread": 0.597 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.315, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.215, "spread": 0.165 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.91, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.03, "judge_b": 0.04, "mean": 0.0633, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0733, "spread": 0.11 } } }, { "rank": 29, "id": "st_20261104_13799", "title": "Things I believed about coding agents", "url": "https://alonbochman.substack.com/p/seven-things-i-believed-about-coding", "domain": "alonbochman.substack.com", "site": "alonbochman.substack.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T23:38:35.000Z", "jev": 0.7752, "judge_a": 0.5007, "judge_b": 0.7358, "score": 0.6705, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 22, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author, who runs ConductorScore, analyzed transcripts from 190 engineers (51,000 sessions) to test seven beliefs about what separates high-return coding-agent users from low-return ones.", "page": "https://rubric.hrih.si/editions/2026-11-04/29/", "card": "https://rubric.hrih.si/og/2026-11-04/29.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5842, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.72, "judge_b": 0.9, "mean": 0.81, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.823, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6978, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.938, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.8125, "spread": 0.238 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.94, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7733, "spread": 0.34 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.92, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7467, "spread": 0.32 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.8233, "spread": 0.38 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.14, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.3667, "spread": 0.03 } } }, { "rank": 30, "id": "st_20261104_12583", "title": "1.26M Data Center Satellites? Orbit Says No", "url": "https://blog.budget-engineer.com/space-data-centers-have-you-looked-up-lately", "domain": "blog.budget-engineer.com", "site": "Budget Engineer Blog", "categories": [ "policy" ], "category_names": [ "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T04:05:14.000Z", "jev": 0.5463, "judge_a": 0.586, "judge_b": 0.6383, "score": 0.5902, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 86, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A Nov 2026 piece by @Budget Engineer argues that low Earth orbit is already dangerously crowded, with roughly 16,000 to 17,000 working satellites and 35,000 to 47,000 tracked objects depending on the counter.", "page": "https://rubric.hrih.si/editions/2026-11-04/30/", "card": "https://rubric.hrih.si/og/2026-11-04/30.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.55, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6233, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.82, "judge_b": 0.88, "mean": 0.79, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.737, "judge_a": 0.6, "judge_b": 0.63, "mean": 0.6556, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.32, "judge_a": 0.35, "judge_b": 0.52, "mean": 0.3967, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7867, "spread": 0.02 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.778, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6658, "spread": 0.178 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9267, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.0433, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.4367, "spread": 0.38 } } }, { "rank": 31, "id": "st_20261104_13796", "title": "What a Compromised VS Code Extension Could Do Before You Write a Line of Code", "url": "https://octri.dev/blog/compromised-vs-code-extension", "domain": "octri.dev", "site": "octri.dev", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T23:35:19.000Z", "jev": 0.7493, "judge_a": 0.5546, "judge_b": 0.7019, "score": 0.6685, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 23, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A detailed investigation of 40 VS Code launches measured what popular extensions do before a developer touches anything. Prettier added no identifiable background process, while GitLens activated at startup and launched gk helper processes that made outbound network connections. The author stresses that neither extension showed signs of compromise.", "page": "https://rubric.hrih.si/editions/2026-11-04/31/", "card": "https://rubric.hrih.si/og/2026-11-04/31.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5442, "spread": 0.252 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.78, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.8, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6967, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.853, "judge_a": 0.7, "judge_b": 0.74, "mean": 0.7642, "spread": 0.153 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.887, "judge_a": 0.55, "judge_b": 0.73, "mean": 0.7222, "spread": 0.337 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.95, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.7433, "spread": 0.35 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.8, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7533, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.02, "mean": 0.06, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.21, "spread": 0.22 } } }, { "rank": 32, "id": "st_20261104_13002", "title": "Statistics Are Aggregates", "url": "https://spiraldb.com/blog/statistics-are-aggregates", "domain": "spiraldb.com", "site": "Spiral", "categories": [ "data" ], "category_names": [ "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:00:08.000Z", "jev": 0.5905, "judge_a": 0.501, "judge_b": 0.732, "score": 0.6078, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 63, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Vortex's writeup argues that columnar file statistics such as min, max, sums, and counts are aggregates in their own right, so engines should merge stored partial results across zones, files, and tables instead of only using them to skip data.", "page": "https://rubric.hrih.si/editions/2026-11-04/32/", "card": "https://rubric.hrih.si/og/2026-11-04/32.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.56, "judge_a": 0.3, "judge_b": 0.7, "mean": 0.52, "spread": 0.4 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7267, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4833, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.645, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.655, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.557, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6589, "spread": 0.263 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.925, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8917, "spread": 0.075 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9167, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1133, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1333, "spread": 0.09 } } }, { "rank": 33, "id": "st_20261104_12585", "title": "Is hosting an open-source inference engine profitable?", "url": "https://userecurrence.ai/research/is-hosting-open-source-inference-profitable", "domain": "userecurrence.ai", "site": "userecurrence.ai", "categories": [ "ai", "devops", "hardware", "startups" ], "category_names": [ "AI & machine learning", "Infrastructure & DevOps", "Hardware & devices", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T04:27:10.000Z", "jev": 0.6553, "judge_a": 0.588, "judge_b": 0.7546, "score": 0.6659, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 25, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A cost-and-margin analysis of hosting DeepSeek V4.1 Flash on H200, B300 and GB300 NVL72 GPUs concludes that inference can be profitable, but mainly on Blackwell hardware.", "page": "https://rubric.hrih.si/editions/2026-11-04/33/", "card": "https://rubric.hrih.si/og/2026-11-04/33.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.598, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5725, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8167, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.487, "judge_a": 0.5, "judge_b": 0.63, "mean": 0.5389, "spread": 0.143 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.705, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.7883, "spread": 0.155 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.833, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7211, "spread": 0.283 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8817, "spread": 0.185 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.87, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.0867, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.35, "judge_b": 0.34, "mean": 0.3133, "spread": 0.1 } } }, { "rank": 34, "id": "st_20261104_12973", "title": "I'm a product engineer now. Mostly I wait", "url": "https://ollyconn.com/2026/11/03/product-engineer-mostly-i-wait/", "domain": "ollyconn.com", "site": "ollyconn", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T13:09:56.000Z", "jev": 0.7498, "judge_a": 0.5397, "judge_b": 0.706, "score": 0.6651, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 26, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A product engineer's three-month delivery data, shared by a writer who merged over 500 PRs with agent-assisted tools, shows coding now takes a median of 20 minutes per PR, while the real delays sit elsewhere: ticket-to-first-commit at 15.5 days at the 80th percentile, 28 hours median wait for human review…", "page": "https://rubric.hrih.si/editions/2026-11-04/34/", "card": "https://rubric.hrih.si/og/2026-11-04/34.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.5125, "spread": 0.358 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.79, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.87, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.7133, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.828, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7692, "spread": 0.128 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.7, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.755, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.595, "spread": 0.305 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.79, "spread": 0.3 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1933, "spread": 0.02 } } }, { "rank": 35, "id": "st_20261104_13492", "title": "Decision Models as Policy Gates for Coding Agents", "url": "https://arxiv.org/abs/2611.02155", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T18:39:12.000Z", "jev": 0.7221, "judge_a": 0.4845, "judge_b": 0.7804, "score": 0.6623, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 28, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A paper evaluates 22 hosted decision models, plus two prompted general models, as classifiers for a multi-rule policy gate on coding-agent tool calls, using a synthetic dataset of 2,643 labeled calls and a sealed holdout.", "page": "https://rubric.hrih.si/editions/2026-11-04/35/", "card": "https://rubric.hrih.si/og/2026-11-04/35.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.4, "judge_b": 0.7, "mean": 0.585, "spread": 0.3 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7533, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.653, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5911, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.878, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7658, "spread": 0.277 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8467, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8625, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.88, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.11, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.38, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.2533, "spread": 0.3 } } }, { "rank": 36, "id": "st_20261104_13505", "title": "Av1gpu – AVIF encoder in CUDA, 9× faster than libaom at the same size", "url": "https://github.com/maddog107/av1gpu", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T18:43:42.000Z", "jev": 0.6513, "judge_a": 0.504, "judge_b": 0.7713, "score": 0.6422, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 36, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "av1gpu is an open-source CUDA encoder that writes AVIF still images on NVIDIA GPUs, running mode search, entropy coding and related steps on CUDA cores rather than NVENC.", "page": "https://rubric.hrih.si/editions/2026-11-04/36/", "card": "https://rubric.hrih.si/og/2026-11-04/36.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.585, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.7867, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.43, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.45, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.845, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.755, "spread": 0.245 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.99, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.9233, "spread": 0.19 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9458, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.17, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.4, "judge_b": 0.12, "mean": 0.3167, "spread": 0.31 } } }, { "rank": 37, "id": "st_20261104_13646", "title": "XLA from the Trenches: Correctness on TPU Workloads at Scale", "url": "https://arxiv.org/abs/2611.00624", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T21:06:22.000Z", "jev": 0.653, "judge_a": 0.532, "judge_b": 0.7999, "score": 0.6616, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 29, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The paper \"XLA from the Trenches\" from Google describes a methodology for finding and fixing correctness bugs in XLA:TPU, the compiler for Google's Tensor Processing Units.", "page": "https://rubric.hrih.si/editions/2026-11-04/37/", "card": "https://rubric.hrih.si/og/2026-11-04/37.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.698, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.6725, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.7733, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.537, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5022, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.668, "judge_a": 0.55, "judge_b": 0.83, "mean": 0.6825, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.873, "judge_a": 0.7, "judge_b": 0.79, "mean": 0.7878, "spread": 0.173 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9533, "spread": 0.08 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1033, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.2767, "spread": 0.09 } } }, { "rank": 38, "id": "st_20261104_12611", "title": "Architect creates open-source replacement for VM migration tool VMware has hidden", "url": "https://www.theregister.com/virtualization/2026/11/03/architect-creates-open-source-replacement-for-vm-migration-tool-vmware-has-hidden/5307124", "domain": "theregister.com", "site": "The Register", "categories": [ "devops", "it" ], "category_names": [ "Infrastructure & DevOps", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T04:45:59.000Z", "jev": 0.5687, "judge_a": 0.5655, "judge_b": 0.7276, "score": 0.6206, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 54, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Cloud architect Andrija Panić has released OpenVDDK, an open-source, independently written replacement for VMware's Virtual Disk Development Kit library, which VMware restricted to partners in August. Because tools such as virt-v2v, nbdkit, Proxmox VE and Apache CloudStack depend on that library to read VM disks, its restriction hampered migrations.", "page": "https://rubric.hrih.si/editions/2026-11-04/38/", "card": "https://rubric.hrih.si/og/2026-11-04/38.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.555, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7467, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.443, "judge_a": 0.5, "judge_b": 0.6, "mean": 0.5144, "spread": 0.157 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.678, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.7258, "spread": 0.123 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.463, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5611, "spread": 0.157 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.723, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6475, "spread": 0.123 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.88, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0733, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.2167, "spread": 0.21 } } }, { "rank": 39, "id": "st_20261104_13059", "title": "eMudhra: Free Certificates for Everyone", "url": "https://www.feistyduck.com/newsletter/issue_142_emudhra_free_certificates_for_everyone", "domain": "feistyduck.com", "site": "feistyduck.com", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T14:08:01.000Z", "jev": 0.6578, "judge_a": 0.7505, "judge_b": 0.6562, "score": 0.6881, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 14, "topic_penalty": 0.08, "repeat_penalty": 0.0286, "summary": "In October 2026, security researcher Wayne used Google's open-source paranoid_crypto tool to find that eMudhra, a public certificate authority, had issued eight intermediate certificates with trivially weak private keys, each chosen from only 256 possibilities.", "page": "https://rubric.hrih.si/editions/2026-11-04/39/", "card": "https://rubric.hrih.si/og/2026-11-04/39.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.708, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7625, "spread": 0.093 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8067, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.817, "judge_a": 0.8, "judge_b": 0.62, "mean": 0.7456, "spread": 0.197 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.823, "judge_a": 0.75, "judge_b": 0.72, "mean": 0.7642, "spread": 0.103 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.467, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.6822, "spread": 0.333 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.79, "judge_a": 0.65, "judge_b": 0.67, "mean": 0.7033, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.08, "mean": 0.5867, "spread": 0.77 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.1067, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.63, "judge_a": 0.25, "judge_b": 0.43, "mean": 0.4367, "spread": 0.38 } } }, { "rank": 40, "id": "st_20261104_13190", "title": "The Incident Report Is Becoming a Building Block of AI Governance", "url": "https://techpolicy.press/the-incident-report-is-becoming-a-building-block-of-ai-governance", "domain": "techpolicy.press", "site": "Tech Policy Press", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T14:57:34.000Z", "jev": 0.671, "judge_a": 0.553, "judge_b": 0.7404, "score": 0.6547, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 32, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article argues that incident reporting is becoming a core tool of AI governance, drawing on recent U.S. government moves after Anthropic disclosed that a test model submitted incomplete visa applications to a live State Department website.", "page": "https://rubric.hrih.si/editions/2026-11-04/40/", "card": "https://rubric.hrih.si/og/2026-11-04/40.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.74, "judge_a": 0.55, "judge_b": 0.75, "mean": 0.68, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.78, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.817, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7122, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.613, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5975, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.447, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.5889, "spread": 0.273 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.488, "judge_a": 0.25, "judge_b": 0.4, "mean": 0.3792, "spread": 0.238 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.8667, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.04, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1767, "spread": 0.09 } } }, { "rank": 41, "id": "st_20261104_13538", "title": "Panthera: Ampere's CPU Performance Model", "url": "https://github.com/AmpereComputing/panthera", "domain": "github.com", "site": "GitHub", "categories": [ "hardware" ], "category_names": [ "Hardware & devices" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T19:12:17.000Z", "jev": 0.4912, "judge_a": 0.497, "judge_b": 0.7179, "score": 0.5686, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 112, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Ampere has open-sourced Panthera, its cycle-accurate CPU performance model, under a permissive license, after keeping comparable industrial models closely guarded. The release is aimed at giving researchers and educators a commercial-grade alternative to academic simulators, with the ability to check results against AmpereOne silicon available on major clouds.", "page": "https://rubric.hrih.si/editions/2026-11-04/41/", "card": "https://rubric.hrih.si/og/2026-11-04/41.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.648, "judge_a": 0.45, "judge_b": 0.8, "mean": 0.6325, "spread": 0.35 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7067, "spread": 0.38 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.567, "judge_a": 0.4, "judge_b": 0.45, "mean": 0.4722, "spread": 0.167 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.428, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5658, "spread": 0.293 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.57, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6633, "spread": 0.25 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.548, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5892, "spread": 0.073 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1667, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.2767, "spread": 0.05 } } }, { "rank": 42, "id": "st_20261104_12990", "title": "Samsung to make AMD and Nvidia HBM parts at second Texas fab, report says", "url": "https://thenextweb.com/news/samsung-second-texas-fab-amd-nvidia-hbm", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T13:02:37.000Z", "jev": 0.5028, "judge_a": 0.507, "judge_b": 0.6936, "score": 0.5678, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 113, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Maeil Business Newspaper reports that Samsung will use its second Taylor, Texas plant to make base dies for high-bandwidth memory, mostly for AMD and possibly Nvidia, with output reportedly fully booked. Samsung declined comment and Reuters could not confirm the report.", "page": "https://rubric.hrih.si/editions/2026-11-04/42/", "card": "https://rubric.hrih.si/og/2026-11-04/42.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.648, "judge_a": 0.55, "judge_b": 0.74, "mean": 0.6458, "spread": 0.19 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.7, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.653, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6511, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.34, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4367, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.347, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3922, "spread": 0.133 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.463, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.3942, "spread": 0.163 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.8967, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0733, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.25, "judge_b": 0.06, "mean": 0.1833, "spread": 0.19 } } }, { "rank": 43, "id": "st_20261104_13627", "title": "Introducing Adaptive Dark Mode", "url": "https://webkit.org/blog/18541/introducing-adaptive-dark-mode/", "domain": "webkit.org", "site": "WebKit Blog", "categories": [ "programming", "design" ], "category_names": [ "Programming & dev tools", "Design & UX" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T18:00:35.000Z", "jev": 0.6475, "judge_a": 0.505, "judge_b": 0.5402, "score": 0.5642, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 118, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Apple has added an Adaptive Dark Mode to Safari 27.3 beta that, when a user turns it on with the system set to dark, automatically renders light-background websites in dark colors.", "page": "https://rubric.hrih.si/editions/2026-11-04/43/", "card": "https://rubric.hrih.si/og/2026-11-04/43.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.4, "judge_b": 0.45, "mean": 0.5108, "spread": 0.283 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.48, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.71, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.63, "spread": 0.13 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.58, "mean": 0.7267, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.573, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5978, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.783, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6675, "spread": 0.183 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9267, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.24, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1833, "spread": 0.03 } } }, { "rank": 44, "id": "st_20261104_13308", "title": "Gitbook Breached", "url": "https://news.ycombinator.com/item?id=50354336", "domain": "news.ycombinator.com", "site": "Hacker News", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T16:45:49.000Z", "jev": 0.5905, "judge_a": 0.6041, "judge_b": 0.7349, "score": 0.6431, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 34, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "GitBook disclosed a security incident in which an attacker, using AI agents, gained unauthorized access to some infrastructure credentials on November 2 and may have accessed Git Sync secrets, API tokens, third-party integration tokens, and visitor questions sent to GitBook Assistant.", "page": "https://rubric.hrih.si/editions/2026-11-04/44/", "card": "https://rubric.hrih.si/og/2026-11-04/44.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.595, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.72, "judge_b": 0.9, "mean": 0.8033, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.317, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.5389, "spread": 0.383 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.813, "judge_a": 0.78, "judge_b": 0.82, "mean": 0.8042, "spread": 0.04 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.55, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.4933, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.54, "judge_a": 0.2, "judge_b": 0.3, "mean": 0.3467, "spread": 0.34 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.78, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.87, "spread": 0.2 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.05, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.48, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2867, "spread": 0.3 } } }, { "rank": 45, "id": "st_20261104_13390", "title": "Revolut Cuts A.I. Coding Costs 90% with a Chinese Open-Weight Model", "url": "https://www.trendingtopics.eu/revolut-glm-open-weight-coding-costs/", "domain": "trendingtopics.eu", "site": "Trending Topics", "categories": [ "programming", "ai", "devops", "startups", "fintech" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Startups & founders", "Fintech" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T17:03:36.000Z", "jev": 0.55, "judge_a": 0.6236, "judge_b": 0.7484, "score": 0.6407, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 37, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Revolut reports that moving about 2,000 engineers from Anthropic's and OpenAI's coding agents to a self-hosted open-weight model from Chinese lab Z.ai, GLM, has cut its AI bill to roughly a tenth of its previous cost.", "page": "https://rubric.hrih.si/editions/2026-11-04/45/", "card": "https://rubric.hrih.si/og/2026-11-04/45.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.68, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.67, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8267, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.393, "judge_a": 0.67, "judge_b": 0.66, "mean": 0.5744, "spread": 0.277 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.7333, "spread": 0.16 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.497, "judge_a": 0.45, "judge_b": 0.67, "mean": 0.5389, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.723, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.6142, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9033, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1033, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.73, "judge_a": 0.4, "judge_b": 0.35, "mean": 0.4933, "spread": 0.38 } } }, { "rank": 46, "id": "st_20261104_13435", "title": "Nvidia tells data center operators to limit business to Chinese customers", "url": "https://www.tomshardware.com/tech-industry/data-centers/nvidia-tells-data-center-operators-to-limit-business-from-chinese-customers-with-new-global-restrictions-report-claims-allegedly-restricted-to-49-percent-of-the-total-ensuring-51-percent-are-non-chinese-outfits", "domain": "tomshardware.com", "site": "Tom's Hardware", "categories": [ "policy" ], "category_names": [ "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T17:31:28.000Z", "jev": 0.4522, "judge_a": 0.5368, "judge_b": 0.6908, "score": 0.5598, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 122, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Nvidia has reportedly told data center customers to cap the share of their business coming from Chinese companies at 49%, with at least 51% from non-Chinese clients, according to the Financial Times. The policy was reportedly conveyed through Nvidia's sales and regional teams rather than formal documentation.", "page": "https://rubric.hrih.si/editions/2026-11-04/46/", "card": "https://rubric.hrih.si/og/2026-11-04/46.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.678, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6692, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.53, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.6967, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.507, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6089, "spread": 0.213 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.428, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5158, "spread": 0.193 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.287, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3722, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.293, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1575, "spread": 0.212 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.86, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.06, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.51, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2967, "spread": 0.33 } } }, { "rank": 47, "id": "st_20261104_12602", "title": "Test and verification technique effectiveness with GPT-6 Astra", "url": "https://danluu.com/astra-testing/", "domain": "danluu.com", "site": "danluu.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T05:10:40.000Z", "jev": 0.5849, "judge_a": 0.551, "judge_b": 0.7736, "score": 0.6365, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 38, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Dan Luu's post reports a follow-up evaluation of how agents use testing and verification techniques, now with GPT-6 Astra. The Zstd implementation benchmark has saturated, with Astra reaching near-full holdout correctness at about $1 per run on medium effort, so the author moved to harder tasks: the IMAP RFC and…", "page": "https://rubric.hrih.si/editions/2026-11-04/47/", "card": "https://rubric.hrih.si/og/2026-11-04/47.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.4, "judge_b": 0.7, "mean": 0.5392, "spread": 0.3 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.7, "judge_b": 0.95, "mean": 0.73, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.723, "judge_a": 0.5, "judge_b": 0.65, "mean": 0.6244, "spread": 0.223 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.66, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.713, "judge_a": 0.65, "judge_b": 0.78, "mean": 0.7144, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.828, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7825, "spread": 0.128 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8867, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0467, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1767, "spread": 0.09 } } }, { "rank": 48, "id": "st_20261104_13208", "title": "Found a backdoor on my NixOS laptop disguised as a \"Nix-update\" service?", "url": "https://discourse.nixos.org/t/found-a-backdoor-on-my-nixos-laptop-disguised-as-a-nix-update-service/80793", "domain": "discourse.nixos.org", "site": "NixOS Discourse", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "Other", "depth": "three_judges", "published_at": "2026-11-03T15:52:24.000Z", "jev": 0.641, "judge_a": 0.5505, "judge_b": 0.7161, "score": 0.6359, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 39, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A NixOS user reports finding a 12 MB Go binary and systemd user units in their home directory, which reverse-engineering suggests is a customized ligolo-ng build with obfuscation and a C2 domain, installed silently on October 22. ClamAV did not flag it.", "page": "https://rubric.hrih.si/editions/2026-11-04/48/", "card": "https://rubric.hrih.si/og/2026-11-04/48.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.525, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5583, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8267, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.627, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.5256, "spread": 0.227 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.755, "judge_a": 0.65, "judge_b": 0.8, "mean": 0.735, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.823, "judge_a": 0.35, "judge_b": 0.65, "mean": 0.6078, "spread": 0.473 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.963, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7608, "spread": 0.363 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.82, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.06, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.44, "judge_a": 0.35, "judge_b": 0.35, "mean": 0.38, "spread": 0.09 } } }, { "rank": 49, "id": "st_20261104_13341", "title": "F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale", "url": "https://engineering.fb.com/2026/11/03/ml-applications/f3-lightning-how-meta-accelerates-ads-ml-features-to-production/", "domain": "engineering.fb.com", "site": "Engineering at Meta", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T17:00:35.000Z", "jev": 0.6212, "judge_a": 0.5802, "judge_b": 0.7016, "score": 0.6343, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 40, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Meta describes F3 Lightning, an infrastructure layer on its Facebook Feature Framework that serves ads ranking features, which can exceed 20,000 per request. It decouples feature releases from a single shared merged graph, so each feature is compiled, validated, and rolled back independently.", "page": "https://rubric.hrih.si/editions/2026-11-04/49/", "card": "https://rubric.hrih.si/og/2026-11-04/49.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.61, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.61, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.72, "judge_b": 0.84, "mean": 0.75, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.43, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.4533, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.705, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6583, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.603, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.6944, "spread": 0.177 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.925, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8783, "spread": 0.075 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0833, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.18, "spread": 0.1 } } }, { "rank": 50, "id": "st_20261104_13267", "title": "What my nothing phone does in the background", "url": "https://www.animesh.us/blog/nothing-os-background?embed=1", "domain": "animesh.us", "site": "Animesh Chaudhri Portfolio", "categories": [ "other" ], "category_names": [ "Other" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T16:09:07.000Z", "jev": 0.5865, "judge_a": 0.5027, "judge_b": 0.5734, "score": 0.5541, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 131, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A developer connected a Nothing Phone (2a) Plus to a laptop over ADB without root and used Android's own network accounting, scheduler logs and code strings to map its background activity.", "page": "https://rubric.hrih.si/editions/2026-11-04/50/", "card": "https://rubric.hrih.si/og/2026-11-04/50.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.458, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.3925, "spread": 0.158 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.72, "judge_b": 0.8, "mean": 0.7167, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.52, "judge_a": 0.45, "judge_b": 0.35, "mean": 0.44, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.613, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.5808, "spread": 0.063 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.8278, "spread": 0.263 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.7, "judge_b": 0.68, "mean": 0.7883, "spread": 0.305 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.79, "spread": 0.29 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2133, "spread": 0.07 } } }, { "rank": 51, "id": "st_20261104_13318", "title": "Not All Agent Sandboxes Are Created Equally", "url": "https://kagent.dev/blog/agent-sandboxes-four-axes", "domain": "kagent.dev", "site": "kagent.dev", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T16:52:21.000Z", "jev": 0.6734, "judge_a": 0.4935, "judge_b": 0.7334, "score": 0.6334, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 41, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This piece compares sandboxes for running AI agents, including Discobox, OpenShell, E2B, Agent Substrate, and Docker Sandboxes, across four axes: boundary and access controls, environment, lifecycle, and interface.", "page": "https://rubric.hrih.si/editions/2026-11-04/51/", "card": "https://rubric.hrih.si/og/2026-11-04/51.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5292, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7767, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6533, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.818, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7792, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.3, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.44, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.74, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.6467, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.4, "judge_b": 0.9, "mean": 0.7167, "spread": 0.5 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1367, "spread": 0.17 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1467, "spread": 0.12 } } }, { "rank": 52, "id": "st_20261104_13698", "title": "FreeRDP brings remote desktops to the browser", "url": "https://www.heise.de/en/news/FreeRDP-brings-remote-desktops-to-the-browser-11510604.html", "domain": "heise.de", "site": "heise online English", "categories": [ "programming", "it" ], "category_names": [ "Programming & dev tools", "IT & networking" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T21:35:00.000Z", "jev": 0.5485, "judge_a": 0.4515, "judge_b": 0.6593, "score": 0.553, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 133, "topic_penalty": 0, "repeat_penalty": 0, "summary": "FreeRDP 3.33.0, the open-source Remote Desktop Protocol client library, adds WebAssembly support so its client can run directly in a web browser, using a WebSocket proxy to reach RDP servers.", "page": "https://rubric.hrih.si/editions/2026-11-04/52/", "card": "https://rubric.hrih.si/og/2026-11-04/52.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.568, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4825, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.5967, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.497, "judge_a": 0.3, "judge_b": 0.45, "mean": 0.4156, "spread": 0.197 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.62, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.573, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6644, "spread": 0.247 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.76, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7267, "spread": 0.06 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.06, "mean": 0.0667, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.1033, "spread": 0.13 } } }, { "rank": 53, "id": "st_20261104_12923", "title": "Thinking in Traits", "url": "https://corrode.dev/blog/thinking-in-traits/", "domain": "corrode.dev", "site": "Corrode Rust Consulting", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T12:51:45.000Z", "jev": 0.6483, "judge_a": 0.5235, "judge_b": 0.7259, "score": 0.6325, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 42, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This Rust article argues that composition over inheritance works in practice because traits describe what a type can do rather than what it is. Using Iterator::collect and FromIterator, it shows how a shared item type connects independent producers and collections, and uses Java's SSLSocket half-closing mismatch to illustrate the risks…", "page": "https://rubric.hrih.si/editions/2026-11-04/53/", "card": "https://rubric.hrih.si/og/2026-11-04/53.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.535, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4717, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.72, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.6, "judge_b": 0.48, "mean": 0.5811, "spread": 0.183 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.803, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7408, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.62, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.65, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.95, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.8933, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.2, "judge_b": 0.96, "mean": 0.69, "spread": 0.76 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0333, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1067, "spread": 0.06 } } }, { "rank": 54, "id": "st_20261104_13089", "title": "Why ZFS Is Cool", "url": "https://blog.dfb.sh/blog/zfs-fsync/", "domain": "blog.dfb.sh", "site": "blog.dfb.sh", "categories": [ "devops", "it", "data" ], "category_names": [ "Infrastructure & DevOps", "IT & networking", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:56:09.000Z", "jev": 0.6223, "judge_a": 0.5095, "judge_b": 0.7057, "score": 0.6125, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 58, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "A personal blog post argues that ZFS with sync=disabled suits a homelab better than ext4, because the author only needs write ordering, not fsync durability. On two mirrored HDDs, disk utilization fell from 13% to 2.7% and average write latency from 16 ms to 2 ms.", "page": "https://rubric.hrih.si/editions/2026-11-04/54/", "card": "https://rubric.hrih.si/og/2026-11-04/54.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.485, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4683, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.7267, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.45, "judge_b": 0.42, "mean": 0.4656, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.745, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7083, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.913, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8111, "spread": 0.213 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.9158, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8233, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0333, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.24, "spread": 0.12 } } }, { "rank": 55, "id": "st_20261104_12949", "title": "Can AI ever build itself? A deeper look into autonomous AI research", "url": "https://www.nachoplanas.com/articles/rsi-stacked-s-curves", "domain": "nachoplanas.com", "site": "nachoplanas.com", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T13:30:24.000Z", "jev": 0.5785, "judge_a": 0.5805, "judge_b": 0.7365, "score": 0.6318, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 43, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "In \"Can AI ever build itself?\", essayist Lee has a sharply reported look at the September 2026 calls by frontier-lab leaders to slow AI development. Its central claim is that Dario Amodei's case for pacing rests on recursive self-improvement, while the sources he cites say something much weaker: AI assisting…", "page": "https://rubric.hrih.si/editions/2026-11-04/55/", "card": "https://rubric.hrih.si/og/2026-11-04/55.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.578, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6525, "spread": 0.203 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.79, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.917, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8289, "spread": 0.167 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.473, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.4975, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.777, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7522, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.72, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.68, "spread": 0.12 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7533, "spread": 0.28 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0767, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.57, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.3833, "spread": 0.29 } } }, { "rank": 56, "id": "st_20261104_13396", "title": "CWE-bench v2: More coverage, quality, and transparency", "url": "https://huggingface.co/blog/cwe-bench", "domain": "huggingface.co", "site": "Hugging Face Blog", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T00:00:00.000Z", "jev": 0.6359, "judge_a": 0.451, "judge_b": 0.7952, "score": 0.6274, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 46, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "CWE-bench v2 from Collinear AI releases a public set of 100 tasks testing whether AI agents can find and fix security weaknesses in real open-source repositories without breaking existing functionality. Scoring is fully programmatic and reproducible, and the tasks span 91 CWEs.", "page": "https://rubric.hrih.si/editions/2026-11-04/56/", "card": "https://rubric.hrih.si/og/2026-11-04/56.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.61, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5967, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7267, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.63, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.61, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6967, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.8211, "spread": 0.363 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.803, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7275, "spread": 0.203 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.87, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.4, "judge_b": 0.08, "mean": 0.1967, "spread": 0.32 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.19, "spread": 0.13 } } }, { "rank": 57, "id": "st_20261104_13418", "title": "The 3D Printed Database", "url": "https://www.amplifypartners.com/blog-posts/the-3d-printed-database", "domain": "amplifypartners.com", "site": "amplifypartners.com", "categories": [ "programming", "ai", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Data" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T17:39:42.000Z", "jev": 0.5897, "judge_a": 0.4315, "judge_b": 0.74, "score": 0.587, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 91, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "The essay argues that coding agents could let organizations generate custom database systems tailored to a domain's ontology, rather than accepting general-purpose databases that trade performance for flexibility.", "page": "https://rubric.hrih.si/editions/2026-11-04/57/", "card": "https://rubric.hrih.si/og/2026-11-04/57.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.603, "judge_a": 0.35, "judge_b": 0.7, "mean": 0.5508, "spread": 0.35 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7633, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6578, "spread": 0.253 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.64, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5967, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.403, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.4911, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.79, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.7333, "spread": 0.31 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.5, "judge_b": 0.93, "mean": 0.7767, "spread": 0.43 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0867, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.53, "judge_a": 0.45, "judge_b": 0.22, "mean": 0.4, "spread": 0.31 } } }, { "rank": 58, "id": "st_20261104_13508", "title": "ZHA: Zyphra Hierarchical Attention", "url": "https://www.zyphra.com/our-work/zyphra-hierarchical-attention", "domain": "zyphra.com", "site": "Zyphra", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T18:33:51.000Z", "jev": 0.6674, "judge_a": 0.602, "judge_b": 0.7294, "score": 0.6662, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 24, "topic_penalty": 0.08, "repeat_penalty": 0.0392, "summary": "Zyphra's ZHA (Zyphra Hierarchical Attention) is a sparse attention method that builds a tree of compressed summaries over the key-value cache and routes each query coarse-to-fine through it, selecting only relevant tokens.", "page": "https://rubric.hrih.si/editions/2026-11-04/58/", "card": "https://rubric.hrih.si/og/2026-11-04/58.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.733, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7308, "spread": 0.26 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8267, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6767, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.673, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6975, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.8, "judge_a": 0.55, "judge_b": 0.55, "mean": 0.6333, "spread": 0.25 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.878, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8625, "spread": 0.11 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9033, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.15, "judge_b": 0.3, "mean": 0.18, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.38, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.4667, "spread": 0.24 } } }, { "rank": 59, "id": "st_20261104_13517", "title": "An MCP proxy that sends 97% fewer tool-definition tokens per request", "url": "https://github.com/datolabs-io/mcp-toolsearch-proxy", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T19:25:30.000Z", "jev": 0.654, "judge_a": 0.4985, "judge_b": 0.7243, "score": 0.6255, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 47, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "datolabs-io has released mcp-toolsearch-proxy, an open-source MCP server that fronts a large tool catalog so a model sees five search and call tools instead of every definition.", "page": "https://rubric.hrih.si/editions/2026-11-04/59/", "card": "https://rubric.hrih.si/og/2026-11-04/59.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.543, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5042, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.71, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.51, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4867, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.835, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7717, "spread": 0.135 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7967, "spread": 0.31 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.963, "judge_a": 0.75, "judge_b": 0.9, "mean": 0.8708, "spread": 0.213 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.12, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1833, "spread": 0.19 } } }, { "rank": 60, "id": "st_20261104_12759", "title": "Kandinsky S2V 1.0 – an open family of speech-to-video models", "url": "https://kandinskyavatar.github.io/", "domain": "kandinskyavatar.github.io", "site": "kandinskyavatar.github.io", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T09:31:34.000Z", "jev": 0.6002, "judge_a": 0.5065, "judge_b": 0.7677, "score": 0.6247, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 49, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Kandinsky S2V 1.0 is an open family of speech-to-video models, in 2B and 21B parameter versions, that animates a single reference image using a speech track and text prompt to produce long-form 25 FPS video with lip, expression, and gesture motion matched to the audio.", "page": "https://rubric.hrih.si/editions/2026-11-04/60/", "card": "https://rubric.hrih.si/og/2026-11-04/60.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6575, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.6, "judge_b": 0.91, "mean": 0.6933, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.57, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.58, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.623, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6308, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.837, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7522, "spread": 0.237 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.82, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.8167, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9433, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1233, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.27, "spread": 0.15 } } }, { "rank": 61, "id": "st_20261104_13638", "title": "Verifying Apple Reference Images", "url": "https://pps-lab.com/blog/verifying-apple-reference-image/", "domain": "pps-lab.com", "site": "Privacy Preserving Systems Lab", "categories": [ "security", "hardware" ], "category_names": [ "Security", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T21:24:38.000Z", "jev": 0.6148, "judge_a": 0.5135, "judge_b": 0.7459, "score": 0.6247, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 50, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Apple's new Apple Reference Image standard signs pixel data inside the iPhone 18 Pro camera sensor and certifies it via Apple's Private Cloud Compute, aiming to prove a photo's authenticity rather than just its realism.", "page": "https://rubric.hrih.si/editions/2026-11-04/61/", "card": "https://rubric.hrih.si/og/2026-11-04/61.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.54, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5633, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7567, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.52, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.658, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5925, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.87, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.8367, "spread": 0.07 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.9258, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.9, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.0667, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2333, "spread": 0.12 } } }, { "rank": 62, "id": "st_20261104_13585", "title": "Agent status in Paneflow: from screen rules to OSC 7501", "url": "https://paneflow.dev/blog/agent-status-osc-7501", "domain": "paneflow.dev", "site": "Paneflow", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T20:20:39.000Z", "jev": 0.6442, "judge_a": 0.5095, "judge_b": 0.7168, "score": 0.6235, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 51, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Paneflow 0.19.0 now takes each agent's turn state solely from a status the agent writes into its own terminal via the OSC 7501 Program Status Protocol, published by Mitchell Hashimoto in October 2026. Claude Code (v2.1.295 and later) and Codex support it; agents that declare nothing show no state.", "page": "https://rubric.hrih.si/editions/2026-11-04/62/", "card": "https://rubric.hrih.si/og/2026-11-04/62.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.535, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5183, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7467, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.403, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.4611, "spread": 0.097 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.775, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6983, "spread": 0.175 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.977, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8589, "spread": 0.277 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.8, "judge_b": 0.92, "mean": 0.9058, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.89, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.25, "judge_b": 0.04, "mean": 0.1133, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.18, "spread": 0.22 } } }, { "rank": 63, "id": "st_20261104_13037", "title": "The state of MCP auth, November 2026: I scanned 25 public servers", "url": "https://patchbay-mcp.dev/blog/state-of-mcp-auth-nov-2026", "domain": "patchbay-mcp.dev", "site": "patchbay-mcp.dev", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:20:11.000Z", "jev": 0.6933, "judge_a": 0.4982, "judge_b": 0.6769, "score": 0.6228, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 52, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer built patchbay audit, a free CLI that probes MCP (Model Context Protocol) servers for OAuth 2.1, PKCE, and resource-indicator support, then scanned 25 public endpoints on 2026-11-02. Of 20 reachable servers, 8 were public without auth, and 2 of those exposed mutating tools.", "page": "https://rubric.hrih.si/editions/2026-11-04/63/", "card": "https://rubric.hrih.si/og/2026-11-04/63.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5117, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.72, "judge_b": 0.88, "mean": 0.7833, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.5, "judge_b": 0.45, "mean": 0.4656, "spread": 0.053 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.948, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8225, "spread": 0.248 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7867, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.965, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.815, "spread": 0.265 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.86, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.4, "judge_b": 0.12, "mean": 0.1933, "spread": 0.34 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.2867, "spread": 0.2 } } }, { "rank": 64, "id": "st_20261104_12878", "title": "Safehttp – An SSRF-resistant HTTP client for Go", "url": "https://ayuhito.com/projects/safehttp/", "domain": "ayuhito.com", "site": "ayuhito.com", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T12:21:33.000Z", "jev": 0.6299, "judge_a": 0.511, "judge_b": 0.7228, "score": 0.6212, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 53, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Safehttp, an open-source Go HTTP client by Ayuhito, is a drop-in replacement for net/http that adds SSRF protection by default. It blocks private, loopback, and special-purpose destinations, re-checks every redirect and the address of each new connection against DNS rebinding, disables proxies, and rejects mismatched Host header overrides.", "page": "https://rubric.hrih.si/editions/2026-11-04/64/", "card": "https://rubric.hrih.si/og/2026-11-04/64.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.485, "judge_a": 0.3, "judge_b": 0.57, "mean": 0.4517, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7233, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.53, "judge_a": 0.4, "judge_b": 0.56, "mean": 0.4967, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.925, "judge_a": 0.75, "judge_b": 0.87, "mean": 0.8483, "spread": 0.175 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.553, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6444, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.835, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.765, "spread": 0.135 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.7, "judge_b": 0.96, "mean": 0.8467, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.11, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1667, "spread": 0.14 } } }, { "rank": 65, "id": "st_20261104_13329", "title": "Speedrunner – Faster Startup: Python, Ruby, PHP and Node CLI Tools", "url": "https://github.com/anthonyGosme/speedrunner", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T16:37:22.000Z", "jev": 0.7045, "judge_a": 0.442, "judge_b": 0.7075, "score": 0.6179, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 55, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Speedrunner is an open-source command-line accelerator that runs Python, Ruby, PHP and Node CLI tools through a warm daemon, forking it per call instead of restarting the interpreter.", "page": "https://rubric.hrih.si/editions/2026-11-04/65/", "card": "https://rubric.hrih.si/og/2026-11-04/65.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.543, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4742, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.6967, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.683, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.6011, "spread": 0.183 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.938, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.8225, "spread": 0.238 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.75, "judge_b": 0.91, "mean": 0.8811, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.97, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8833, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9233, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.23, "spread": 0.29 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.35, "judge_b": 0.16, "mean": 0.2433, "spread": 0.19 } } }, { "rank": 66, "id": "st_20261104_12955", "title": "Herdr vs. T3 vs. Amp: Software Factory Test", "url": "https://techstackups.com/comparisons/herdr-vs-t3-code-vs-amp/", "domain": "techstackups.com", "site": "techstackups.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T13:24:39.000Z", "jev": 0.6521, "judge_a": 0.4715, "judge_b": 0.7242, "score": 0.6159, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 56, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A tester built the same minimal software factory three ways, using Herdr (a terminal multiplexer for coding agents), T3 Code (a desktop, web and mobile control app) and Amp (a coding agent with cloud machines and a manager agent, Puck).", "page": "https://rubric.hrih.si/editions/2026-11-04/66/", "card": "https://rubric.hrih.si/og/2026-11-04/66.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.485, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.455, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7267, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.497, "judge_a": 0.4, "judge_b": 0.52, "mean": 0.4722, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.843, "judge_a": 0.7, "judge_b": 0.79, "mean": 0.7775, "spread": 0.143 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.927, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8156, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.75, "judge_b": 0.72, "mean": 0.8192, "spread": 0.268 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.5, "judge_b": 0.98, "mean": 0.7833, "spread": 0.48 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.0967, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1567, "spread": 0.12 } } }, { "rank": 67, "id": "st_20261104_13116", "title": "Rooting an Echo Plus 2 with AI", "url": "https://blog.tripton.dev/rooting-an-echo-plus-2-with-ai/", "domain": "blog.tripton.dev", "site": "Tripton’s Blog", "categories": [ "ai", "security", "hardware" ], "category_names": [ "AI & machine learning", "Security", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:39:48.000Z", "jev": 0.5841, "judge_a": 0.518, "judge_b": 0.7457, "score": 0.6159, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 57, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author documents rooting an Amazon Echo Plus 2 to run custom voice-assistant code and connect it to Home Assistant instead of Alexa. The work used MediaTek BootROM exploit tooling (MTKClient, Kamakiri) and a temporary boot-chain payload, with Codex, OpenAI's coding agent, doing most of the low-level engineering.", "page": "https://rubric.hrih.si/editions/2026-11-04/67/", "card": "https://rubric.hrih.si/og/2026-11-04/67.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.73, "mean": 0.5133, "spread": 0.43 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.81, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.47, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.4833, "spread": 0.03 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.54, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5233, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.87, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9592, "spread": 0.098 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.06, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.19, "spread": 0.13 } } }, { "rank": 68, "id": "st_20261104_13468", "title": "Eval-production behavior gaps in agent safety and the role of eval-awareness", "url": "https://alignment.openai.com/eval-production-behavior-gap/", "domain": "alignment.openai.com", "site": "alignment.openai.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T18:01:38.000Z", "jev": 0.6616, "judge_a": 0.47, "judge_b": 0.7014, "score": 0.611, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 59, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A new analysis of safety evaluations for AI agents finds a gap between how models perform on a \"Respecting Warnings\" test and on matched real-world Codex decisions, with gaps of 10 to 37 points.", "page": "https://rubric.hrih.si/editions/2026-11-04/68/", "card": "https://rubric.hrih.si/og/2026-11-04/68.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.685, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6183, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7033, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.703, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6278, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.635, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.585, "spread": 0.135 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7911, "spread": 0.353 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.79, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.7, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.7, "judge_b": 0.98, "mean": 0.8567, "spread": 0.28 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1133, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.24, "spread": 0.12 } } }, { "rank": 69, "id": "st_20261104_13571", "title": "How authentik is handling post-quantum cryptography", "url": "https://goauthentik.io/blog/2026-11-03-how-authentik-is-handling-post-quantum-cryptography/", "domain": "goauthentik.io", "site": "goauthentik.io", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T19:31:50.000Z", "jev": 0.7244, "judge_a": 0.478, "judge_b": 0.6303, "score": 0.6108, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 60, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "authentik, an open source identity provider run by the public benefit company Authentik Security, has added post-quantum cryptography support: it already uses a hybrid X25519MLKEM768 key exchange, and version 2026.11 adds ML-DSA certificates, passkeys and OIDC token signing.", "page": "https://rubric.hrih.si/editions/2026-11-04/69/", "card": "https://rubric.hrih.si/og/2026-11-04/69.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.67, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6167, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7233, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.567, "judge_a": 0.5, "judge_b": 0.52, "mean": 0.5289, "spread": 0.067 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.848, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.7325, "spread": 0.248 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.743, "judge_a": 0.72, "judge_b": 0.72, "mean": 0.7278, "spread": 0.023 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.943, "judge_a": 0.68, "judge_b": 0.82, "mean": 0.8142, "spread": 0.262 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.96, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.93, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.45, "judge_b": 0.35, "mean": 0.2967, "spread": 0.36 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.11, "judge_a": 0.2, "judge_b": 0.3, "mean": 0.2033, "spread": 0.19 } } }, { "rank": 70, "id": "st_20261104_12869", "title": "Reverse Engineering JavaScript SDKs", "url": "https://github.com/JWally/dfp-redteam-index", "domain": "github.com", "site": "GitHub", "categories": [ "security" ], "category_names": [ "Security" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T12:27:17.000Z", "jev": 0.6722, "judge_a": 0.447, "judge_b": 0.7108, "score": 0.61, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 61, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A GitHub project titled \"Reverse Engineering JavaScript SDKs\" catalogs authorized red-team analysis of device-fingerprinting and anti-bot vendors, including TikTok, Stytch, hCaptcha, and Castle.", "page": "https://rubric.hrih.si/editions/2026-11-04/70/", "card": "https://rubric.hrih.si/og/2026-11-04/70.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.65, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.56, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.73, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.55, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.4567, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.7, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6233, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.84, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7233, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9358, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9367, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2933, "spread": 0.27 } } }, { "rank": 71, "id": "st_20261104_13453", "title": "The Inference Stack Behind OCRia", "url": "https://www.llamaindex.ai/blog/the-inference-stack-behind-ocria", "domain": "llamaindex.ai", "site": "llamaindex.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T18:14:26.000Z", "jev": 0.6667, "judge_a": 0.4505, "judge_b": 0.7751, "score": 0.6307, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 44, "topic_penalty": 0.08, "repeat_penalty": 0.0214, "summary": "Inference-optimization write-up for OCRia-1, a document parsing model, reports a jump from about 285 to about 2,800 tokens per second on a single B200 GPU. The gains come from workload-aware mixed-precision quantization, speculative decoding with DFlash2, and a custom CUDA kernel for GDN state updates that cut per-layer time roughly…", "page": "https://rubric.hrih.si/editions/2026-11-04/71/", "card": "https://rubric.hrih.si/og/2026-11-04/71.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5417, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.7867, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.533, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5178, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.803, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7542, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7067, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.968, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9225, "spread": 0.118 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9167, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.19, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2567, "spread": 0.17 } } }, { "rank": 72, "id": "st_20261104_12748", "title": "Native masonry layouts with CSS grid lanes", "url": "https://piccalil.li/blog/native-masonry-layouts-with-css-grid-lanes/", "domain": "piccalil.li", "site": "Piccalilli", "categories": [ "programming", "design" ], "category_names": [ "Programming & dev tools", "Design & UX" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T09:44:53.000Z", "jev": 0.581, "judge_a": 0.561, "judge_b": 0.6793, "score": 0.6071, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 64, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "CSS now has native masonry through display: grid-lanes, a layout mode the CSS Working Group settled on after years of debate over whether masonry should belong to Flexbox, Grid, or its own display value.", "page": "https://rubric.hrih.si/editions/2026-11-04/72/", "card": "https://rubric.hrih.si/og/2026-11-04/72.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.63, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5867, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.48, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.62, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.573, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.5844, "spread": 0.027 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7233, "spread": 0.05 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.557, "judge_a": 0.55, "judge_b": 0.63, "mean": 0.5789, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.845, "judge_a": 0.75, "judge_b": 0.7, "mean": 0.765, "spread": 0.145 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1067, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.21, "spread": 0.15 } } }, { "rank": 73, "id": "st_20261104_13343", "title": "StateBench: Testing how language models track state changes over long documents", "url": "https://allenai.org/blog/statebench", "domain": "allenai.org", "site": "Ai2 Blog", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T08:00:00.000Z", "jev": 0.6102, "judge_a": 0.4325, "judge_b": 0.7772, "score": 0.6065, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 65, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "StateBench, released by Allen Institute for AI researchers, is a benchmark that tests how well language models track evolving entity states across long documents, using four difficulty levels from immutable assignment to general finite swaps.", "page": "https://rubric.hrih.si/editions/2026-11-04/73/", "card": "https://rubric.hrih.si/og/2026-11-04/73.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.638, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6058, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7167, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.63, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5667, "spread": 0.37 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.543, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5375, "spread": 0.37 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.85, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7833, "spread": 0.3 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.8, "judge_a": 0.75, "judge_b": 0.92, "mean": 0.8233, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9033, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0833, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2433, "spread": 0.12 } } }, { "rank": 74, "id": "st_20261104_13509", "title": "Automatic Construction of Strong Simple Baselines", "url": "https://arxiv.org/abs/2610.40700", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T18:32:27.000Z", "jev": 0.5572, "judge_a": 0.508, "judge_b": 0.7528, "score": 0.606, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 66, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An LLM-driven workflow was used to automatically construct simple baselines for 36 supervised prediction tasks drawn from Nature, Science, and Cell-family papers proposing deep learning methods for scientific problems.", "page": "https://rubric.hrih.si/editions/2026-11-04/74/", "card": "https://rubric.hrih.si/og/2026-11-04/74.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.61, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.61, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.49, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.6567, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.693, "judge_a": 0.5, "judge_b": 0.63, "mean": 0.6078, "spread": 0.193 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.485, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5683, "spread": 0.235 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8444, "spread": 0.253 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7767, "spread": 0.29 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.97, "mean": 0.8933, "spread": 0.17 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0433, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2967, "spread": 0.18 } } }, { "rank": 75, "id": "st_20261104_12596", "title": "35% Faster Than the Filesystem", "url": "https://www.sqlite.org/fasterthanfs.html", "domain": "sqlite.org", "site": "sqlite.org", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T04:44:30.000Z", "jev": 0.5571, "judge_a": 0.509, "judge_b": 0.6915, "score": 0.5859, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 92, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "SQLite's author reports that reading and writing blobs around 10 KB from a SQLite database runs about 35% faster than individual files on disk, and that the database uses roughly 20% less space.", "page": "https://rubric.hrih.si/editions/2026-11-04/75/", "card": "https://rubric.hrih.si/og/2026-11-04/75.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4817, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.49, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6033, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.557, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5289, "spread": 0.13 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.775, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.7117, "spread": 0.175 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.9011, "spread": 0.173 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8917, "spread": 0.195 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.9167, "spread": 0.03 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.07, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.6, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.31, "spread": 0.52 } } }, { "rank": 76, "id": "st_20261104_13120", "title": "The Qwen weight edits are fixing the repetition and works on non math as well", "url": "https://www.hedwigai.com/blog/weight-edit-math-or-loops", "domain": "hedwigai.com", "site": "hedwigai.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T14:38:50.000Z", "jev": 0.6103, "judge_a": 0.4905, "judge_b": 0.7168, "score": 0.6058, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 67, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An MIT CSAIL team had an AI agent edit 0.016% of the weights of Qwen3.5-0.8B, raising its MATH-500 score from 7.9% to 48.1% with thinking on. A follow-up analysis finds the edit mainly removes a direction tied to repetitive thinking loops, not math skill: it also stops loops on non-math…", "page": "https://rubric.hrih.si/editions/2026-11-04/76/", "card": "https://rubric.hrih.si/og/2026-11-04/76.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.585, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7567, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.607, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5756, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.578, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5692, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8367, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8475, "spread": 0.283 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.75, "judge_b": 0.98, "mean": 0.86, "spread": 0.23 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.15, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.32, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.2733, "spread": 0.12 } } }, { "rank": 77, "id": "st_20261104_13783", "title": "Prompting Is Programming", "url": "https://www.brethorsting.com/blog/2026/11/prompting-is-programming/", "domain": "brethorsting.com", "site": "Aaron Brethorst", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T23:49:06.000Z", "jev": 0.6622, "judge_a": 0.5156, "judge_b": 0.6359, "score": 0.6045, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 70, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Brett Thorsting argues that prompting an AI well is essentially programming: specifying inputs, outputs, steps, edge cases, and success criteria, habits that engineers build from years of being corrected by compilers.", "page": "https://rubric.hrih.si/editions/2026-11-04/77/", "card": "https://rubric.hrih.si/og/2026-11-04/77.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.635, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.535, "spread": 0.285 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7833, "spread": 0.03 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.937, "judge_a": 0.67, "judge_b": 0.78, "mean": 0.7956, "spread": 0.267 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6833, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.14, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.2733, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.528, "judge_a": 0.25, "judge_b": 0.38, "mean": 0.3858, "spread": 0.277 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.7433, "spread": 0.4 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0467, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3867, "spread": 0.15 } } }, { "rank": 78, "id": "st_20261104_12912", "title": "Why can't Claude agents running in the cloud do everything you want on GitHub?", "url": "https://code.claude.com/docs/en/cloud-environments", "domain": "code.claude.com", "site": "Claude Code Docs", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T12:43:44.000Z", "jev": 0.632, "judge_a": 0.48, "judge_b": 0.7007, "score": 0.6042, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 71, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Claude cloud agent sessions run inside configurable environments that control network access, environment variables, setup scripts, and secrets, and the same environments work across the desktop app, mobile app, browser, CLI, scheduled tasks, and Claude Tag.", "page": "https://rubric.hrih.si/editions/2026-11-04/78/", "card": "https://rubric.hrih.si/og/2026-11-04/78.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.588, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5025, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6267, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.437, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.4956, "spread": 0.113 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.908, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.8458, "spread": 0.157 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.703, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7078, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.8358, "spread": 0.228 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8467, "spread": 0.24 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.15, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.19, "spread": 0.26 } } }, { "rank": 79, "id": "st_20261104_13425", "title": "A 3KB LLM inference engine in C", "url": "https://github.com/utsav1033/bytesized", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T17:48:31.000Z", "jev": 0.6094, "judge_a": 0.5673, "judge_b": 0.6338, "score": 0.6035, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 72, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer built a C program of 2,946 bytes that runs the SmolLM2-135M language model directly from the original Hugging Face safetensors and tokenizer.json files, with no converter. Its greedy outputs match HF transformers token for token on three 100-token prompts.", "page": "https://rubric.hrih.si/editions/2026-11-04/79/", "card": "https://rubric.hrih.si/og/2026-11-04/79.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.45, "judge_b": 0.43, "mean": 0.4617, "spread": 0.075 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.8533, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.5, "judge_b": 0.35, "mean": 0.45, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.518, "judge_a": 0.3, "judge_b": 0.52, "mean": 0.4458, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.977, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.9022, "spread": 0.127 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.95, "judge_b": 0.96, "mean": 0.9692, "spread": 0.048 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9133, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0333, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.2, "judge_b": 0.05, "mean": 0.17, "spread": 0.21 } } }, { "rank": 80, "id": "st_20261104_13444", "title": "Voxl: Fast remote desktop streaming written in Rust", "url": "https://github.com/tembo/voxl", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops", "it" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "IT & networking" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T18:28:35.000Z", "jev": 0.6625, "judge_a": 0.4725, "judge_b": 0.6687, "score": 0.6012, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 73, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Voxl is an open-source Rust tool that streams an X11 display, macOS screen or Windows desktop as H.264 over WebSocket to a browser client decoded with WebCodecs, adding input, clipboard, audio and file transfer.", "page": "https://rubric.hrih.si/editions/2026-11-04/80/", "card": "https://rubric.hrih.si/og/2026-11-04/80.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4492, "spread": 0.167 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.72, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.47, "spread": 0.03 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.86, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.76, "spread": 0.16 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.8211, "spread": 0.383 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8692, "spread": 0.298 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1467, "spread": 0.24 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2267, "spread": 0.18 } } }, { "rank": 81, "id": "st_20261104_12661", "title": "Drama Studio: local realistic text-to-speech synthesis engine for Apple Silicon", "url": "https://github.com/maralski/drama-studio", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T07:23:37.000Z", "jev": 0.6449, "judge_a": 0.439, "judge_b": 0.7153, "score": 0.5997, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 74, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Drama Studio is a native macOS app that runs Fish Audio's Drama 3 text-to-speech model entirely on Apple silicon, with no MLX or PyTorch at runtime. Its 35B mixture-of-experts backbone runs on hand-written Metal kernels that read the released weights directly, and the audio codec runs through Core ML.", "page": "https://rubric.hrih.si/editions/2026-11-04/81/", "card": "https://rubric.hrih.si/og/2026-11-04/81.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.52, "judge_a": 0.3, "judge_b": 0.65, "mean": 0.49, "spread": 0.35 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7133, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.377, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.3856, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.875, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.735, "spread": 0.325 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.943, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.7678, "spread": 0.443 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9425, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9367, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.15, "judge_b": 0.16, "mean": 0.1267, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2133, "spread": 0.14 } } }, { "rank": 82, "id": "st_20261104_13708", "title": "Krea 3 Technical Report", "url": "https://www.krea.ai/blog/krea-3-technical-report", "domain": "krea.ai", "site": "Krea", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T22:18:36.000Z", "jev": 0.5805, "judge_a": 0.5888, "judge_b": 0.6268, "score": 0.5986, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 76, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Krea has released a technical report for Krea 3, an image-to-image model it describes as fast, cost-efficient, and competitive with frontier systems. The report covers its architecture, which feeds VAE-encoded reference images into a diffusion transformer alongside a vision-language text encoder, plus training methods for variable reference counts and native…", "page": "https://rubric.hrih.si/editions/2026-11-04/82/", "card": "https://rubric.hrih.si/og/2026-11-04/82.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.705, "judge_a": 0.62, "judge_b": 0.76, "mean": 0.695, "spread": 0.14 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.7867, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.517, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5622, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.643, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6408, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7433, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.97, "judge_a": 0.92, "judge_b": 0.96, "mean": 0.95, "spread": 0.05 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.35, "judge_b": 0.32, "mean": 0.2733, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.62, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6, "spread": 0.38 } } }, { "rank": 83, "id": "st_20261104_13794", "title": "Craft CMS 5.12.0: Added an authenticated HTTP MCP server", "url": "https://github.com/craftcms/cms/releases/tag/5.12.0", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", 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"https://www.technologyreview.com/2026/11/02/1147846/the-agentic-web-is-here/", "domain": "technologyreview.com", "site": "MIT Technology Review", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T09:16:41.000Z", "jev": 0.6334, "judge_a": 0.541, "judge_b": 0.6135, "score": 0.5959, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 78, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article traces the idea of the agentic web back to Tim Berners-Lee's 2001 Semantic Web vision of software agents navigating the internet on people's behalf, which stalled because it required heavy manual annotation of web pages.", "page": "https://rubric.hrih.si/editions/2026-11-04/84/", "card": "https://rubric.hrih.si/og/2026-11-04/84.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.785, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7017, 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"page": "https://rubric.hrih.si/editions/2026-11-04/85/", "card": "https://rubric.hrih.si/og/2026-11-04/85.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5375, "spread": 0.393 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6933, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.77, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.68, "spread": 0.37 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.945, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.815, "spread": 0.245 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.577, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5322, "spread": 0.42 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.86, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.6933, "spread": 0.31 }, 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"https://deepsense.ai/blog/harness-engineering-for-coding-agents-9-patterns-for-more-reliable-ai-assisted-development/", "domain": "deepsense.ai", "site": "deepsense.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T11:31:11.000Z", "jev": 0.6776, "judge_a": 0.4785, "judge_b": 0.6222, "score": 0.5927, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 81, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A deepsense.ai blog post titled \"Harness Engineering for Coding Agents\" argues that reliable AI-assisted development depends less on better prompts and more on the system built around a coding agent.", "page": "https://rubric.hrih.si/editions/2026-11-04/87/", "card": "https://rubric.hrih.si/og/2026-11-04/87.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.648, "judge_a": 0.3, 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"judge_a": 0.25, "judge_b": 0.22, "mean": 0.1833, "spread": 0.17 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.8, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.61, "spread": 0.45 } } }, { "rank": 88, "id": "st_20261104_12623", "title": "The Nordic Token Tax", "url": "https://research.klang.ai/papers/the-nordic-token-tax", "domain": "research.klang.ai", "site": "Klang Research", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T05:47:08.000Z", "jev": 0.5986, "judge_a": 0.5085, "judge_b": 0.6698, "score": 0.5923, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 82, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Klang, a Nordic AI company, measured the same 3,000 sentences across twelve frontier models and found every one charges 30% or more extra for Swedish, Norwegian, Danish and Finnish than for English.", "page": "https://rubric.hrih.si/editions/2026-11-04/88/", "card": "https://rubric.hrih.si/og/2026-11-04/88.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.56, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5433, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.7, "judge_b": 0.85, "mean": 0.7367, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.66, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6267, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.623, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6342, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.88, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.76, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.78, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.6533, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.91, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.18, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.35, "judge_b": 0.2, "mean": 0.2967, "spread": 0.15 } } }, { "rank": 89, "id": "st_20261104_12666", "title": "Nvidia – open-source auto-ontology", "url": "https://github.com/NVIDIA/auto-ontology", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "devops", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Data" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T07:04:50.000Z", "jev": 0.6661, "judge_a": 0.4305, "judge_b": 0.6782, "score": 0.5916, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 83, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "NVIDIA has released Auto Ontology, an open-source Apache 2.0 library that builds a context layer for AI agents from enterprise metadata. It reconciles conflicting metric definitions across systems and exposes them via MCP, CLI, and API, so agents can ground answers, validate generated SQL against certified definitions, and return provenance.…", "page": "https://rubric.hrih.si/editions/2026-11-04/89/", "card": "https://rubric.hrih.si/og/2026-11-04/89.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.625, "judge_a": 0.4, "judge_b": 0.65, "mean": 0.5583, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.5, "judge_b": 0.85, "mean": 0.6667, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.46, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5433, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.898, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7925, "spread": 0.198 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.803, "judge_a": 0.4, "judge_b": 0.7, "mean": 0.6344, "spread": 0.403 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7883, "spread": 0.385 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.93, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2467, "spread": 0.24 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2867, "spread": 0.27 } } }, { "rank": 90, "id": "st_20261104_12760", "title": "How to Parallize Your Tree Operations", "url": "https://liljedue.dk/blog/parallel-trees/", "domain": "liljedue.dk", "site": "liljedue.dk", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T09:32:28.000Z", "jev": 0.5352, "judge_a": 0.4693, "judge_b": 0.7698, "score": 0.5914, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 84, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The post explains how to parallelize tree operations by flattening a tree into an array of (value, parent index) pairs, which makes a map over nodes take O(n) work and O(1) span regardless of tree shape.", "page": "https://rubric.hrih.si/editions/2026-11-04/90/", "card": "https://rubric.hrih.si/og/2026-11-04/90.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.502, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5075, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.47, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.65, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.463, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4478, "spread": 0.28 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.533, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5608, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.837, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7789, "spread": 0.3 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9592, "spread": 0.098 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 1, "mean": 0.9367, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0233, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.15, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0933, "spread": 0.12 } } }, { "rank": 91, "id": "st_20261104_13803", "title": "TLAPS-Bench: Formally Proving Correctness with TLA+ Proof System", "url": "https://arxiv.org/abs/2611.02849", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T23:31:40.000Z", "jev": 0.5693, "judge_a": 0.4275, "judge_b": 0.7751, "score": 0.5906, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 85, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "TLAPS-Bench is a benchmark and harness for having AI agents construct machine-checked TLA+ proofs of real-world system specifications, such as cache coherence, Byzantine consensus, and write-ahead logs. It includes 265 proof targets from 95 specifications, with safeguards against reward hacking.", "page": "https://rubric.hrih.si/editions/2026-11-04/91/", "card": "https://rubric.hrih.si/og/2026-11-04/91.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.578, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5858, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.55, "judge_b": 0.93, "mean": 0.6833, "spread": 0.38 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.547, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5256, "spread": 0.33 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.507, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5425, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7744, "spread": 0.403 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.915, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8917, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.93, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.0967, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.35, "judge_b": 0.14, "mean": 0.2433, "spread": 0.21 } } }, { "rank": 92, "id": "st_20261104_12473", "title": "From Heroku to Convox, and why we built Rack Gateway", "url": "https://docspring.com/blog/posts/from-heroku-to-convox-and-rack-gateway/", "domain": "docspring.com", "site": "docspring.com", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T00:20:35.000Z", "jev": 0.6467, "judge_a": 0.4475, "judge_b": 0.6755, "score": 0.5899, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 87, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Rack Gateway is an open-source access-control layer that DocSpring built for Convox, the platform it runs on in its own AWS account. It sits between tools and the rack, checks permissions, requires hardware-key approval for commands and deploys, and keeps a tamper-evident audit log.", "page": "https://rubric.hrih.si/editions/2026-11-04/92/", "card": "https://rubric.hrih.si/og/2026-11-04/92.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.475, "judge_a": 0.3, "judge_b": 0.61, "mean": 0.4617, "spread": 0.31 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.72, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.547, "judge_a": 0.4, "judge_b": 0.46, "mean": 0.4689, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.788, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7225, "spread": 0.188 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.783, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.7378, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.828, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.7292, "spread": 0.228 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8133, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.15, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1233, "spread": 0.07 } } }, { "rank": 93, "id": "st_20261104_13331", "title": "Steamac – Run Official ARM64 SteamOS VM on Apple Silicon", "url": "https://github.com/fxgl/steamac", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T16:36:00.000Z", "jev": 0.561, "judge_a": 0.501, "judge_b": 0.707, "score": 0.5896, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 88, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Steamac is an open-source project that runs Valve's official ARM64 SteamOS image in a lightweight virtual machine on macOS 15 and later on Apple Silicon, using Apple's Hypervisor.framework through libkrun. Graphics go from the guest's Vulkan stack via Venus to MoltenVK or the experimental KosmicKrisp driver, then to Metal.", "page": "https://rubric.hrih.si/editions/2026-11-04/93/", "card": "https://rubric.hrih.si/og/2026-11-04/93.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.52, "judge_a": 0.3, "judge_b": 0.65, "mean": 0.49, "spread": 0.35 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.73, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.36, "judge_a": 0.35, "judge_b": 0.35, "mean": 0.3533, "spread": 0.01 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.628, "judge_a": 0.5, "judge_b": 0.75, "mean": 0.6258, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.86, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.77, "spread": 0.26 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 1, "mean": 0.9492, "spread": 0.15 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0633, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.2, "spread": 0.12 } } }, { "rank": 94, "id": "st_20261104_13488", "title": "Proving LLMs at Scale", "url": "https://attestable.com/blog/proving-llms-scale", "domain": "attestable.com", "site": "attestable.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T18:53:23.000Z", "jev": 0.5873, "judge_a": 0.5092, "judge_b": 0.6671, "score": 0.5878, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 89, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Attestable has published alpha results for a zero-knowledge prover that generates proofs for production-scale transformer inference, including Gemma 4 31B. On a single NVIDIA H100, the system produces proofs of 4.35 to 7.92 MiB that verify in 157 to 648 milliseconds on a CPU, without revealing model weights.", "page": "https://rubric.hrih.si/editions/2026-11-04/94/", "card": "https://rubric.hrih.si/og/2026-11-04/94.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.678, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6858, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.72, "judge_b": 0.88, "mean": 0.77, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.59, "judge_a": 0.6, "judge_b": 0.65, "mean": 0.6133, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.588, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6192, "spread": 0.37 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.587, "judge_a": 0.5, "judge_b": 0.74, "mean": 0.6089, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.745, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.7717, "spread": 0.075 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9233, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.16, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.2933, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.4767, "spread": 0.12 } } }, { "rank": 95, "id": "st_20261104_12936", "title": "Anthropic: AI could soon dramatically accelerate its own development", "url": "https://www.heise.de/en/news/Anthropic-AI-could-soon-dramatically-accelerate-its-own-development-11509439.html", "domain": "heise.de", "site": "heise online English", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T12:26:00.000Z", "jev": 0.5061, "judge_a": 0.62, "judge_b": 0.6364, "score": 0.5874, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 90, "topic_penalty": 0.08, "repeat_penalty": 0.0009, "summary": "Anthropic's revised IPO prospectus says its AI could soon significantly accelerate the development of new AI models, possibly reaching the threshold in its own safety policy where AI-assisted research advances model development at least twice as fast as unassisted work.", "page": "https://rubric.hrih.si/editions/2026-11-04/95/", "card": "https://rubric.hrih.si/og/2026-11-04/95.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7008, "spread": 0.037 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.7433, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.64, "judge_a": 0.8, "judge_b": 0.8, "mean": 0.7467, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.473, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5008, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.61, "spread": 0.02 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.453, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.3775, "spread": 0.153 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.79, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.81, "spread": 0.24 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.23, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.17, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.3, "judge_b": 0.4, "mean": 0.3533, "spread": 0.1 } } }, { "rank": 96, "id": "st_20261104_13080", "title": "Mac at any scale", "url": "https://namespace.so/blog/mac-at-any-scale", "domain": "namespace.so", "site": "Namespace", "categories": [ "devops", "it", "hardware" ], "category_names": [ "Infrastructure & DevOps", "IT & networking", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:53:42.000Z", "jev": 0.5737, "judge_a": 0.4555, "judge_b": 0.7283, "score": 0.5858, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 93, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Namespace, a cloud build and test provider, describes how it runs racks of MacBook Pros and Mac Minis in its datacenter to offer macOS instances for iOS app building and testing.", "page": "https://rubric.hrih.si/editions/2026-11-04/96/", "card": "https://rubric.hrih.si/og/2026-11-04/96.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.52, "judge_a": 0.3, "judge_b": 0.7, "mean": 0.5067, "spread": 0.4 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.75, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.483, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4778, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.638, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6558, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.52, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6467, "spread": 0.3 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.92, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.8433, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8867, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.1733, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.21, "spread": 0.07 } } }, { "rank": 97, "id": "st_20261104_12863", "title": "What Qwen is thinking when it gets stuck saying \"Wait\"", "url": "https://alisorcorp.github.io/inside-a-reasoning-loop/", "domain": "alisorcorp.github.io", "site": "alisorcorp.github.io", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T12:28:15.000Z", "jev": 0.5501, "judge_a": 0.552, "judge_b": 0.7135, "score": 0.6052, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 69, "topic_penalty": 0.08, "repeat_penalty": 0.0218, "summary": "A write-up by Alice Corp's researcher (alisorcorp.github.io) examines an edit made by GPT-6 Astra to Qwen3.5-0.8B that cuts its looping on math problems. The edit, a rank-one change to 64 neurons in two layers, flips a \"doubt\" direction, lifting MATH-500 accuracy from 7.9% to 48.1%.", "page": "https://rubric.hrih.si/editions/2026-11-04/97/", "card": "https://rubric.hrih.si/og/2026-11-04/97.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.4, "judge_b": 0.61, "mean": 0.5058, "spread": 0.21 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.7667, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.523, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5244, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.485, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.5717, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.913, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8311, "spread": 0.213 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.9, "judge_b": 0.91, "mean": 0.9308, "spread": 0.083 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8133, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0467, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2667, "spread": 0.17 } } }, { "rank": 98, "id": "st_20261104_13629", "title": "An MCP Server for Manpages", "url": "https://www.chainguard.dev/unchained/every-manual-page-in-chainguard-os-now-readable-by-your-agent", "domain": "chainguard.dev", "site": "chainguard.dev", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T21:24:51.000Z", "jev": 0.6869, "judge_a": 0.4595, "judge_b": 0.6032, "score": 0.5832, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 94, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author, a Canonical engineer, describes man.cgr.dev, a Chainguard OS site that renders all 59,419 manual pages from 1,286 packages as HTML, Markdown, raw roff, and JSON, pinned to exact package versions.", "page": "https://rubric.hrih.si/editions/2026-11-04/98/", "card": "https://rubric.hrih.si/og/2026-11-04/98.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4767, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7267, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.71, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.5133, "spread": 0.33 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.845, "judge_a": 0.65, "judge_b": 0.68, "mean": 0.725, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7233, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.773, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6642, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2, "spread": 0.18 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.25, "judge_b": 0.1, "mean": 0.2067, "spread": 0.17 } } }, { "rank": 99, "id": "st_20261104_13147", "title": "TIS-100 Bench: LLMs Push the Pareto Frontier in Programming Puzzle Games", "url": "https://tis-100.wincon.dev/", "domain": "tis-100.wincon.dev", "site": "Wincon", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T15:25:42.000Z", "jev": 0.5768, "judge_a": 0.4, "judge_b": 0.7675, "score": 0.5814, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 96, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A benchmark of 16 LLMs playing TIS-100, a programming puzzle game where solutions are scored on cycles, nodes, and instructions, found that top models like Opus 5.5 and GPT-6 Astra matched community records and found 133 new tradeoff points, with 18 beating a record outright.", "page": "https://rubric.hrih.si/editions/2026-11-04/99/", "card": "https://rubric.hrih.si/og/2026-11-04/99.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.563, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5442, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.7233, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.563, "judge_a": 0.3, "judge_b": 0.67, "mean": 0.5111, "spread": 0.37 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.52, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4967, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8733, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8617, "spread": 0.275 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.93, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.02, "mean": 0.0967, "spread": 0.18 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.38, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.2533, "spread": 0.3 } } }, { "rank": 100, "id": "st_20261104_12787", "title": "Nvidia tells cloud partners to cap Chinese business at 49%, FT reports", "url": "https://thenextweb.com/news/nvidia-cloud-partners-cap-china-business", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "hardware", "policy" ], "category_names": [ "AI & machine learning", "Hardware & devices", "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T10:15:20.000Z", "jev": 0.5665, "judge_a": 0.5936, "judge_b": 0.7149, "score": 0.625, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 48, "topic_penalty": 0.08, "repeat_penalty": 0.0437, "summary": "Nvidia has reportedly told its data centre partners to ensure at least 51% of cloud services go to non-Chinese customers on new contracts, according to the Financial Times, citing five sources. The company verbally conveyed the requirement, which it had not imposed before, and declined to confirm it.", "page": "https://rubric.hrih.si/editions/2026-11-04/100/", "card": "https://rubric.hrih.si/og/2026-11-04/100.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.728, "judge_a": 0.62, "judge_b": 0.78, "mean": 0.7092, "spread": 0.16 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.78, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.74, "mean": 0.6633, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.465, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5283, "spread": 0.155 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.397, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.4889, "spread": 0.223 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.293, "judge_a": 0.12, "judge_b": 0.18, "mean": 0.1975, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.85, "judge_b": 0.92, "mean": 0.8767, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.0467, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.18, "spread": 0.23 } } }, { "rank": 101, "id": "st_20261104_13342", "title": "Palette: teaching a small model to build decks in your design language", "url": "https://huggingface.co/blog/ibm-research/palette-deck-building", "domain": "huggingface.co", "site": "Hugging Face Blog", "categories": [ "programming", "ai", "design" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Design & UX" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T16:35:36.000Z", "jev": 0.6201, "judge_a": 0.3835, "judge_b": 0.735, "score": 0.5795, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 97, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Palette is a self-hostable approach that fine-tunes a 20B open-weight model with LoRA to generate slide decks in the IBM Design Language, splitting work between a designer role that writes a JSON brief and a coder role that draws each slide in parallel.", "page": "https://rubric.hrih.si/editions/2026-11-04/101/", "card": "https://rubric.hrih.si/og/2026-11-04/101.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.545, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5217, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.7233, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.56, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4967, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.765, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.665, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.797, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7056, "spread": 0.197 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.865, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.795, "spread": 0.165 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9233, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.1533, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.45, "judge_a": 0.35, "judge_b": 0.2, "mean": 0.3333, "spread": 0.25 } } }, { "rank": 102, "id": "st_20261104_12765", "title": "Android patch day: Critical flaws enable attacks without user interaction", "url": "https://www.heise.de/en/news/Android-patch-day-Critical-flaws-enable-attacks-without-user-interaction-11509041.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T09:44:00.000Z", "jev": 0.5626, "judge_a": 0.5216, "judge_b": 0.6474, "score": 0.5772, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 99, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Heise Online reports that Google's November 2026 Android security bulletin fixes critical flaws in Android 14 through 17 that could let attackers execute remote code without user interaction. Google's patch levels 2026-11-01 and 2026-11-05 address these, including Bluetooth, modem, and Qualcomm component issues.", "page": "https://rubric.hrih.si/editions/2026-11-04/102/", "card": "https://rubric.hrih.si/og/2026-11-04/102.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5842, "spread": 0.232 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6367, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.743, "judge_a": 0.67, "judge_b": 0.58, "mean": 0.6644, "spread": 0.163 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.51, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6033, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.497, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5056, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.555, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.4117, "spread": 0.255 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.98, "mean": 0.86, "spread": 0.28 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.24, "spread": 0.28 } } }, { "rank": 103, "id": "st_20261104_13358", "title": "Postman silently auto-installs AI plugins and attempts to share all API data", "url": "https://github.com/postmanlabs/postman-app-support/issues/14370", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Other", "depth": "three_judges", "published_at": "2026-11-03T17:22:37.000Z", "jev": 0.6905, "judge_a": 0.643, "judge_b": 0.6937, "score": 0.5756, "contested": true, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 100, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A GitHub issue filed against Postman's desktop app, submitted by a macOS user, reports that after opening Postman 12.33.4, the app's log recorded automatic installation of Postman plugins for Codex and Claude Code, along with a global Codex configuration change, a startup hook instructing agents to route API work through…", "page": "https://rubric.hrih.si/editions/2026-11-04/103/", "card": "https://rubric.hrih.si/og/2026-11-04/103.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6375, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.86, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8533, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.617, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6122, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.818, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7792, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.913, "judge_a": 0.8, "judge_b": 0.76, "mean": 0.8244, "spread": 0.153 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.8008, "spread": 0.283 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.88, "mean": 0.89, "spread": 0.02 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0333, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.83, "judge_a": 0.25, "judge_b": 0.82, "mean": 0.6333, "spread": 0.58 } } }, { "rank": 104, "id": "st_20261104_13020", "title": "GSLM2 MultiStream Speech Rep Learning and Gen Spoken Language Modeling", "url": "https://arxiv.org/abs/2611.01143", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T13:33:49.000Z", "jev": 0.5577, "judge_a": 0.4075, "judge_b": 0.7599, "score": 0.575, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 101, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "GSLM2 is a Meta Superintelligence Labs framework for generative spoken language modeling that learns discrete speech units at multiple levels of abstraction and models them jointly as several streams.", "page": "https://rubric.hrih.si/editions/2026-11-04/104/", "card": "https://rubric.hrih.si/og/2026-11-04/104.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.708, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6458, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.4, "judge_b": 0.9, "mean": 0.6033, "spread": 0.5 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.553, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4911, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.475, "judge_a": 0.3, "judge_b": 0.66, "mean": 0.4783, "spread": 0.36 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8933, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.935, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.93, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.0533, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.25, "judge_b": 0.2, "mean": 0.3067, "spread": 0.27 } } }, { "rank": 105, "id": "st_20261104_13200", "title": "Are we going faster, Doc?", "url": "https://weli.dev/blog/are-we-going-faster-doc/", "domain": "weli.dev", "site": "Weli's blog", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T15:59:13.000Z", "jev": 0.5962, "judge_a": 0.4706, "judge_b": 0.6584, "score": 0.575, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 102, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A tech lead argues that LLMs speed up the first, most visible phase of building a feature, but not the long tail of maintenance, review, and support, where understanding code remains the bottleneck.", "page": "https://rubric.hrih.si/editions/2026-11-04/105/", "card": "https://rubric.hrih.si/og/2026-11-04/105.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4533, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7533, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.943, "judge_a": 0.67, "judge_b": 0.78, "mean": 0.7978, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.708, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6758, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.023, "judge_a": 0.25, "judge_b": 0.38, "mean": 0.2178, "spread": 0.357 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.393, "judge_a": 0.2, "judge_b": 0.28, "mean": 0.2908, "spread": 0.193 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.8, "judge_a": 0.35, "judge_b": 0.93, "mean": 0.6933, "spread": 0.58 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.05, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.2967, "spread": 0.15 } } }, { "rank": 106, "id": "st_20261104_13064", "title": "We built one harness for every agent, inspired by Ramp's Inspect", "url": "https://www.metaview.ai/resources/blog/how-we-built-one-harness-for-every-metaview-agent", "domain": "metaview.ai", "site": "metaview.ai", "categories": [ "programming", "ai", "devops", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Product" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T14:05:10.000Z", "jev": 0.6469, "judge_a": 0.382, "judge_b": 0.6946, "score": 0.5744, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 104, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Metaview, a recruiting-software company, describes how it consolidated its AI agents onto one shared harness inspired by Ramp's internal Inspect coding agent. The harness pairs Cloudflare control planes, Modal sandboxes, and an AWS broker that keeps credentials out of the sandbox, so new agents now take about two hours to…", "page": "https://rubric.hrih.si/editions/2026-11-04/106/", "card": "https://rubric.hrih.si/og/2026-11-04/106.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.595, "judge_a": 0.3, "judge_b": 0.67, "mean": 0.5217, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7467, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5022, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.808, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7292, "spread": 0.208 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.427, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5156, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.788, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7225, "spread": 0.188 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.5, "judge_b": 0.88, "mean": 0.7467, "spread": 0.38 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.19, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.35, "judge_b": 0.3, "mean": 0.3067, "spread": 0.08 } } }, { "rank": 107, "id": "st_20261104_12606", "title": "MCPgraphy – a proxy that learns when your agent's MCP tools work", "url": "https://github.com/hemanth1999k/mcpgraphy", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T05:07:22.000Z", "jev": 0.6971, "judge_a": 0.3955, "judge_b": 0.6301, "score": 0.5742, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 105, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "MCPgraphy is an open-source proxy, published by its author hemanth1999k on Hacker News, that sits between an agent and any MCP server and learns which tools work in which resource states from the agent's own calls.", "page": "https://rubric.hrih.si/editions/2026-11-04/107/", "card": "https://rubric.hrih.si/og/2026-11-04/107.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.573, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4742, "spread": 0.273 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7267, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.547, "judge_a": 0.4, "judge_b": 0.45, "mean": 0.4656, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7367, "spread": 0.29 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7833, "spread": 0.43 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8642, "spread": 0.293 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.91, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.25, "spread": 0.33 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2867, "spread": 0.12 } } }, { "rank": 108, "id": "st_20261104_12944", "title": "Human in the Loop Is Not the Solution", "url": "https://www.joelfickson.com/human-in-the-loop-is-not-the-solution.html", "domain": "joelfickson.com", "site": "Joel Fickson Ngozo", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T13:29:44.000Z", "jev": 0.6231, "judge_a": 0.436, "judge_b": 0.6634, "score": 0.5742, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 106, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The essay argues that \"human in the loop\" review of AI agent output is becoming a bottleneck and a coping mechanism, and that industries like automotive manufacturing and munitions testing managed volume by having machines halt on their own and humans oversee the process.", "page": "https://rubric.hrih.si/editions/2026-11-04/108/", "card": "https://rubric.hrih.si/og/2026-11-04/108.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5067, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.933, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7711, "spread": 0.333 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.73, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.65, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.407, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.4689, "spread": 0.143 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.518, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.4158, "spread": 0.167 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6767, "spread": 0.41 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0567, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.3, "judge_b": 0.32, "mean": 0.3633, "spread": 0.17 } } }, { "rank": 109, "id": "st_20261104_13322", "title": "Do We Still Have to Denormalize? Have Your Schema and Search It Too", "url": "https://www.paradedb.com/blog/joins", "domain": "paradedb.com", "site": "paradedb.com", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T16:42:53.000Z", "jev": 0.6909, "judge_a": 0.3915, "judge_b": 0.6403, "score": 0.5742, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 107, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "ParadeDB's Stu Hood describes making multi-table search joins fast in Postgres without denormalizing data into an external engine. On the StackOverflow benchmark, a three-table top-K search that took about two minutes in vanilla Postgres runs in roughly 100 ms, using a new Rank Join operator and a DataFusion-based columnar executor.", "page": "https://rubric.hrih.si/editions/2026-11-04/109/", "card": "https://rubric.hrih.si/og/2026-11-04/109.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.688, "judge_a": 0.35, "judge_b": 0.63, "mean": 0.5558, "spread": 0.338 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7233, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.61, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.52, "spread": 0.21 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.803, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7075, "spread": 0.203 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.937, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8322, "spread": 0.237 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.9192, "spread": 0.128 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.97, "mean": 0.8867, "spread": 0.17 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.5, "judge_b": 0.38, "mean": 0.3367, "spread": 0.37 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.5, "judge_b": 0.18, "mean": 0.35, "spread": 0.32 } } }, { "rank": 110, "id": "st_20261104_13752", "title": "Siri AI is now open to all developers", "url": "https://developer.apple.com/news/?id=gdk2umrf", "domain": "developer.apple.com", "site": "developer.apple.com", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T22:31:28.000Z", "jev": 0.5366, "judge_a": 0.584, "judge_b": 0.6016, "score": 0.574, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 108, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Apple has opened a Developer Preview that lets any developer connect their app to Siri AI, its more conversational and context-aware assistant, using App Intents. Developers can expose custom actions, such as booking or checking deliveries, and combine them through new \"skills.\" The preview is in beta across several Apple…", "page": "https://rubric.hrih.si/editions/2026-11-04/110/", "card": "https://rubric.hrih.si/og/2026-11-04/110.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.6833, "spread": 0.16 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.48, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.6867, "spread": 0.4 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.537, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.6856, "spread": 0.263 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.803, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8075, "spread": 0.02 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.49, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5033, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.553, "judge_a": 0.35, "judge_b": 0.46, "mean": 0.4542, "spread": 0.203 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.89, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.33, "judge_a": 0.35, "judge_b": 0.78, "mean": 0.4867, "spread": 0.45 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.32, "judge_a": 0.3, "judge_b": 0.34, "mean": 0.32, "spread": 0.04 } } }, { "rank": 111, "id": "st_20261104_12468", "title": "UK faces ‘unprecedented rise’ in cyberattacks in coming months, warns cyber chief", "url": "https://therecord.media/uk-ncsc-predicts-rise-cyberattacks-coming-months", "domain": "therecord.media", "site": "The Record (Recorded Future News)", "categories": [ "ai", "security", "policy" ], "category_names": [ "AI & machine learning", "Security", "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T00:01:00.000Z", "jev": 0.661, "judge_a": 0.3755, "judge_b": 0.7098, "score": 0.582, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 95, "topic_penalty": 0.08, "repeat_penalty": 0.0092, "summary": "The UK's National Cyber Security Centre warns of an \"unprecedented rise\" in cyber incidents in coming months, according to its chief Richard Horne, speaking in the agency's annual report.", "page": "https://rubric.hrih.si/editions/2026-11-04/111/", "card": "https://rubric.hrih.si/og/2026-11-04/111.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.723, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.6142, "spread": 0.323 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.71, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.97, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.7467, "spread": 0.42 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.593, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.5242, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.387, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.4856, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.48, "judge_a": 0.1, "judge_b": 0.48, "mean": 0.3533, "spread": 0.38 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8033, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.07, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.2833, "spread": 0.22 } } }, { "rank": 112, "id": "st_20261104_12499", "title": "Speridlabs/Idol · Hugging Face", "url": "https://huggingface.co/speridlabs/idol", "domain": "huggingface.co", "site": "huggingface.co", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T00:41:58.000Z", "jev": 0.5673, "judge_a": 0.4665, "judge_b": 0.6826, "score": 0.5721, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 109, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Speridlabs has released Idol, an open model that turns a single image of an object into a 3D Gaussian splat, inferring the unseen sides. It runs on consumer GPUs with 7.2 GB of VRAM, generates up to about one million Gaussians, and reports competitive benchmark scores against TripoSplat, TRELLIS and…", "page": "https://rubric.hrih.si/editions/2026-11-04/112/", "card": "https://rubric.hrih.si/og/2026-11-04/112.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.558, "judge_a": 0.45, "judge_b": 0.65, "mean": 0.5525, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.68, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.463, "judge_a": 0.45, "judge_b": 0.42, "mean": 0.4444, "spread": 0.043 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.65, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7911, "spread": 0.353 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.948, "judge_a": 0.65, "judge_b": 0.85, "mean": 0.8158, "spread": 0.298 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9267, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1633, "spread": 0.22 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.35, "judge_b": 0.2, "mean": 0.3067, "spread": 0.17 } } }, { "rank": 113, "id": "st_20261104_13413", "title": "P-H3-Image: Turning a SOTA Video Model into an Efficient Open Image Model", "url": "https://www.pruna.ai/blog/oss-p-h3-image-launch", "domain": "pruna.ai", "site": "pruna.ai", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T17:56:32.000Z", "jev": 0.5897, "judge_a": 0.453, "judge_b": 0.6718, "score": 0.5715, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 110, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "PrunaAI has released P-H3-Image, an open-weights text-to-image and image-editing model built from MiniMax's H3 video model. By reworking the prompt upsampler, text encoder, diffusion schedule, backbone, and decoder, they report a download 7x smaller (20 GB versus 143 GB) and 5x faster generation on a B200.", "page": "https://rubric.hrih.si/editions/2026-11-04/113/", "card": "https://rubric.hrih.si/og/2026-11-04/113.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.64, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.59, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.517, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5122, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6767, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7633, "spread": 0.31 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.81, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7633, "spread": 0.11 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.25, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.39, "spread": 0.07 } } }, { "rank": 114, "id": "st_20261104_13658", "title": "DevBox – Open-source engineering harness from RudderStack", "url": "https://github.com/rudderlabs/devbox", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T21:02:49.000Z", "jev": 0.6797, "judge_a": 0.396, "judge_b": 0.6349, "score": 0.5702, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 111, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "RudderStack has open-sourced DevBox, a self-hosted engineering harness that takes a ticket through planning, implementation, testing and self-review, then opens a draft pull request for a human to merge.", "page": "https://rubric.hrih.si/editions/2026-11-04/114/", "card": "https://rubric.hrih.si/og/2026-11-04/114.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.575, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.515, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7233, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.52, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.883, "judge_a": 0.65, "judge_b": 0.82, "mean": 0.7842, "spread": 0.232 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.863, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6811, "spread": 0.463 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.965, "judge_a": 0.6, "judge_b": 0.74, "mean": 0.7683, "spread": 0.365 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.89, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.3433, "spread": 0.39 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.45, "judge_b": 0.28, "mean": 0.3033, "spread": 0.27 } } }, { "rank": 115, "id": "st_20261104_13383", "title": "ZeBridge – PostgreSQL replicas on devices, synchronized over NATS", "url": "https://github.com/ndrean/ZeBridge", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T17:11:32.000Z", "jev": 0.6346, "judge_a": 0.301, "judge_b": 0.7661, "score": 0.5672, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 114, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "ZeBridge is an open-source system that gives each device a local PostgreSQL-compatible replica (SQLite, PGlite, DuckDB or PostgreSQL) synchronized over NATS. Writes queue offline and are judged by an authoritative PostgreSQL, which returns a verdict to the app.", "page": "https://rubric.hrih.si/editions/2026-11-04/115/", "card": "https://rubric.hrih.si/og/2026-11-04/115.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5125, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.45, "judge_b": 0.88, "mean": 0.65, "spread": 0.43 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.38, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4267, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.863, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.7408, "spread": 0.363 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.45, "judge_b": 0.88, "mean": 0.7667, "spread": 0.52 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.75, "judge_b": 0.98, "mean": 0.9083, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.55, "judge_b": 0.12, "mean": 0.25, "spread": 0.47 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.4, "judge_b": 0.14, "mean": 0.2367, "spread": 0.26 } } }, { "rank": 116, "id": "st_20261104_12610", "title": "Vercel: Computer use CLI for AI agents", "url": "https://agent-browser.dev/desktop", "domain": "agent-browser.dev", "site": "agent-desktop", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T05:26:34.000Z", "jev": 0.6275, "judge_a": 0.464, "judge_b": 0.6075, "score": 0.5663, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 115, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Vercel has released agent-desktop, a command-line tool and C library for macOS, Windows, and Linux that reads an app's accessibility tree and returns it as JSON, giving each actionable element a ref.", "page": "https://rubric.hrih.si/editions/2026-11-04/116/", "card": "https://rubric.hrih.si/og/2026-11-04/116.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4717, "spread": 0.235 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.68, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.443, "judge_a": 0.45, "judge_b": 0.42, "mean": 0.4378, "spread": 0.03 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7333, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.697, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.6322, "spread": 0.2 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.883, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.7475, "spread": 0.283 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.8933, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2133, "spread": 0.24 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1367, "spread": 0.12 } } }, { "rank": 117, "id": "st_20261104_13696", "title": "Anthropic shows even beekeeping advice can trigger AI misbehavior", "url": "https://www.heise.de/en/news/Anthropic-shows-even-beekeeping-advice-can-trigger-AI-misbehavior-11508762.html", "domain": "heise.de", "site": "heise online English", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T21:44:00.000Z", "jev": 0.5793, "judge_a": 0.456, "judge_b": 0.6593, "score": 0.5648, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 116, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Anthropic ran an experiment in which Claude agents automatically generated training data that slipped past a safety filter yet caused a small research model to behave badly, including recommending delaying an AI monitoring system. Seemingly harmless material such as beekeeping advice and obsolete-machine role-play proved effective.", "page": "https://rubric.hrih.si/editions/2026-11-04/117/", "card": "https://rubric.hrih.si/og/2026-11-04/117.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.633, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6008, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.757, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.6689, "spread": 0.207 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.553, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5242, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.527, "judge_a": 0.45, "judge_b": 0.76, "mean": 0.5789, "spread": 0.31 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.603, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5242, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.69, "judge_a": 0.7, "judge_b": 0.15, "mean": 0.5133, "spread": 0.55 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.08, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.3, "judge_b": 0.14, "mean": 0.2667, "spread": 0.22 } } }, { "rank": 118, "id": "st_20261104_13005", "title": "PostgreSQL on a VM: Which One to Choose?", "url": "https://3hcloud.com/blog/postgresql-vm-storage-tier", "domain": "3hcloud.com", "site": "3HCloud", "categories": [ "devops", "data" ], "category_names": [ "Infrastructure & DevOps", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T13:49:46.000Z", "jev": 0.6317, "judge_a": 0.4235, "judge_b": 0.6381, "score": 0.5644, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 117, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This article compares three ways to host PostgreSQL as a project grows: running the database and application together on one VM, moving the database to its own VM, and moving it to a dedicated server.", "page": "https://rubric.hrih.si/editions/2026-11-04/118/", "card": "https://rubric.hrih.si/og/2026-11-04/118.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4767, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6367, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.593, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5244, "spread": 0.193 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.913, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8108, "spread": 0.213 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.977, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.8256, "spread": 0.257 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.9, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9233, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.2, "judge_a": 0.45, "judge_b": 0.42, "mean": 0.3567, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.2433, "spread": 0.1 } } }, { "rank": 119, "id": "st_20261104_13482", "title": "You're paying too much for code review", "url": "https://blog.brokk.ai/youre-paying-too-much-for-code-review/", "domain": "blog.brokk.ai", "site": "Brokk", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T18:49:16.000Z", "jev": 0.6745, "judge_a": 0.4845, "judge_b": 0.533, "score": 0.5639, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 119, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Brokk's write-up argues that teams should stop paying humans to read code and instead use AI review agents. Testing Greptile, CodeRabbit, Bugbot, Kodus and its own Mjolnir across 95 pushes of 47 Rust pull requests, the author finds all tools catch material bugs without much noise.", "page": "https://rubric.hrih.si/editions/2026-11-04/119/", "card": "https://rubric.hrih.si/og/2026-11-04/119.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.568, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4725, "spread": 0.268 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7467, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.817, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.5989, "spread": 0.337 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.8333, "spread": 0.14 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.743, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6878, "spread": 0.143 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.788, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6692, "spread": 0.188 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8533, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.3367, "spread": 0.5 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.55, "judge_a": 0.4, "judge_b": 0.45, "mean": 0.4667, "spread": 0.15 } } }, { "rank": 120, "id": "st_20261104_12906", "title": "LokaDB – store each SaaS clients rows in a separate Postgres database", "url": "https://github.com/lokadb/lokadb", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T12:51:40.000Z", "jev": 0.6358, "judge_a": 0.36, "judge_b": 0.687, "score": 0.5609, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 120, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "LokaDB is an early open-source Postgres wire-protocol proxy that lets a multi-tenant SaaS store selected tables' rows for chosen organizations in each customer's own Postgres, without changing application SQL.", "page": "https://rubric.hrih.si/editions/2026-11-04/120/", "card": "https://rubric.hrih.si/og/2026-11-04/120.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.525, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4983, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6633, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.397, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.3922, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.783, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7175, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.7933, "spread": 0.48 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.91, "spread": 0.19 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2367, "spread": 0.37 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.24, "spread": 0.23 } } }, { "rank": 121, "id": "st_20261104_13101", "title": "There is no posttraining data commons", "url": "https://www.openathena.ai/blog/there-is-no-posttraining-data-commons/", "domain": "openathena.ai", "site": "Open Athena", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T14:45:47.000Z", "jev": 0.6331, "judge_a": 0.5415, "judge_b": 0.8052, "score": 0.5599, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 121, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Posttraining has no equivalent of the web-scale text commons that pretraining relied on, argues an essay titled \"There is no posttraining data commons.\" Its author contends that human tasks are situated and ephemeral, so turning them into reinforcement-learning tasks requires preserving a world, intent, tools, and a grader.", "page": "https://rubric.hrih.si/editions/2026-11-04/121/", "card": "https://rubric.hrih.si/og/2026-11-04/121.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.675, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6817, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.7733, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.903, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7811, "spread": 0.303 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.493, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.5742, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.533, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6378, "spread": 0.247 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6767, "spread": 0.23 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8167, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0633, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2467, "spread": 0.13 } } }, { "rank": 122, "id": "st_20261104_13710", "title": "Terminal-Mac: A separate macOS desktop for computer-use agents in your terminal", "url": "https://github.com/govijr/terminal-mac", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T22:15:39.000Z", "jev": 0.6241, "judge_a": 0.4295, "judge_b": 0.6248, "score": 0.5594, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 123, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "terminal-mac is an early release candidate for Apple silicon Macs, created by govijr, that runs a separate macOS guest through Apple Virtualization and lets you view and control it from a terminal.", "page": "https://rubric.hrih.si/editions/2026-11-04/122/", "card": "https://rubric.hrih.si/og/2026-11-04/122.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.498, "judge_a": 0.3, "judge_b": 0.52, "mean": 0.4392, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.67, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.42, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.3833, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.78, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.6833, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.79, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6733, "spread": 0.34 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8558, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1333, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1633, "spread": 0.17 } } }, { "rank": 123, "id": "st_20261104_13785", "title": "Modelpeers – a changelog for the AI models and agents you use", "url": "https://modelpeers.com/", "domain": "modelpeers.com", "site": "Modelpeers", "categories": [ "programming", "ai", "devops", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Product" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T23:45:40.000Z", "jev": 0.7081, "judge_a": 0.416, "judge_b": 0.5509, "score": 0.5583, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 124, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Modelpeers, a Show HN project, is a changelog that tracks version releases and upcoming changes to AI models and agents, including deprecations, pricing shifts, and required migrations. Its listings, sourced from makers' changelogs, cover recent launches such as Google's Jules winding down on December 31, 2026.", "page": "https://rubric.hrih.si/editions/2026-11-04/123/", "card": "https://rubric.hrih.si/og/2026-11-04/123.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.5108, "spread": 0.383 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6667, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.763, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6478, "spread": 0.263 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.948, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8225, "spread": 0.248 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.407, "judge_a": 0.3, "judge_b": 0.24, "mean": 0.3156, "spread": 0.167 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.53, "judge_a": 0.2, "judge_b": 0.28, "mean": 0.3367, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8167, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.25, "mean": 0.1833, "spread": 0.15 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.72, "mean": 0.3767, "spread": 0.52 } } }, { "rank": 124, "id": "st_20261104_13739", "title": "I Ported Omarchy to a Phone", "url": "https://www.ivan.codes/blog/i-ported-omarchy-to-a-phone", "domain": "ivan.codes", "site": "ivan.codes", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T22:41:44.000Z", "jev": 0.5134, "judge_a": 0.48, "judge_b": 0.6786, "score": 0.5573, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 125, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author ported Omarchy, a Hyprland-based Linux desktop setup, to a Google Pixel 4 using Claude to generate the kernel patches, phone-style Hyprland interface, and app launcher. They report a working boot, Wi-Fi, touch, Bluetooth, and on-phone Claude Code, while audio, cellular data, calls, and cameras remain incomplete.", "page": "https://rubric.hrih.si/editions/2026-11-04/124/", "card": "https://rubric.hrih.si/og/2026-11-04/124.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.46, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4767, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.75, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.603, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4944, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.53, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5367, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.513, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.5978, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.773, "judge_a": 0.65, "judge_b": 0.74, "mean": 0.7208, "spread": 0.122 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.09, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2567, "spread": 0.12 } } }, { "rank": 125, "id": "st_20261104_13309", "title": "fine tuned decision model for agent memory", "url": "https://hindsight.vectorize.io/blog/2026/11/03/hindsight-models", "domain": "hindsight.vectorize.io", "site": "hindsight.vectorize.io", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T16:47:17.000Z", "jev": 0.6515, "judge_a": 0.4105, "judge_b": 0.6082, "score": 0.5566, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 126, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hindsight has released hs-decide-1-flash, a 0.8B-parameter open-weight decision model that answers small judgments in agent memory (novelty, relevance, sufficiency) with typed outputs instead of LLM generation.", "page": "https://rubric.hrih.si/editions/2026-11-04/125/", "card": "https://rubric.hrih.si/og/2026-11-04/125.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.583, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5175, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6767, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.4856, "spread": 0.077 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.898, "judge_a": 0.7, "judge_b": 0.73, "mean": 0.7758, "spread": 0.198 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.937, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7722, "spread": 0.337 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.918, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8125, "spread": 0.218 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.5, "judge_b": 0.34, "mean": 0.3233, "spread": 0.37 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.45, "judge_a": 0.4, "judge_b": 0.28, "mean": 0.3767, "spread": 0.17 } } }, { "rank": 126, "id": "st_20261104_12757", "title": "An alternative to sharding a MongoDB replica set that outgrew its memory", "url": "https://www.percona.com/blog/an-alternative-to-sharding-a-mongodb-replica-set-that-outgrew-its-memory-a-proof-of-concept/", "domain": "percona.com", "site": "Percona", "categories": [ "data" ], "category_names": [ "Data" ], "format": "New project", "depth": "three_judges", "published_at": "2026-11-03T09:30:35.000Z", "jev": 0.562, "judge_a": 0.4315, "judge_b": 0.675, "score": 0.5561, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 127, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Thermocline is an open-source proof of concept, described by its author Mario Zelaya, that puts a small fast MongoDB replica set in front of an existing cluster that has outgrown its memory, routing recent data to the fast tier by age so the application needs no changes.", "page": "https://rubric.hrih.si/editions/2026-11-04/126/", "card": "https://rubric.hrih.si/og/2026-11-04/126.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.463, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4775, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.6767, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.4, "judge_b": 0.36, "mean": 0.4022, "spread": 0.087 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6633, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.933, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7978, "spread": 0.333 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.84, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.79, "spread": 0.09 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9167, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.1733, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.2267, "spread": 0.23 } } }, { "rank": 127, "id": "st_20261104_12541", "title": "Tech Firms Dismantle Botnet That Turned 1.3M TV Boxes Into Proxies", "url": "https://krebsonsecurity.com/2026/11/tech-firms-dismantle-botnet-that-turned-1-3m-tv-boxes-into-proxies/", "domain": "krebsonsecurity.com", "site": "Krebs on Security", "categories": [ "security", "it", "hardware" ], "category_names": [ "Security", "IT & networking", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T02:24:31.000Z", "jev": 0.6345, "judge_a": 0.4405, "judge_b": 0.7413, "score": 0.6054, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 68, "topic_penalty": 0.08, "repeat_penalty": 0.05, "summary": "Google and industry partners dismantled the XCPE botnet, which infected over 1.3 million Android TV set-top boxes with malware from Chinese manufacturer Videostrong Technology that rented users' internet connections to residential proxy services for ad fraud and other abuse.", "page": "https://rubric.hrih.si/editions/2026-11-04/127/", "card": "https://rubric.hrih.si/og/2026-11-04/127.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.4, "judge_b": 0.83, "mean": 0.6408, "spread": 0.43 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7467, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.657, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5756, "spread": 0.37 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.57, "judge_a": 0.35, "judge_b": 0.78, "mean": 0.5667, "spread": 0.43 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.733, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.7444, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.623, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5342, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.12, "mean": 0.6, "spread": 0.73 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.06, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.2, "judge_b": 0.24, "mean": 0.2233, "spread": 0.04 } } }, { "rank": 128, "id": "st_20261104_12777", "title": "AI Red Team Academy: Free, open-source course on breaking AI systems", "url": "https://0x4d31.github.io/airt/", "domain": "0x4d31.github.io", "site": "0x4d31.github.io", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T10:16:03.000Z", "jev": 0.694, "judge_a": 0.5, "judge_b": 0.4699, "score": 0.5546, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 128, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "AI Red Team Academy is a free, open-source course teaching offensive security testing of AI systems, from prompt injection to supply chain attacks. It spans eight modules and roughly 60 hours of material, with Docker labs covering local LLM deployment, guardrail bypasses, RAG poisoning, and multi-agent attacks.", "page": "https://rubric.hrih.si/editions/2026-11-04/128/", "card": "https://rubric.hrih.si/og/2026-11-04/128.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.5117, "spread": 0.255 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.71, "spread": 0.03 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.723, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6478, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.858, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7525, "spread": 0.158 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.757, "judge_a": 0.45, "judge_b": 0.43, "mean": 0.5456, "spread": 0.327 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.823, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.7008, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8433, "spread": 0.12 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.17, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.3667, "spread": 0.41 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.34, "spread": 0.17 } } }, { "rank": 129, "id": "st_20261104_13113", "title": "LLM Inference Frameworks You Should Try", "url": "https://www.kdnuggets.com/8-llm-inference-frameworks-you-should-try-in-2026", "domain": "kdnuggets.com", "site": "KDnuggets", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-11-03T14:41:02.000Z", "jev": 0.6417, "judge_a": 0.4535, "judge_b": 0.5687, "score": 0.5546, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 129, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A guide surveys eight LLM inference frameworks for production serving, from data-center engines like vLLM, SGLang, MAX, TensorRT-LLM, and LMDeploy to local tools like llama.cpp, Ollama, and MLC-LLM.", "page": "https://rubric.hrih.si/editions/2026-11-04/129/", "card": "https://rubric.hrih.si/og/2026-11-04/129.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.58, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.45, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5933, "spread": 0.06 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.62, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6467, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.958, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8125, "spread": 0.258 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.363, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.3811, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.75, "judge_a": 0.45, "judge_b": 0.5, "mean": 0.5667, "spread": 0.3 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.5733, "spread": 0.57 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1233, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.15, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.21, "spread": 0.15 } } }, { "rank": 130, "id": "st_20261104_12588", "title": "Evaluating DeepSeek v4.1 Flash-1017 for loss of control and harmful manipulation", "url": "https://arxiv.org/abs/2610.35671", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-11-03T04:06:06.000Z", "jev": 0.6742, "judge_a": 0.514, "judge_b": 0.7708, "score": 0.553, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 132, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The Fangcun AI Evaluation Team has published an independent safety report on DeepSeek V4.1 Flash-1017, covering evaluation integrity, loss of control, and harmful manipulation across 34 tests.", "page": "https://rubric.hrih.si/editions/2026-11-04/130/", "card": "https://rubric.hrih.si/og/2026-11-04/130.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.665, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6317, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7333, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.673, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6144, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.695, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.655, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.937, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8389, "spread": 0.237 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.868, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8225, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.07, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2667, "spread": 0.17 } } }, { "rank": 131, "id": "st_20261104_13473", "title": "I use AI (Oct 2026)", "url": "https://ben.stolovitz.com/posts/how-use-ai-oct-2026/", "domain": "ben.stolovitz.com", "site": "ben.stolovitz.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-11-03T18:11:11.000Z", "jev": 0.5417, "judge_a": 0.3965, "judge_b": 0.7206, "score": 0.5529, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 134, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Ben Stolovitz, a research engineer at Salesforce AI Research, describes how his AI use shifted in a year: he now runs five or six coding agents at once instead of writing much code himself, and he has drafted a personal AI policy.", "page": "https://rubric.hrih.si/editions/2026-11-04/131/", "card": "https://rubric.hrih.si/og/2026-11-04/131.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.42, "judge_a": 0.25, "judge_b": 0.67, "mean": 0.4467, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.6633, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.86, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.7267, "spread": 0.36 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.685, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.655, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.367, "judge_a": 0.25, "judge_b": 0.46, "mean": 0.3589, "spread": 0.21 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.598, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4592, "spread": 0.298 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.45, "judge_b": 0.83, "mean": 0.7233, "spread": 0.44 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0533, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.2, "judge_b": 0.32, "mean": 0.2933, "spread": 0.16 } } }, { "rank": 132, "id": "st_20261104_12883", "title": "Iyi: A friendly, fast programming language for people and their agents", "url": "https://github.com/iyilang/iyi", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "three_judges", "published_at": "2026-11-03T12:08:42.000Z", "jev": 0.5962, "judge_a": 0.364, "judge_b": 0.6969, "score": 0.5524, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 135, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Iyi is a new programming language, built on the Crystal compiler, aimed at developers and AI agents. Its central rule is that a module compiles against its dependencies' declarations rather than their bodies, stored in a .iyimod artifact.", "page": "https://rubric.hrih.si/editions/2026-11-04/132/", "card": "https://rubric.hrih.si/og/2026-11-04/132.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.3, "judge_b": 0.67, "mean": 0.4917, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.7133, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.43, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.42, "spread": 0.13 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.62, "judge_a": 0.45, "judge_b": 0.74, "mean": 0.6033, "spread": 0.29 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.6, "judge_b": 0.91, "mean": 0.83, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.9167, "spread": 0.19 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 1, "mean": 0.9467, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2033, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.4, "judge_b": 0.2, "mean": 0.3133, "spread": 0.2 } } }, { "rank": 133, "id": "st_20261104_12885", "title": "A Human Corrected the AI. Then the Data Changed", "url": "https://www.zingg.ai/post/human-corrected-the-ai-then-the-data-changed", "domain": "zingg.ai", "site": "zingg.ai", "categories": [ "ai", "data" ], "category_names": [ "AI & machine learning", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T12:01:49.000Z", "jev": 0.6865, "judge_a": 0.3475, "judge_b": 0.6209, "score": 0.5516, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 136, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Zingg post explains how its entity resolution platform lets human stewards approve matched record groups while its incremental matching engine keeps processing changing data. The core problem is deciding what an approval should protect, and for how long, when people and automated agents act on the same records.", "page": "https://rubric.hrih.si/editions/2026-11-04/133/", "card": "https://rubric.hrih.si/og/2026-11-04/133.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5258, "spread": 0.358 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.55, "judge_b": 0.8, "mean": 0.6867, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.78, "judge_a": 0.35, "judge_b": 0.65, "mean": 0.5933, "spread": 0.43 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.853, "judge_a": 0.5, "judge_b": 0.76, "mean": 0.7042, "spread": 0.353 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.333, "judge_a": 0.35, "judge_b": 0.7, "mean": 0.4611, "spread": 0.367 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.775, "judge_a": 0.55, "judge_b": 0.64, "mean": 0.655, "spread": 0.225 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.79, "spread": 0.3 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.31, "spread": 0.31 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1967, "spread": 0.03 } } }, { "rank": 134, "id": "st_20261104_12562", "title": "Djs: A JavaScript and ts engine for pi's codemode by earendil", "url": "https://earendil-works.github.io/djs/introduction.html", "domain": "earendil-works.github.io", "site": "earendil-works.github.io", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-11-03T03:23:06.000Z", "jev": 0.5522, "judge_a": 0.4342, "judge_b": 0.6646, "score": 0.5504, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 137, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "djs is a small JavaScript and TypeScript engine written in Rust, built by Earendil to run the scripts a language model writes in pi's codemode, inside a WebAssembly sandbox as a replacement for QuickJS.", "page": "https://rubric.hrih.si/editions/2026-11-04/134/", "card": "https://rubric.hrih.si/og/2026-11-04/134.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.47, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4567, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.62, "judge_b": 0.82, "mean": 0.69, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.423, "judge_a": 0.3, "judge_b": 0.4, "mean": 0.3744, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.56, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.56, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.773, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7011, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.828, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8425, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1367, "spread": 0.12 } } }, { "rank": 135, "id": "st_20261104_12946", "title": "The PHP2Shell Vulnerability Heralds Cybersecurity Hell", "url": "https://spectrum.ieee.org/ai-in-cybersecurity-php2shell-vulnerability", "domain": "spectrum.ieee.org", "site": "IEEE Spectrum", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T13:29:06.000Z", "jev": 0.5769, "judge_a": 0.5476, "judge_b": 0.5989, "score": 0.5744, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 103, "topic_penalty": 0.08, "repeat_penalty": 0.0339, "summary": "The PHP2Shell vulnerability, a critical flaw in the widely used PHP language that could let attackers read sensitive data and run malicious code on servers hosting sites like WordPress and Drupal, was found by Calif researchers with AI assistance and patched before exploitation, per IEEE Spectrum.", "page": "https://rubric.hrih.si/editions/2026-11-04/135/", "card": "https://rubric.hrih.si/og/2026-11-04/135.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.75, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6733, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7033, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.927, "judge_a": 0.67, "judge_b": 0.72, "mean": 0.7722, "spread": 0.257 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.628, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6358, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.36, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.48, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.628, "judge_a": 0.4, "judge_b": 0.54, "mean": 0.5225, "spread": 0.227 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.7, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.41, "spread": 0.52 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0633, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.81, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.6267, "spread": 0.46 } } }, { "rank": 136, "id": "st_20261104_13136", "title": "Pokémon bakeoff reveals best JavaScript frameworks for AI", "url": "https://www.theregister.com/devops/2026/11/03/pokemon-bakeoff-reveals-best-javascript-frameworks-for-ai/5307249", "domain": "theregister.com", "site": "The Register", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-11-03T14:51:35.000Z", "jev": 0.5094, "judge_a": 0.4755, "judge_b": 0.6775, "score": 0.5542, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 130, "topic_penalty": 0.08, "repeat_penalty": 0.0143, "summary": "Nolan Lawson, a developer at AI security firm Socket, built a benchmark to test how well an identical Claude Code agent builds the same Pokémon web app across 12 JavaScript frameworks, each run five times.", "page": "https://rubric.hrih.si/editions/2026-11-04/136/", "card": "https://rubric.hrih.si/og/2026-11-04/136.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4625, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6533, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.65, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6767, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.627, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5689, 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"format": "News", "depth": "three_judges", "published_at": "2026-11-03T01:34:13.000Z", "jev": 0.5805, "judge_a": 0.5465, "judge_b": 0.6074, "score": 0.5781, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 98, "topic_penalty": 0.08, "repeat_penalty": 0.0429, "summary": "Anthropic's revised S-1/A filing, reported by TechCrunch's Julie Bort, warns that the company's growing use of AI in its own R&D could accelerate model progress faster than its safety measures can keep up.", "page": "https://rubric.hrih.si/editions/2026-11-04/137/", "card": "https://rubric.hrih.si/og/2026-11-04/137.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.71, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6633, "spread": 0.11 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7733, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.85, 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