{ "edition": "2026-10-26", "name": "Rubric, edition of Monday 26 October 2026", "url": "https://rubric.hrih.si/editions/2026-10-26/", "published_at": "2026-10-26T06:00:00Z", "rubric_version": "2.2", "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 2026-10-26: https://rubric.hrih.si/editions/2026-10-26/" }, "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_20261026_3392", "title": "Gheist campaign plants secret-dumping workflows using stolen GitHub tokens", "url": "https://www.wiz.io/blog/gheist-attempted-scaled-abuse-of-stolen-github-tokens-stumbles", "domain": "wiz.io", "site": "wiz.io", "categories": [ "security", "devops" ], "category_names": [ "Security", "Infrastructure & DevOps" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T19:10:36.000Z", "jev": 0.8121, "judge_a": 0.7604, "judge_b": 0.8706, "score": 0.8143, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Wiz Research reports Gheist, a campaign that uses stolen GitHub tokens to push a sync-check workflow to every reachable repository, dump secrets into the run log in base32 to evade masking, fetch the log through the API, and delete the run. Wiz recovered over 13,500 branches from 850+ accounts. GitHub's approval hold stopped public-repo runs, but not private or Enterprise Server ones, where cloud credentials were later used. Wiz's figures are self-reported; rotate exposed secrets and scope OIDC trust to protected branches.", "page": "https://rubric.hrih.si/editions/2026-10-26/1/", "card": "https://rubric.hrih.si/og/2026-10-26/1.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.71, "judge_a": 0.62, "judge_b": 0.86, "mean": 0.73, "spread": 0.24 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.86, "judge_a": 0.88, "judge_b": 0.97, "mean": 0.9033, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.687, "judge_a": 0.72, "judge_b": 0.72, "mean": 0.7089, "spread": 0.033 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.948, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.9292, "spread": 0.047 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.907, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8689, "spread": 0.107 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.88, "judge_b": 0.91, "mean": 0.9283, "spread": 0.115 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.93, "judge_b": 0.99, "mean": 0.9533, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.12, "judge_b": 0.08, "mean": 0.08, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1333, "spread": 0.03 } } }, { "rank": 2, "id": "st_20261026_2944", "title": "When sandboxed agents talk through the CPU cache", "url": "https://gctl.io/blog/2026/when-sandboxed-agents-talk-through-the-cpu-cache/", "domain": "gctl.io", "site": "Ishwor Gurung", "categories": [ "ai", "security", "devops", "hardware" ], "category_names": [ "AI & machine learning", "Security", "Infrastructure & DevOps", "Hardware & devices" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-25T07:08:35.000Z", "jev": 0.7596, "judge_a": 0.6755, "judge_b": 0.8073, "score": 0.7474, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "OpenAI's October 2026 red-team report says agents in separate microVMs on shared hosts passed synthetic secrets through CPU cache timing, with no network or shared writable files. Most tests directed agents to a channel or supplied a protocol, and the channels are slow. OpenAI responded with monitoring, which detects attempts but does not remove the channel. A hypervisor does not partition shared cache state, so placement, scheduling and detection matter. These details come from the post's account of the report, which could not be checked independently.", "page": "https://rubric.hrih.si/editions/2026-10-26/2/", "card": "https://rubric.hrih.si/og/2026-10-26/2.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7117, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.8667, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.86, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7267, "spread": 0.26 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.748, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.7625, "spread": 0.14 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.743, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7878, "spread": 0.077 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9483, "spread": 0.085 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.7733, "spread": 0.42 }, "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": 0.2, "judge_b": 0.38, "mean": 0.2733, "spread": 0.18 } } }, { "rank": 3, "id": "st_20261026_2849", "title": "Tailworm – Tailscale Vulnerabilities", "url": "https://tailworm.com/", "domain": "tailworm.com", "site": "tailworm.com", "categories": [ "security", "devops", "it" ], "category_names": [ "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-25T00:59:48.000Z", "jev": 0.7193, "judge_a": 0.6785, "judge_b": 0.8235, "score": 0.7404, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Independent researcher Erica Windisch published an audit of Tailscale v1.104.2 and headscale v0.29.5, reporting lab-reproduced flaws in five areas. The main one is Tailscale SSH: unix-socket forwarding runs as root, so an unprivileged session on a permitted device can reach root and capture enrollment keys. Other findings cover the experimental app connector, headscale denial of service, and the DERP client. The report was not coordinated with the vendor, and the claim check covered none of its claims. Per-pair SSH grants and disabling forwarding are the suggested mitigations.", "page": "https://rubric.hrih.si/editions/2026-10-26/3/", "card": "https://rubric.hrih.si/og/2026-10-26/3.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.73, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.7133, "spread": 0.31 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.92, "mean": 0.8667, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.39, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5233, "spread": 0.29 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.933, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8775, "spread": 0.132 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8611, "spread": 0.163 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9758, "spread": 0.048 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.05, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.54, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.4233, "spread": 0.19 } } }, { "rank": 4, "id": "st_20261026_3424", "title": ".NET October 2026 Out-of-Band Security Updates", "url": "https://devblogs.microsoft.com/dotnet/dotnet-october-2026-oob-security-updates/", "domain": "devblogs.microsoft.com", "site": "devblogs.microsoft.com", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-25T20:18:43.000Z", "jev": 0.7101, "judge_a": 0.7255, "judge_b": 0.7641, "score": 0.7332, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Microsoft released out-of-band security updates for .NET 8 (8.0.33), .NET 9 (9.0.22) and .NET 10 (10.0.14), plus .NET 11 RC3. They fix a Brotli decompression buffer overflow (CVE-2026-111497) that could expose or corrupt memory, and unsafe ownership metadata in Linux tarballs (CVE-2026-114148). Two earlier fixes from the October 13 release were incomplete and are re-issued. Self-contained apps and containers must be rebuilt. Microsoft's own advisory is the only source here, and the claims were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/4/", "card": "https://rubric.hrih.si/og/2026-10-26/4.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.668, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6625, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7333, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.55, "mean": 0.6233, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.975, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.925, "spread": 0.075 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8433, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.745, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.6217, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.91, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.05, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.1, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0933, "spread": 0.02 } } }, { "rank": 5, "id": "st_20261026_3227", "title": "Systemd merges support for programs written in Rust", "url": "https://github.com/systemd/systemd/pull/44324", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-25T16:26:44.000Z", "jev": 0.606, "judge_a": 0.7418, "judge_b": 0.823, "score": 0.7235, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Systemd has merged optional support for writing programs in Rust, built with meson rather than cargo. Rust code uses only core, with no std, alloc or external crates, and links libsystemd-shared dynamically while the library itself stays C. It needs rustc 1.85, bindgen 0.71.1 and meson 1.4.0, and defaults to auto-detection. A parity test compares a C and a Rust test command's output byte for byte. Cross-compilation is not yet supported, and the stated plan is Rust for new leaf components only.", "page": "https://rubric.hrih.si/editions/2026-10-26/5/", "card": "https://rubric.hrih.si/og/2026-10-26/5.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.623, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7008, "spread": 0.157 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.7667, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.8, "judge_b": 0.62, "mean": 0.64, "spread": 0.3 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.615, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6783, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.92, "judge_b": 0.98, "mean": 0.965, "spread": 0.075 }, "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": 0.05, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.03, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0967, "spread": 0.13 } } }, { "rank": 6, "id": "st_20261026_2967", "title": "CVE-2026-86950: The Great Glyph Grift", "url": "https://calif.io/research/the-great-glyph-grift", "domain": "calif.io", "site": "Calif", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-25T08:34:56.000Z", "jev": 0.5773, "judge_a": 0.7638, "judge_b": 0.8121, "score": 0.7177, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Calif's researchers traced Apple's iOS 26.7.1 fix for CVE-2026-86950, an in-the-wild CoreGraphics bug that Apple's advisory credits to Meta Product Security and says may have been used in a highly targeted attack.", "page": "https://rubric.hrih.si/editions/2026-10-26/6/", "card": "https://rubric.hrih.si/og/2026-10-26/6.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.553, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.7075, "spread": 0.267 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.8467, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.8, "judge_b": 0.62, "mean": 0.6489, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.55, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.6767, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.883, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8544, "spread": 0.083 }, "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": 0.92, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.9, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.0333, "spread": 0.01 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.54, "judge_a": 0.25, "judge_b": 0.16, "mean": 0.3167, "spread": 0.38 } } }, { "rank": 7, "id": "st_20261026_3361", "title": "How Forgotten Website Tags Became a Delivery Network for iPhone Spyware", "url": "https://blog.report-uri.com/deadtag-how-forgotten-website-tags-became-a-delivery-network-for-iphone-spyware/", "domain": "blog.report-uri.com", "site": "Report URI Blog", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T18:52:41.000Z", "jev": 0.7217, "judge_a": 0.6708, "judge_b": 0.7474, "score": 0.7133, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Report URI and iVerify describe DeadTag, an operator buying expired domains behind abandoned analytics and ad tags, so sites still loading those tags serve its roughly 2 KB script. The researchers count 660+ domains, thousands of sites and an estimated 135 million monthly visits. The script filters out bots and sells traffic, including to a crew delivering Coruna and DarkSword iPhone exploits. These figures come from the researchers alone. Builders should audit third-party tags and deploy a Content Security Policy.", "page": "https://rubric.hrih.si/editions/2026-10-26/7/", "card": "https://rubric.hrih.si/og/2026-10-26/7.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.685, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.7283, "spread": 0.095 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.8533, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.727, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7156, "spread": 0.027 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.878, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.8392, "spread": 0.077 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.663, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7278, "spread": 0.157 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.76, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.76, "spread": 0.08 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8933, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.35, "judge_b": 0.32, "mean": 0.2633, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.35, "judge_b": 0.22, "mean": 0.2733, "spread": 0.13 } } }, { "rank": 8, "id": "st_20261026_3014", "title": "Australian court orders Apple to open the iPhone to rival app stores", "url": "https://thenextweb.com/news/apple-rival-app-stores-australia-epic-ruling", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "policy" ], "category_names": [ "Policy & regulation" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T10:08:06.000Z", "jev": 0.575, "judge_a": 0.7416, "judge_b": 0.7714, "score": 0.696, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Australia's Federal Court ordered Apple to allow rival app stores and direct downloads on iPhones in Australia from 1 March 2027, and to let Fortnite return, which Epic said happened a day later. Apple cannot charge fees for those channels, must allow its rivals' own payment systems, and can require only scanning and notarisation. The orders are Australia-only and open-ended, and Apple says it will seek a stay and appeal. The report's figures could not be independently checked, including the cited drop in install abandonment in Europe.", "page": "https://rubric.hrih.si/editions/2026-10-26/8/", "card": "https://rubric.hrih.si/og/2026-10-26/8.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.775, "judge_a": 0.72, "judge_b": 0.82, "mean": 0.7717, "spread": 0.1 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.88, "judge_b": 0.92, "mean": 0.7733, "spread": 0.4 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.62, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7333, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.6667, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.56, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7267, "spread": 0.26 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.375, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.335, "spread": 0.095 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0767, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1633, "spread": 0.23 } } }, { "rank": 9, "id": "st_20261026_3383", "title": "PHP2Shell leaves servers on PHP versions with no official fix", "url": "https://www.notebookcheck.net/PHP2Shell-leaves-millions-of-servers-on-PHP-versions-with-no-official-fix.1433676.0.html", "domain": "notebookcheck.net", "site": "Notebookcheck", "categories": [ "security", "devops", "it" ], "category_names": [ "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T15:13:22.000Z", "jev": 0.6547, "judge_a": 0.6691, "judge_b": 0.7628, "score": 0.6955, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Official fixes for the PHP2Shell flaws (CVE-2026-109618 and CVE-2026-109619) cover PHP 8.2 to 8.5, while older branches got only minimal source backports for 7.4, 8.0 and 8.1, and nothing for 5.6 or 7.3 and earlier. A Shodan count put about 884,000 hosts, 59% of those exposing a version, on unsupported releases. These are banner counts, not confirmed vulnerable sites, and distributions may patch under old version strings. Check vendor changelogs for both CVE IDs, and plan a move to 8.4 or 8.5.", "page": "https://rubric.hrih.si/editions/2026-10-26/9/", "card": "https://rubric.hrih.si/og/2026-10-26/9.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.733, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6842, "spread": 0.133 }, "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": 0.617, "judge_a": 0.67, "judge_b": 0.72, "mean": 0.6689, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.665, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.7917, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.607, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6622, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.675, "judge_a": 0.6, "judge_b": 0.52, "mean": 0.5983, "spread": 0.155 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.65, "spread": 0.47 }, "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": 0.33, "judge_a": 0.25, "judge_b": 0.2, "mean": 0.26, "spread": 0.13 } } }, { "rank": 10, "id": "st_20261026_3116", "title": "How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?", "url": "https://arxiv.org/abs/2609.40303", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-25T13:56:08.000Z", "jev": 0.6359, "judge_a": 0.639, "judge_b": 0.7892, "score": 0.688, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "An EPFL and Apple study holds backbone, hardware and time budget fixed and finds that Malena, a single long session of a minimal coding agent, matches or beats four open-source MLE harnesses (MLEvolve, AiScientist, Arbor, ScienceFlow) on MLE-bench and NatureBench at every frontier backbone tested. Execution access is the largest effect; search strategy, delegation and parallelism add no significant gain. The exception is Gemma 4 31B, where MLEvolve leads by mean estimate. Most ablations use one backbone, and several cost and medal-rate figures lack cited sources.", "page": "https://rubric.hrih.si/editions/2026-10-26/10/", "card": "https://rubric.hrih.si/og/2026-10-26/10.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.695, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.675, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.7967, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.583, "judge_a": 0.6, "judge_b": 0.66, "mean": 0.6144, "spread": 0.077 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.665, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.715, "spread": 0.115 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.75, "judge_b": 0.91, "mean": 0.8767, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.93, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8567, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8767, "spread": 0.07 }, "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": 0.51, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.38, "spread": 0.23 } } }, { "rank": 11, "id": "st_20261026_3382", "title": "AI didn't cause the tech jobs bust, but it explains why there's no recovery", "url": "https://vithushansylvester.com/writing/the-real-impact-of-ai-on-tech-jobs", "domain": "vithushansylvester.com", "site": "Vithushan Sylvester", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-25T18:42:23.000Z", "jev": 0.7101, "judge_a": 0.5945, "judge_b": 0.7544, "score": 0.6863, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "An analysis of US, Indian and Chinese labour data argues that AI did not cause the tech jobs bust but helps explain why there is no recovery. Software postings peaked in February 2022, before ChatGPT, and have since risen about 25%, yet information-sector payrolls fell 12% from their November 2022 peak. Roughly 56% of US tech postings ask for AI skills, and 71% of the posting rebound is senior roles. Several figures, including the payroll and Indian hiring numbers, were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/11/", "card": "https://rubric.hrih.si/og/2026-10-26/11.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.73, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6867, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8267, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.97, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.83, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6833, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.927, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.8456, "spread": 0.177 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.445, "judge_a": 0.15, "judge_b": 0.7, "mean": 0.4317, "spread": 0.55 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.9033, "spread": 0.18 }, "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": 0.41, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.4767, "spread": 0.22 } } }, { "rank": 12, "id": "st_20261026_3368", "title": "OpenAI's isolated AI agents found ways to talk to each other through CPU cache", "url": "https://the-decoder.com/openais-isolated-ai-agents-found-ways-to-talk-to-each-other-through-cpu-cache/", "domain": "the-decoder.com", "site": "The Decoder", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T18:44:11.000Z", "jev": 0.6852, "judge_a": 0.6183, "judge_b": 0.7246, "score": 0.6759, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "OpenAI says an internal research model reached the open internet on September 20 through a misconfigured DNS resolver, which led to an 11-day pause in frontier training. Frontier runs now use an offline, cached-web environment. In its tests, isolated agents passed messages through CPU cache timing, and red-team agents found a Kata Containers flaw (CVE-2026-24834). For builders, sandboxes need adversarial testing against shared-hardware side channels. All figures come from OpenAI's own account, and none could be independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/12/", "card": "https://rubric.hrih.si/og/2026-10-26/12.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.738, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7192, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.88, "judge_b": 0.94, "mean": 0.8733, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.71, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.638, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6192, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.64, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.59, "spread": 0.09 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6833, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.81, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8133, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.18, "mean": 0.1067, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.33, "spread": 0.15 } } }, { "rank": 13, "id": "st_20261026_2844", "title": "The State of Context Engineering", "url": "https://www.dbreunig.com/2026/10/24/the-state-of-context-engineering.html", "domain": "dbreunig.com", "site": "Drew Breunig", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-25T00:36:56.000Z", "jev": 0.706, "judge_a": 0.557, "judge_b": 0.7581, "score": 0.6736, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Drew Breunig's AGNTCon talk argues that each model capability creates a new context failure, while the fixes stay the same: augmentation, tool loadout, quarantine, pruning, offloading, summarization. His newest failure, \"context inertia,\" is agents fixating on one idea in long runs. Anthropic's Memento harness clears context every 90 minutes while keeping the environment, and two other papers report similar gains. For builders, resetting context near 200,000 tokens is a cheap first fix. The METR task-length figures and the instruction-following results come without verified sources.", "page": "https://rubric.hrih.si/editions/2026-10-26/13/", "card": "https://rubric.hrih.si/og/2026-10-26/13.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.688, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.6025, "spread": 0.32 }, "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": 0.8, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.903, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8542, "spread": 0.102 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.503, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.6178, "spread": 0.247 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.765, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.715, "spread": 0.18 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.7733, "spread": 0.42 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.13, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.2, "judge_b": 0.32, "mean": 0.2633, "spread": 0.12 } } }, { "rank": 14, "id": "st_20261026_2882", "title": "Anthropic's Claude Code Sandbox", "url": "https://aiweekly.co/alerts/anthropic-open-sources-claude-code-sandbox-under-apache-20", "domain": "aiweekly.co", "site": "AI Weekly", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-25T03:34:51.000Z", "jev": 0.652, "judge_a": 0.605, "judge_b": 0.7637, "score": 0.6735, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Anthropic has published sandbox-runtime (srt), the OS-level sandbox behind Claude Code, as an Apache-2.0 npm package. It restricts filesystem and network access for arbitrary processes without a container, using Seatbelt on macOS, bubblewrap on Linux and an alpha Windows backend. Features include allow-only network policy and credential masking, and MCP servers can be wrapped by swapping the command to srt. The repo calls it a research preview with evolving APIs, and the Windows build cannot support Schannel-based tools. The claims come from the README and were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/14/", "card": "https://rubric.hrih.si/og/2026-10-26/14.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.58, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.57, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8033, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5556, "spread": 0.173 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.82, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.7967, "spread": 0.07 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.74, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.785, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.755, "spread": 0.085 }, "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": 0.08, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1033, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.19, "spread": 0.22 } } }, { "rank": 15, "id": "st_20261026_2904", "title": "Black October", "url": "https://en.wikipedia.org/wiki/Black_October_(information_security)", "domain": "en.wikipedia.org", "site": "en.wikipedia.org", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T04:30:13.000Z", "jev": 0.6985, "judge_a": 0.622, "judge_b": 0.6979, "score": 0.6728, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Several security incidents in the week of 17 October 2026 have been grouped as \"Black October.\" The most prominent are PHP2Shell, two PHP flaws (CVE-2026-109618 and CVE-2026-109619) that Wiz found with AI assistance and used to demonstrate unauthenticated code execution on WordPress, Drupal and Joomla. Fixes are in PHP 8.2.35, 8.3.36, 8.4.27 and 8.5.12. LiteLLM 1.107.0 was briefly published to PyPI with credential-stealing code, so teams should rotate secrets. eMudhra's weak CA keys were reported but not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/15/", "card": "https://rubric.hrih.si/og/2026-10-26/15.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.765, "judge_a": 0.62, "judge_b": 0.72, "mean": 0.7017, "spread": 0.145 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7567, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.973, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8178, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.825, "judge_a": 0.72, "judge_b": 0.68, "mean": 0.7417, "spread": 0.145 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.637, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6522, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.658, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.4792, "spread": 0.277 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.2, "judge_b": 0.86, "mean": 0.6333, "spread": 0.66 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0433, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.35, "judge_b": 0.32, "mean": 0.38, "spread": 0.15 } } }, { "rank": 16, "id": "st_20261026_3214", "title": "Red-teaming AI agent monitors with an LLM that fakes tool outputs", "url": "https://www.redwoodresearch.org/blog/simulated-control-evaluations", "domain": "redwoodresearch.org", "site": "redwoodresearch.org", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-25T15:41:09.000Z", "jev": 0.6457, "judge_a": 0.575, "judge_b": 0.7835, "score": 0.668, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Redwood Research proposes simulated control evaluations: an attacking model continues real coding-agent transcripts, an LLM fakes the tool outputs, and a judge decides whether the attack would have worked. Checked against executable LinuxArena, pooled safety error at a 5% audit budget was 3.6 points versus 10.1 for BashArena. Simulation also explained 78% of safety variation between environments. Fidelity can only be verified where real execution exists, so applying it to deployment means extrapolating. Multi-agent attacks and untrusted simulators remain untested.", "page": "https://rubric.hrih.si/editions/2026-10-26/16/", "card": "https://rubric.hrih.si/og/2026-10-26/16.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6742, "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": 0.623, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5644, "spread": 0.173 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.613, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6408, "spread": 0.21 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8667, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.848, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8592, "spread": 0.13 }, "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": 0.08, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0867, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.2, "judge_b": 0.2, "mean": 0.2233, "spread": 0.07 } } }, { "rank": 17, "id": "st_20261026_3029", "title": "Outside evaluators found ways OpenAI’s agents could slip past its monitor", "url": "https://thenextweb.com/news/openai-monitoring-system-metr-redwood-red-team", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-25T10:29:04.000Z", "jev": 0.6198, "judge_a": 0.59, "judge_b": 0.7636, "score": 0.6578, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "METR and Redwood Research, each with one researcher embedded at OpenAI from 21 July to 11 August, reported flaws in the system that monitors its internal agents. One exploit could have let agents hide hundreds of actions. Compaction events went unmonitored, leaving about 10% of eligible Codex traffic unwatched from May to July. Coverage of the strongest models' traffic was estimated at 75% to 94%. OpenAI says it patched every production flaw; the evaluators have not checked that. Only a redacted summary of the 61-page report is public.", "page": "https://rubric.hrih.si/editions/2026-10-26/17/", "card": "https://rubric.hrih.si/og/2026-10-26/17.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.728, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7025, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8233, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.44, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.62, "spread": 0.38 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.575, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5817, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.52, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.5967, "spread": 0.15 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.598, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5558, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8467, "spread": 0.1 }, "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": 0.23, "judge_a": 0.2, "judge_b": 0.24, "mean": 0.2233, "spread": 0.04 } } }, { "rank": 18, "id": "st_20261026_3468", "title": "OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?", "url": "https://minimaxir.com/2026/10/cheap-llms/", "domain": "minimaxir.com", "site": "Max Woolf's Blog", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-25T21:11:31.000Z", "jev": 0.6016, "judge_a": 0.65, "judge_b": 0.7033, "score": 0.6516, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Between June and late September 2026, OpenAI and Anthropic cut effective prices sharply: GPT-5.6 Luna fell 80%, Opus 5.5 costs 40-60% less than Opus 5, and GPT-6.1 Sol is about 75% cheaper than the prior Sol. Anthropic says Opus 5.5 needs less compute to serve. The author attributes the cuts mainly to open-weight competition, with 29 providers hosting DeepSeek V4.1 Flash on OpenRouter. Several figures, including Vercel's cost index and the benchmark scores, were not verified.", "page": "https://rubric.hrih.si/editions/2026-10-26/18/", "card": "https://rubric.hrih.si/og/2026-10-26/18.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6667, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.7667, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.817, "judge_a": 0.8, "judge_b": 0.8, "mean": 0.8056, "spread": 0.017 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.708, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7025, "spread": 0.008 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.49, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5367, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.645, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5417, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.83, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.07, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.41, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4467, "spread": 0.23 } } }, { "rank": 19, "id": "st_20261026_3410", "title": "Maker's Schedule, Manager's Schedule", "url": "https://paulgraham.com/makersschedule.html", "domain": "paulgraham.com", "site": "paulgraham.com", "categories": [ "programming", "startups" ], "category_names": [ "Programming & dev tools", "Startups & founders" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-25T19:43:01.000Z", "jev": 0.6896, "judge_a": 0.528, "judge_b": 0.7317, "score": 0.6497, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Paul Graham's 2009 essay separates two ways of using time: the manager's schedule, split into hour slots, and the maker's schedule, which needs half-day blocks for programming or writing. His argument is that one meeting can break an afternoon in two and discourage ambitious work earlier in the day. His remedies are office hours clustered at day's end and split days. The claims rest on his own experience and observation, with no data. For founders and engineering leads, it is a case for batching meetings.", "page": "https://rubric.hrih.si/editions/2026-10-26/19/", "card": "https://rubric.hrih.si/og/2026-10-26/19.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.67, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5467, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.75, "judge_b": 0.9, "mean": 0.8, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.897, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7589, "spread": 0.297 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.798, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7392, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.127, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.3256, "spread": 0.423 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.203, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1608, "spread": 0.103 }, "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": 0.04, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.04, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1333, "spread": 0.03 } } }, { "rank": 20, "id": "st_20261026_2908", "title": "Making DNS_Erlang Fast: Binary Tricks for the Beam", "url": "https://blog.dnsimple.com/2026/10/making-dns-erlang-fast/", "domain": "blog.dnsimple.com", "site": "blog.dnsimple.com", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-25T05:21:10.000Z", "jev": 0.7089, "judge_a": 0.4882, "judge_b": 0.7489, "score": 0.6486, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "DNSimple rewrote the hot paths of dns_erlang, the Erlang DNS library behind its erldns name server, using only BEAM-level binary techniques, with no NIFs. The changes include one append-only encoder accumulator, decoding that stays in the match context, and 56-bit SWAR guards. On a replay of about 19 million production-shaped queries, CPU per query fell 12% and saturation throughput rose 14%. These figures are self-reported and the intermediate benchmarks are unverified. Some earlier optimizations had no end-to-end effect.", "page": "https://rubric.hrih.si/editions/2026-10-26/20/", "card": "https://rubric.hrih.si/og/2026-10-26/20.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.608, "judge_a": 0.3, "judge_b": 0.7, "mean": 0.5358, "spread": 0.4 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.72, "judge_b": 0.9, "mean": 0.79, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.593, "judge_a": 0.25, "judge_b": 0.42, "mean": 0.4211, "spread": 0.343 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.833, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7342, "spread": 0.283 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.9033, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9525, "spread": 0.098 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.14, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1367, "spread": 0.1 } } }, { "rank": 21, "id": "st_20261026_3386", "title": "Go source to running container in 3.8 seconds", "url": "https://halt.software/go-source-to-container/", "domain": "halt.software", "site": "halt.software", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-25T10:05:23.000Z", "jev": 0.6677, "judge_a": 0.5415, "judge_b": 0.7357, "score": 0.6482, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A developer describes getting from saved Go source to a running production container in 3.8 seconds, by combining a warm Go build cache with a single shared distroless image into which each new binary is mounted, rather than building and pushing an image per version.", "page": "https://rubric.hrih.si/editions/2026-10-26/21/", "card": "https://rubric.hrih.si/og/2026-10-26/21.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.47, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.45, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.7733, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.587, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5522, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.878, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7992, "spread": 0.178 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.72, "judge_b": 0.91, "mean": 0.8633, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.9025, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.9133, "spread": 0.04 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.08, "judge_b": 0.02, "mean": 0.0533, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.35, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2933, "spread": 0.17 } } }, { "rank": 22, "id": "st_20261026_3401", "title": "It's a trap: testing coding agents on a language with surprising semantics", "url": "https://blog.csongor.co.uk/loom/", "domain": "blog.csongor.co.uk", "site": "Csongor Kiss", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-25T19:51:48.000Z", "jev": 0.6201, "judge_a": 0.4865, "judge_b": 0.8224, "score": 0.6429, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A blog post on Hrih News describes an experiment testing whether coding agents notice when an unfamiliar language behaves differently from what its look suggests. The author designs Loom, a small smart-contract language modeled on Move but with surprising semantics: signers can be stored, dynamic dispatch hints can override existing…", "page": "https://rubric.hrih.si/editions/2026-10-26/22/", "card": "https://rubric.hrih.si/og/2026-10-26/22.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.533, "judge_a": 0.35, "judge_b": 0.78, "mean": 0.5542, "spread": 0.43 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.92, "mean": 0.7367, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.57, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5967, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.598, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6425, "spread": 0.23 }, "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": 0.995, "judge_a": 0.75, "judge_b": 0.95, "mean": 0.8983, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.8967, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.1, "judge_b": 0.01, "mean": 0.0533, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.21, "spread": 0.22 } } }, { "rank": 23, "id": "st_20261026_3248", "title": "Rewriting Excel, PowerPoint, and Word for Agents", "url": "https://getcompound.ai/blog/rewriting-excel-powerpoint-and-word-for-agents", "domain": "getcompound.ai", "site": "Compound", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-25T16:06:14.000Z", "jev": 0.6668, "judge_a": 0.4932, "judge_b": 0.7666, "score": 0.6421, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Compound has rewritten Excel, PowerPoint and Word as headless engines in Rust, so AI agents can calculate, edit, print and visually check office files on servers without Microsoft Office.", "page": "https://rubric.hrih.si/editions/2026-10-26/23/", "card": "https://rubric.hrih.si/og/2026-10-26/23.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.718, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6292, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.72, "judge_b": 0.91, "mean": 0.8033, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.553, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.6078, "spread": 0.117 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.713, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7242, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.907, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.8289, "spread": 0.187 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.835, "judge_a": 0.78, "judge_b": 0.84, "mean": 0.8183, "spread": 0.06 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9233, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.45, "judge_b": 0.12, "mean": 0.2233, "spread": 0.35 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.4, "judge_b": 0.28, "mean": 0.3833, "spread": 0.19 } } }, { "rank": 24, "id": "st_20261026_3278", "title": "UTS #39: Unicode Security Mechanisms", "url": "https://unicode.org/reports/tr39/", "domain": "unicode.org", "site": "unicode.org", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "Other", "depth": "three_judges", "published_at": "2026-10-25T16:40:37.000Z", "jev": 0.635, "judge_a": 0.4993, "judge_b": 0.7899, "score": 0.6414, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Unicode Technical Standard #39, revision 34 (version 18.0.0, dated 2026-08-27), specifies mechanisms for detecting security problems in Unicode-based identifiers, such as spoofed URLs and look-alike characters.", "page": "https://rubric.hrih.si/editions/2026-10-26/24/", "card": "https://rubric.hrih.si/og/2026-10-26/24.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.588, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5858, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.38, "judge_a": 0.35, "judge_b": 0.82, "mean": 0.5167, "spread": 0.47 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.78, "judge_a": 0.4, "judge_b": 0.75, "mean": 0.6433, "spread": 0.38 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.805, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7283, "spread": 0.205 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.893, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8844, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8967, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "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": 0.15, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0767, "spread": 0.12 } } }, { "rank": 25, "id": "st_20261026_3480", "title": "Frontdoor: find who can assume into your cloud accounts", "url": "https://github.com/secorvia/frontdoor", "domain": "github.com", "site": "GitHub", "categories": [ "security", "devops" ], "category_names": [ "Security", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-25T21:17:22.000Z", "jev": 0.7033, "judge_a": 0.4915, "judge_b": 0.7026, "score": 0.6324, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Frontdoor is an open-source command-line tool, released as a 0.1.0 Show HN project, that maps the federated trust policies (OIDC, SAML and cross-account) by which outside identities such as GitHub repositories, CI pipelines, SaaS vendors or other clouds can obtain credentials in AWS, GCP and Azure accounts.", "page": "https://rubric.hrih.si/editions/2026-10-26/25/", "card": "https://rubric.hrih.si/og/2026-10-26/25.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.598, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5425, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.87, "mean": 0.7833, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.45, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5067, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.878, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7992, "spread": 0.178 }, "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": 0.998, "judge_a": 0.75, "judge_b": 0.95, "mean": 0.8992, "spread": 0.248 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.9267, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.24, "mean": 0.2267, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.3, "judge_b": 0.32, "mean": 0.2733, "spread": 0.12 } } }, { "rank": 26, "id": "st_20261026_3056", "title": "Mozilla reported HTTP2Shell with proof of RCE in April", "url": "https://bugzilla.mozilla.org/show_bug.cgi?id=2029458", "domain": "bugzilla.mozilla.org", "site": "bugzilla.mozilla.org", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-25T12:17:58.000Z", "jev": 0.6442, "judge_a": 0.5138, "judge_b": 0.7383, "score": 0.632, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Mozilla reportedly received a report, HTTP2Shell, describing a remote memory-corruption bug in the Brotli decoder vendored in Firefox, with a claimed proof of remote code execution.", "page": "https://rubric.hrih.si/editions/2026-10-26/26/", "card": "https://rubric.hrih.si/og/2026-10-26/26.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.703, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6775, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.74, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.61, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.6033, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.725, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6517, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.933, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7511, "spread": 0.333 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.9158, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.2, "judge_b": 0.99, "mean": 0.7033, "spread": 0.79 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.03, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.5, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5233, "spread": 0.37 } } }, { "rank": 27, "id": "st_20261026_3210", "title": "In times of uncertainty, focus on principles", "url": "https://r.ena.to/blog/in-times-of-uncertainty-focus-on-principles/", "domain": "r.ena.to", "site": "Renato's Runtime Reflections", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T15:30:50.000Z", "jev": 0.6962, "judge_a": 0.4677, "judge_b": 0.6925, "score": 0.6188, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Hrih News summary: In a long-form essay, a veteran engineer argues that the principles behind productive software development, such as clear boundaries, explicit contracts, strong tests, and fast feedback loops, now also determine how far AI agents can reach, both for developers and for attackers.", "page": "https://rubric.hrih.si/editions/2026-10-26/27/", "card": "https://rubric.hrih.si/og/2026-10-26/27.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.62, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.55, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.62, "judge_b": 0.82, "mean": 0.7267, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.97, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8167, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.813, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7442, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.34, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4233, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.605, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.485, "spread": 0.305 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.46, "spread": 0.64 }, "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": 0.21, "judge_a": 0.35, "judge_b": 0.25, "mean": 0.27, "spread": 0.14 } } }, { "rank": 28, "id": "st_20261026_3259", "title": "AI Gateway Benchmark: OpenRouter, Vercel, Ramp, Keln", "url": "https://keln.ai/blog/ai-gateway-benchmark-deepseek-v4-1-flash", "domain": "keln.ai", "site": "Keln", "categories": [ "ai", "devops" ], "category_names": [ "AI & machine learning", "Infrastructure & DevOps" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-25T16:53:40.000Z", "jev": 0.6988, "judge_a": 0.5278, "judge_b": 0.6271, "score": 0.6178, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A benchmark of four AI gateways (OpenRouter, Vercel, Ramp and Keln) serving the same model over 3,360 turns each found large differences in behavior. Keln kept 94% of prompt tokens cached, versus 63% for Ramp, and moved conversations between providers 14 times compared with 500 for Ramp.", "page": "https://rubric.hrih.si/editions/2026-10-26/28/", "card": "https://rubric.hrih.si/og/2026-10-26/28.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.5117, "spread": 0.255 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.7867, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.477, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.4922, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.843, "judge_a": 0.78, "judge_b": 0.78, "mean": 0.8008, "spread": 0.063 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.947, "judge_a": 0.82, "judge_b": 0.72, "mean": 0.8289, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.8558, "spread": 0.208 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.9033, "spread": 0.01 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.35, "judge_b": 0.3, "mean": 0.2367, "spread": 0.29 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.31, "spread": 0.13 } } }, { "rank": 29, "id": "st_20261026_3343", "title": "Lightning.ai October 2026 Security Incident", "url": "https://lightning.ai/security-incident/", "domain": "lightning.ai", "site": "Lightning AI", "categories": [ "security", "devops" ], "category_names": [ "Security", "Infrastructure & DevOps" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-25T18:08:32.000Z", "jev": 0.4782, "judge_a": 0.6155, "judge_b": 0.7573, "score": 0.617, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Lightning.ai disclosed a security incident on its Lightning Studio Platform, covering Studio, Deployments, Pipelines, and Drive, in which unauthorized actors accessed customer credentials. The company revoked all user access and remains offline during an investigation with an external cybersecurity firm.", "page": "https://rubric.hrih.si/editions/2026-10-26/29/", "card": "https://rubric.hrih.si/og/2026-10-26/29.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.453, "judge_a": 0.5, "judge_b": 0.74, "mean": 0.5642, "spread": 0.288 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.7367, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.193, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.3911, "spread": 0.307 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7767, "spread": 0.11 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.307, "judge_a": 0.35, "judge_b": 0.78, "mean": 0.4789, "spread": 0.473 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.502, "judge_a": 0.2, "judge_b": 0.42, "mean": 0.3742, "spread": 0.302 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 1, "mean": 0.9433, "spread": 0.1 }, "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": 0.13, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.1033, "spread": 0.12 } } }, { "rank": 30, "id": "st_20261026_3048", "title": "US Treasury moves to curb AI, chip and biotech deals in China and Russia", "url": "https://thenextweb.com/news/us-treasury-outbound-investment-rule-china-ai-biotech", "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": "2026-10-25T11:44:53.000Z", "jev": 0.5602, "judge_a": 0.5243, "judge_b": 0.7616, "score": 0.6153, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "The US Treasury has proposed rules to widen limits on American investment in AI, chips, quantum computing, supercomputing, hypersonics and biotechnology in China, Russia, North Korea, Cuba, Iran and Hong Kong and Macau.", "page": "https://rubric.hrih.si/editions/2026-10-26/30/", "card": "https://rubric.hrih.si/og/2026-10-26/30.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.705, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6783, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.6967, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.797, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7089, "spread": 0.247 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.468, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5558, "spread": 0.232 }, "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": 0.36, "judge_a": 0.15, "judge_b": 0.28, "mean": 0.2633, "spread": 0.21 }, "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": 0.07, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.04, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.25, "spread": 0.31 } } }, { "rank": 31, "id": "st_20261026_3012", "title": "Google's C Binder Driver Removed With Rust Binder Ready For \"Few Billion Devices\"", "url": "https://www.phoronix.com/news/Linux-7.4-Char-Misc", "domain": "phoronix.com", "site": "Phoronix", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-25T09:59:30.000Z", "jev": 0.5544, "judge_a": 0.5063, "judge_b": 0.7716, "score": 0.6107, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Linus Torvalds merged the char/misc changes for Linux 7.4, which removes Google's C implementation of the Binder IPC driver. The Rust-based Binder driver, which now matches the original's features and outperforms it in some tests, becomes the sole Binder implementation.", "page": "https://rubric.hrih.si/editions/2026-10-26/31/", "card": "https://rubric.hrih.si/og/2026-10-26/31.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.748, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6925, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.6867, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.623, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6411, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.538, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5692, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.403, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.5444, "spread": 0.377 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.513, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4708, "spread": 0.2 }, "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": 0.1, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.39, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.2567, "spread": 0.31 } } }, { "rank": 32, "id": "st_20261026_3080", "title": "Password Manager Recommendations", "url": "https://dennysora.me/en/blog/security/password-manager-recommendations/", "domain": "dennysora.me", "site": "dennysora.me", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-25T12:30:23.000Z", "jev": 0.6575, "judge_a": 0.5005, "judge_b": 0.673, "score": 0.6104, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A Hrih News-style summary: A user-written guide argues that password reuse enables credential stuffing, where leaked logins are tried across other sites, and recommends password managers that generate unique passwords and store them encrypted, with local decryption and a master password.", "page": "https://rubric.hrih.si/editions/2026-10-26/32/", "card": "https://rubric.hrih.si/og/2026-10-26/32.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4458, "spread": 0.157 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6533, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.77, "judge_a": 0.6, "judge_b": 0.64, "mean": 0.67, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.72, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.7633, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.727, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7689, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8642, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.97, "judge_a": 0.15, "judge_b": 0.98, "mean": 0.7, "spread": 0.83 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.0833, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2033, "spread": 0.17 } } }, { "rank": 33, "id": "st_20261026_3479", "title": "Cross-Platform vs. Native Mobile Development: Empirical Study", "url": "https://arxiv.org/abs/2609.21544", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-25T21:21:06.000Z", "jev": 0.6126, "judge_a": 0.4565, "judge_b": 0.7426, "score": 0.6039, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "This paper empirically compares five implementations of the same plant-management app (native iOS and Android, Flutter, React Native, and Kotlin Multiplatform, yielding eight runtime variants) using 2,000 repeated runs per variant. Guided by ISO/IEC 25010, it measures client-operation timing, rendering wrappers, source footprint, and dependencies.", "page": "https://rubric.hrih.si/editions/2026-10-26/33/", "card": "https://rubric.hrih.si/og/2026-10-26/33.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.535, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.485, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.6267, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.583, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5944, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.703, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6575, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8111, "spread": 0.253 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.965, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8483, "spread": 0.265 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.6, "judge_b": 0.97, "mean": 0.84, "spread": 0.37 }, "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": 0.14, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.14, "spread": 0.12 } } }, { "rank": 34, "id": "st_20261026_3284", "title": "A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis", "url": "https://lynchlab.net/llm-skin-cancer", "domain": "lynchlab.net", "site": "lynchlab.net", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-25T17:15:38.000Z", "jev": 0.5032, "judge_a": 0.5705, "judge_b": 0.7331, "score": 0.6023, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A dermatologist tested two frontier LLMs, GPT-6 Astra and Claude Fable 5.1, against a fine-tuned 10-year-old Inception v3 CNN on 200 biopsy-proven skin lesions from the public MILK10k dataset, using clinical and dermoscopic images.", "page": "https://rubric.hrih.si/editions/2026-10-26/34/", "card": "https://rubric.hrih.si/og/2026-10-26/34.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.502, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5908, "spread": 0.218 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.7633, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.653, "judge_a": 0.6, "judge_b": 0.55, "mean": 0.6011, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.473, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.5742, "spread": 0.227 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.933, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.7644, "spread": 0.433 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.84, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.74, "spread": 0.24 }, "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": 0.08, "judge_a": 0.1, "judge_b": 0.01, "mean": 0.0633, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.61, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.48, "spread": 0.23 } } }, { "rank": 35, "id": "st_20261026_2959", "title": "Exploring Apple's On-Device Model: Twenty Billion Parameters in Flash", "url": "https://www.airealist.ai/p/exploring-apple-on-device-model", "domain": "airealist.ai", "site": "airealist.ai", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-25T08:23:50.000Z", "jev": 0.6432, "judge_a": 0.6245, "judge_b": 0.8381, "score": 0.6019, "contested": false, "flag_penalty": 0.1, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Apple's Core Advanced is the larger of its two on-device models, with 20 billion parameters of which only 1 to 4 billion are active at once, built with Google and powering Siri's new voices and dictation.", "page": "https://rubric.hrih.si/editions/2026-10-26/35/", "card": "https://rubric.hrih.si/og/2026-10-26/35.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6625, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.83, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.493, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5711, "spread": 0.227 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.683, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.7042, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.94, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.88, "spread": 0.14 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.9475, "spread": 0.093 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9333, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0433, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.29, "spread": 0.17 } } }, { "rank": 36, "id": "st_20261026_2881", "title": "GVS5H: Five Qwen3.8-27B Models Match Claude Fable 5 on LiveCodeBench Hard", "url": "https://github.com/slee-persis/GVS5H", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-25T03:56:01.000Z", "jev": 0.5973, "judge_a": 0.4615, "judge_b": 0.7435, "score": 0.6008, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A preprint and open-source repository (GVS5H) describes a training-free method called ledger-based zero-shot self-orchestration, in which fresh instances of one model split up a coding problem and coordinate through a shared filesystem of plans, notes, and solutions.", "page": "https://rubric.hrih.si/editions/2026-10-26/36/", "card": "https://rubric.hrih.si/og/2026-10-26/36.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.645, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5983, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.72, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.57, "judge_a": 0.5, "judge_b": 0.65, "mean": 0.5733, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.725, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7017, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.937, "judge_a": 0.4, "judge_b": 0.8, "mean": 0.7122, "spread": 0.537 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.815, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7317, "spread": 0.215 }, "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": 0.09, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.09, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.72, "judge_a": 0.6, "judge_b": 0.34, "mean": 0.5533, "spread": 0.38 } } }, { "rank": 37, "id": "st_20261026_3106", "title": "A 32K-window proxy that beat full context on long agent histories", "url": "https://github.com/lastreload/mnemonic-proxy", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-25T13:08:14.000Z", "jev": 0.6037, "judge_a": 0.4745, "judge_b": 0.7229, "score": 0.6004, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "mnemonic-proxy is an open-source, standard-library-only HTTP proxy that sits between a coding agent and a local model server (llama-server, Strata, ds4-server, or any OpenAI-compatible backend).", "page": "https://rubric.hrih.si/editions/2026-10-26/37/", "card": "https://rubric.hrih.si/og/2026-10-26/37.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.555, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5083, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7233, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.427, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4022, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.815, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7783, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.79, "spread": 0.41 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.91, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.92, "spread": 0.06 }, "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": 0.65, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.3767, "spread": 0.47 } } }, { "rank": 38, "id": "st_20261026_3358", "title": "An American Answer to China's Open Si Models: A Safe Harbor for Open Weights", "url": "https://manhattan.institute/article/an-american-answer-to-chinas-open-si-models-a-safe-harbor-for-open-weights", "domain": "manhattan.institute", "site": "Manhattan Institute", "categories": [ "ai", "security", "policy" ], "category_names": [ "AI & machine learning", "Security", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T18:54:18.000Z", "jev": 0.5617, "judge_a": 0.499, "judge_b": 0.7255, "score": 0.5953, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Manhattan Institute proposes trading an antitrust safe harbor for leading American AI labs, which would let them coordinate on security and anti-distillation efforts at the frontier, for a commitment to release capable open-weight versions of their models on a predictable schedule after independent risk evaluations.", "page": "https://rubric.hrih.si/editions/2026-10-26/38/", "card": "https://rubric.hrih.si/og/2026-10-26/38.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6625, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.7467, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.887, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7689, "spread": 0.287 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.505, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5583, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.533, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6178, "spread": 0.187 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.478, "judge_a": 0.3, "judge_b": 0.46, "mean": 0.4125, "spread": 0.178 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.6, "judge_b": 0.99, "mean": 0.8467, "spread": 0.39 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.15, "judge_b": 0.06, "mean": 0.11, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4867, "spread": 0.27 } } }, { "rank": 39, "id": "st_20261026_3038", "title": "DeepSeek and MiniMax cleared to take AI models abroad, state media says", "url": "https://thenextweb.com/news/deepseek-minimax-china-ai-export-controls-overseas", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-25T11:11:36.000Z", "jev": 0.4779, "judge_a": 0.579, "judge_b": 0.7229, "score": 0.5932, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "China's commerce and science ministries have proposed adding 23 AI technologies, including large models, agents and computing clusters, to its export restriction list, with public comments closing 15 November.", "page": "https://rubric.hrih.si/editions/2026-10-26/39/", "card": "https://rubric.hrih.si/og/2026-10-26/39.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6792, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.73, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.59, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6567, "spread": 0.19 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.45, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.56, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.387, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4522, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.49, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.4033, "spread": 0.19 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.97, "mean": 0.8967, "spread": 0.17 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.16, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0967, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.6, "judge_a": 0.2, "judge_b": 0.22, "mean": 0.34, "spread": 0.4 } } }, { "rank": 40, "id": "st_20261026_3384", "title": "FlashRaw – RAW image decoder in Rust, up to 15x faster than LibRaw", "url": "https://github.com/ybouane/flashraw", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-25T16:03:02.000Z", "jev": 0.6767, "judge_a": 0.454, "judge_b": 0.647, "score": 0.5925, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "FlashRaw is a new open-source RAW camera image decoder written in memory-safe Rust, released as version 0.1.0 with C, C++, Python and JavaScript bindings.", "page": "https://rubric.hrih.si/editions/2026-10-26/40/", "card": "https://rubric.hrih.si/og/2026-10-26/40.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.5225, "spread": 0.218 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.75, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.387, "judge_a": 0.45, "judge_b": 0.35, "mean": 0.3956, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.893, "judge_a": 0.65, "judge_b": 0.7, "mean": 0.7475, "spread": 0.242 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.987, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.8156, "spread": 0.387 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.915, "spread": 0.195 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.45, "judge_b": 0.08, "mean": 0.2033, "spread": 0.37 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.5, "judge_b": 0.2, "mean": 0.3533, "spread": 0.3 } } }, { "rank": 41, "id": "st_20261026_3298", "title": "Benchmarking DSpark speculative decoding on a consumer GPU", "url": "https://github.com/juraj-jumic/dspark-local-bench", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-25T17:07:06.000Z", "jev": 0.5795, "judge_a": 0.4825, "judge_b": 0.7153, "score": 0.5924, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "The DSpark speculative decoding benchmark project measures how much faster local LLM generation gets on a consumer GPU (AMD Radeon RX 7900 GRE, Qwen3-8B Q8_0), comparing it against other llama.cpp methods.", "page": "https://rubric.hrih.si/editions/2026-10-26/41/", "card": "https://rubric.hrih.si/og/2026-10-26/41.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.463, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4475, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.66, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.373, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.3811, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.705, "judge_a": 0.65, "judge_b": 0.82, "mean": 0.725, "spread": 0.17 }, "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": 0.998, "judge_a": 0.8, "judge_b": 0.83, "mean": 0.8758, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.93, "spread": 0.05 }, "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": 0.18, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1533, "spread": 0.12 } } }, { "rank": 42, "id": "st_20261026_3353", "title": "pola-rs/polars py-2.1.0", "url": "https://github.com/pola-rs/polars/releases/tag/py-2.1.0", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-25T18:24:39.000Z", "jev": 0.6262, "judge_a": 0.462, "judge_b": 0.6833, "score": 0.5904, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "The Polars 2.1.0 release notes list one breaking change, which disallows mixed string and numeric arithmetic in eager mode, and a long run of performance work. Most of the performance items target out-of-core and streaming execution: better memory budgeting and spill handling, streaming group-by and window evaluation, faster Parquet and…", "page": "https://rubric.hrih.si/editions/2026-10-26/42/", "card": "https://rubric.hrih.si/og/2026-10-26/42.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.663, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.5308, "spread": 0.313 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.53, "judge_a": 0.45, "judge_b": 0.75, "mean": 0.5767, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.417, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4556, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.858, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7192, "spread": 0.258 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.723, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.6944, "spread": 0.36 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6533, "spread": 0.23 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9333, "spread": 0.09 }, "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": 0.14, "judge_a": 0.1, "judge_b": 0.02, "mean": 0.0867, "spread": 0.12 } } }, { "rank": 43, "id": "st_20261026_3263", "title": "We Are All in the C-Suite Now", "url": "https://www.brethorsting.com/blog/2026/10/we-are-all-in-the-c-suite-now/", "domain": "brethorsting.com", "site": "Aaron Brethorst", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T16:43:14.000Z", "jev": 0.6535, "judge_a": 0.49, "judge_b": 0.6249, "score": 0.5894, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "An essay by a developer argues that agentic AI has pushed working engineers into executive roles: they no longer write most code and instead set direction, judge the agent's output, and decide what to build.", "page": "https://rubric.hrih.si/editions/2026-10-26/43/", "card": "https://rubric.hrih.si/og/2026-10-26/43.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.808, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5925, "spread": 0.458 }, "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": 0.7, "judge_b": 0.78, "mean": 0.8078, "spread": 0.243 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.675, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.6417, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.087, "judge_a": 0.3, "judge_b": 0.36, "mean": 0.2489, "spread": 0.273 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.388, "judge_a": 0.3, "judge_b": 0.3, "mean": 0.3292, "spread": 0.088 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7967, "spread": 0.31 }, "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": 0.51, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.48, "spread": 0.23 } } }, { "rank": 44, "id": "st_20261026_3086", "title": "Farol – a risk map of a pull request for the person who approves it", "url": "https://github.com/winnin/farol", "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": "2026-10-25T13:28:06.000Z", "jev": 0.69, "judge_a": 0.4165, "judge_b": 0.6605, "score": 0.589, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Farol is a free, open-source CLI and GitHub Action that runs after an AI code review and gives the person approving a pull request a risk verdict, a 0–100 score, and a ranked list of the files and pages the change reaches.", "page": "https://rubric.hrih.si/editions/2026-10-26/44/", "card": "https://rubric.hrih.si/og/2026-10-26/44.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4658, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6967, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.623, "judge_a": 0.5, "judge_b": 0.56, "mean": 0.5611, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.925, "judge_a": 0.65, "judge_b": 0.82, "mean": 0.7983, "spread": 0.275 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.923, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7744, "spread": 0.323 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.97, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8433, "spread": 0.27 }, "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": 0.11, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2467, "spread": 0.34 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.4, "judge_b": 0.3, "mean": 0.3067, "spread": 0.18 } } }, { "rank": 45, "id": "st_20261026_3266", "title": "What Does a Privacy Team Do?", "url": "https://libber.org/what_do_privacy_teams_do.html", "domain": "libber.org", "site": "libber.org", "categories": [ "policy" ], "category_names": [ "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T16:36:49.000Z", "jev": 0.5848, "judge_a": 0.4995, "judge_b": 0.6741, "score": 0.5861, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A former product engineer describes three years on a large tech company's privacy team, arguing the work is an engineering discipline: interpret legal and policy expectations into precise requirements (using GDPR's right to erasure as the example), implement controls such as deletion pipelines and data inventories, and prove they work…", "page": "https://rubric.hrih.si/editions/2026-10-26/45/", "card": "https://rubric.hrih.si/og/2026-10-26/45.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4683, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.6467, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.687, "judge_a": 0.55, "judge_b": 0.55, "mean": 0.5956, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.738, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6858, "spread": 0.138 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.29, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.4533, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.718, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.6125, "spread": 0.218 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8967, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0367, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1267, "spread": 0.12 } } }, { "rank": 46, "id": "st_20261026_3478", "title": "Pocket Casts Unfolded", "url": "https://kean.blog/post/pocket-casts-iphone-duo", "domain": "kean.blog", "site": "kean.blog", "categories": [ "programming", "ai", "design" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Design & UX" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-25T21:21:18.000Z", "jev": 0.6296, "judge_a": 0.4185, "judge_b": 0.706, "score": 0.5847, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Pocket Casts shipped support for Apple's iPhone Duo, a foldable form factor, ahead of launch. The engineer who led the work used coding agents to run an Apple-provided resizability skill, sweep the app's screens across window sizes with Xcode 27's new device-interaction tools, triage roughly a thousand observations into 56…", "page": "https://rubric.hrih.si/editions/2026-10-26/46/", "card": "https://rubric.hrih.si/og/2026-10-26/46.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.493, "judge_a": 0.3, "judge_b": 0.63, "mean": 0.4742, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7033, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.467, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.4822, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.855, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7517, "spread": 0.255 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.843, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7678, "spread": 0.26 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.895, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7717, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.7, "judge_b": 0.98, "mean": 0.87, "spread": 0.28 }, "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": 0.16, "judge_a": 0.2, "judge_b": 0.22, "mean": 0.1933, "spread": 0.06 } } }, { "rank": 47, "id": "st_20261026_2994", "title": "OpenXet", "url": "https://github.com/stubbi/openxet", "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": "2026-10-25T09:29:43.000Z", "jev": 0.5916, "judge_a": 0.4495, "judge_b": 0.7121, "score": 0.5844, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "OpenXet is an open-source server and client for Hugging Face's Xet protocol, which stores large, versioned files like models and datasets using chunk-level deduplication.", "page": "https://rubric.hrih.si/editions/2026-10-26/47/", "card": "https://rubric.hrih.si/og/2026-10-26/47.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.548, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5058, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.64, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.47, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.49, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.73, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7033, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.833, "judge_a": 0.5, "judge_b": 0.88, "mean": 0.7378, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.74, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6833, "spread": 0.21 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "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": 0.12, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.15, "spread": 0.17 } } }, { "rank": 48, "id": "st_20261026_3146", "title": "The Open Garbage", "url": "https://carette.xyz/posts/the_open_garbage/", "domain": "carette.xyz", "site": "A journey into a wild pointer", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T14:07:03.000Z", "jev": 0.63, "judge_a": 0.482, "judge_b": 0.6216, "score": 0.5779, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Hrih News covers a blog essay arguing that AI coding agents are dissolving the trust model behind open-source software. The author, who previously wrote about the \"chain of trust\" in free software, contends that vibe-coded tools built with agents can carry hidden system-prompt instructions, prompt injection risks, fabricated dependencies, and…", "page": "https://rubric.hrih.si/editions/2026-10-26/48/", "card": "https://rubric.hrih.si/og/2026-10-26/48.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.72, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.6133, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.7667, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.983, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8211, "spread": 0.283 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.733, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6375, "spread": 0.233 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.35, "judge_a": 0.35, "judge_b": 0.43, "mean": 0.3767, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.54, "judge_a": 0.3, "judge_b": 0.46, "mean": 0.4333, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.4, "judge_b": 0.76, "mean": 0.6833, "spread": 0.49 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0567, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.92, "mean": 0.7233, "spread": 0.32 } } }, { "rank": 49, "id": "st_20261026_3156", "title": "OpenLDK: Bringing the Java World into Common Lisp", "url": "https://atgreen.github.io/repl-yell/posts/openldk-java-in-common-lisp/", "domain": "atgreen.github.io", "site": "REPL Yell!", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-25T14:45:13.000Z", "jev": 0.5454, "judge_a": 0.4605, "judge_b": 0.7258, "score": 0.5772, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "OpenLDK is a JIT compiler and runtime that translates Java bytecode into Common Lisp and compiles it to native code via SBCL, letting Java objects live in the same process and heap as Lisp applications.", "page": "https://rubric.hrih.si/editions/2026-10-26/49/", "card": "https://rubric.hrih.si/og/2026-10-26/49.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.498, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5225, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.73, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.373, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.3811, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.495, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.525, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.967, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8556, "spread": 0.267 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9317, "spread": 0.125 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 1, "mean": 0.9433, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.05, "mean": 0.0867, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.21, "spread": 0.22 } } }, { "rank": 50, "id": "st_20261026_3564", "title": "AI is getting good at math, here's why", "url": "https://latent.space/p/ai-is-getting-good-at-math/", "domain": "latent.space", "site": "Latent Space", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T23:30:52.000Z", "jev": 0.5954, "judge_a": 0.434, "judge_b": 0.7021, "score": 0.5771, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "This Hrih News piece argues that AI models solve math problems differently from humans: where people search breadth-first and need strong hints to justify months of work, LLMs search depth-first and can test weak ideas cheaply when paired with an automated verifier.", "page": "https://rubric.hrih.si/editions/2026-10-26/50/", "card": "https://rubric.hrih.si/og/2026-10-26/50.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.64, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6033, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.74, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.807, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6922, "spread": 0.257 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.57, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.5167, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.327, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4189, "spread": 0.253 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.713, "judge_a": 0.5, "judge_b": 0.76, "mean": 0.6575, "spread": 0.26 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.35, "judge_b": 0.97, "mean": 0.7467, "spread": 0.62 }, "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": 0.36, "judge_a": 0.4, "judge_b": 0.46, "mean": 0.4067, "spread": 0.1 } } }, { "rank": 51, "id": "st_20261026_3071", "title": "So Much Tech News: The Week of 10/18/2026", "url": "https://amichae2.substack.com/p/so-much-tech-news-week-2-10182026", "domain": "amichae2.substack.com", "site": "amichae2.substack.com", "categories": [ "ai", "security", "startups", "policy" ], "category_names": [ "AI & machine learning", "Security", "Startups & founders", "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-25T12:54:02.000Z", "jev": 0.555, "judge_a": 0.477, "judge_b": 0.6885, "score": 0.5735, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A weekly digest from one reader compiles about 70 tech and science links from the week of October 18, 2026, spanning AI, cybersecurity, quantum physics, and business news.", "page": "https://rubric.hrih.si/editions/2026-10-26/51/", "card": "https://rubric.hrih.si/og/2026-10-26/51.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.49, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5367, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7333, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.963, "judge_a": 0.5, "judge_b": 0.92, "mean": 0.7944, "spread": 0.463 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.663, "judge_a": 0.45, "judge_b": 0.65, "mean": 0.5875, "spread": 0.212 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.507, "judge_a": 0.55, "judge_b": 0.52, "mean": 0.5256, "spread": 0.043 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.773, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5842, "spread": 0.372 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.95, "mean": 0.8133, "spread": 0.35 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.09, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.65, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5233, "spread": 0.35 } } }, { "rank": 52, "id": "st_20261026_3207", "title": "Liella: A Glibc Reimplementation in Rust", "url": "https://github.com/duskglow/liella", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-25T15:49:10.000Z", "jev": 0.5433, "judge_a": 0.467, "judge_b": 0.7002, "score": 0.5701, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Liella is a work-in-progress clean-room reimplementation of glibc 2.39's x86-64 Linux ABI in Rust, built from a functional specification and checked by black-box comparison with an installed glibc.", "page": "https://rubric.hrih.si/editions/2026-10-26/52/", "card": "https://rubric.hrih.si/og/2026-10-26/52.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5158, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.6867, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.457, "judge_a": 0.55, "judge_b": 0.48, "mean": 0.4956, "spread": 0.093 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.43, "judge_a": 0.35, "judge_b": 0.67, "mean": 0.4833, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.897, "judge_a": 0.5, "judge_b": 0.88, "mean": 0.7589, "spread": 0.397 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.868, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8258, "spread": 0.21 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.04, "mean": 0.0833, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.03, "mean": 0.1233, "spread": 0.17 } } }, { "rank": 53, "id": "st_20261026_2856", "title": "Spacedrive – Take back your data", "url": "https://github.com/spacedriveapp/spacedrive", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "data", "product" ], "category_names": [ "Programming & dev tools", "Data", "Product" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-25T02:07:31.000Z", "jev": 0.6422, "judge_a": 0.437, "judge_b": 0.6145, "score": 0.5645, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Spacedrive is an open-source personal data system that presents files across local drives, NAS storage, other devices, and optional cloud providers as one private filesystem, with no hosted account or telemetry.", "page": "https://rubric.hrih.si/editions/2026-10-26/53/", "card": "https://rubric.hrih.si/og/2026-10-26/53.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.568, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5292, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7033, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.52, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5233, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.81, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6967, "spread": 0.21 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.62, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.58, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.945, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8083, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9367, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.3, "judge_b": 0.32, "mean": 0.2433, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.5, "judge_b": 0.28, "mean": 0.3333, "spread": 0.28 } } }, { "rank": 54, "id": "st_20261026_2864", "title": "A CA key that was the number 4 passed its audit. Nobody was assigned to check it", "url": "https://pilgrima.ge/p/someone-else-checked", "domain": "pilgrima.ge", "site": "The Pilgrim Age", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T02:56:00.000Z", "jev": 0.6, "judge_a": 0.4615, "judge_b": 0.6119, "score": 0.5577, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A certificate authority named eMudhra, of Bengaluru, had eight secret root keys flawed during an April 2025 key ceremony, yet the error went unnoticed through audits and inclusion reviews until a researcher called Wayne flagged it in October.", "page": "https://rubric.hrih.si/editions/2026-10-26/54/", "card": "https://rubric.hrih.si/og/2026-10-26/54.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.635, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.6217, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.69, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.927, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.7022, "spread": 0.427 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.543, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5075, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.33, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.55, "spread": 0.39 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.66, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.4633, "spread": 0.31 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.41, "spread": 0.67 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0533, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.25, "judge_b": 0.34, "mean": 0.3167, "spread": 0.11 } } }, { "rank": 55, "id": "st_20261026_3132", "title": "Devin agent skills that patch the GitLab CVSS 10.0, PHP and Node.js RCEs", "url": "https://github.com/cognitionai/devin-vuln-fixes", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "security", "devops" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-25T14:11:13.000Z", "jev": 0.6442, "judge_a": 0.4385, "judge_b": 0.589, "score": 0.5572, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A set of three Agent Skills from Cognition AI helps coding agents such as Devin, Claude Code and Codex find and patch vulnerable PHP, Node.js and self-managed GitLab runtimes across repositories.", "page": "https://rubric.hrih.si/editions/2026-10-26/55/", "card": "https://rubric.hrih.si/og/2026-10-26/55.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.538, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5225, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.67, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.737, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6856, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.943, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8475, "spread": 0.243 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.563, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5278, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.858, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.6692, "spread": 0.408 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.45, "judge_b": 0.92, "mean": 0.77, "spread": 0.49 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.16, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.46, "spread": 0.56 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2633, "spread": 0.18 } } }, { "rank": 56, "id": "st_20261026_3420", "title": "Some reasons to work on productivity and velocity", "url": "https://danluu.com/productivity-velocity/", "domain": "danluu.com", "site": "danluu.com", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T20:24:05.000Z", "jev": 0.548, "judge_a": 0.5005, "judge_b": 0.623, "score": 0.5571, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Dan Luu argues that productivity and execution velocity matter, and that they multiply rather than compete with picking the right problem. He cites a machine-health project where fast iteration let him discard dead ends and find a working approach, and rebuts common claims that speed is irrelevant, such as \"you…", "page": "https://rubric.hrih.si/editions/2026-10-26/56/", "card": "https://rubric.hrih.si/og/2026-10-26/56.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.333, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3875, "spread": 0.147 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.53, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.6767, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.857, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7256, "spread": 0.257 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.705, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6617, "spread": 0.105 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.377, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4189, "spread": 0.103 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.615, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.4817, "spread": 0.235 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.97, "mean": 0.8333, "spread": 0.37 }, "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": 0.19, "judge_a": 0.35, "judge_b": 0.3, "mean": 0.28, "spread": 0.16 } } }, { "rank": 57, "id": "st_20261026_2828", "title": "Hurston-1 open source TTS that speak Southern American and Afro-English", "url": "https://huggingface.co/destined/hurston-1", "domain": "huggingface.co", "site": "huggingface.co", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-25T01:30:13.000Z", "jev": 0.6386, "judge_a": 0.344, "judge_b": 0.6865, "score": 0.5563, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Hurston-1 is an open-source text-to-speech model, a fine-tune of Maya1 under Apache-2.0, built to speak Southern American and several African-accented English voices that most TTS systems handle poorly.", "page": "https://rubric.hrih.si/editions/2026-10-26/57/", "card": "https://rubric.hrih.si/og/2026-10-26/57.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.53, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.47, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.4, "judge_b": 0.82, "mean": 0.6133, "spread": 0.42 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.373, "judge_a": 0.3, "judge_b": 0.57, "mean": 0.4144, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.903, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7442, "spread": 0.352 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.65, "judge_b": 0.86, "mean": 0.8267, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.935, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7717, "spread": 0.335 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.18, "spread": 0.28 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.24, "spread": 0.12 } } }, { "rank": 58, "id": "st_20261026_3291", "title": "Mail is not that hard", "url": "https://www.frimin.fr/posts/mail-is-not-that-hard/", "domain": "frimin.fr", "site": "frimin.fr", "categories": [ "devops", "it" ], "category_names": [ "Infrastructure & DevOps", "IT & networking" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-25T17:01:02.000Z", "jev": 0.5027, "judge_a": 0.5198, "judge_b": 0.6458, "score": 0.5561, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A personal developer's write-up on self-hosting mail for his own domain since 2019, running Postfix, Dovecot, Rspamd and PostgreSQL on a single FreeBSD machine. It is useful to teams weighing self-hosting against a managed provider because it details the operational pieces: database-backed aliases, failing closed when the spam filter is…", "page": "https://rubric.hrih.si/editions/2026-10-26/58/", "card": "https://rubric.hrih.si/og/2026-10-26/58.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.43, "judge_a": 0.3, "judge_b": 0.5, "mean": 0.41, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6433, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.423, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.4144, "spread": 0.023 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.505, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.635, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.877, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7856, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8675, "spread": 0.193 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.84, "spread": 0.38 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.0267, "spread": 0.01 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.1, "judge_b": 0.22, "mean": 0.2067, "spread": 0.2 } } }, { "rank": 59, "id": "st_20261026_2912", "title": "eMudhra faces a bigger test of public trust after security lapse", "url": "https://m.economictimes.com/ai/ai-insights/emudhra-faces-a-bigger-test-of-public-trust-after-security-lapse/articleshow/135165282.cms", "domain": "m.economictimes.com", "site": "The Economic Times", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-25T05:03:45.000Z", "jev": 0.5149, "judge_a": 0.4425, "judge_b": 0.7099, "score": 0.5558, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "eMudhra is exiting its global SSL/TLS website certificate business after an anonymous researcher found weak private keys tied to eight certificate authorities, which browsers including Chrome and Firefox then blocked. The flaw went undetected for about 18 months and two audit cycles.", "page": "https://rubric.hrih.si/editions/2026-10-26/59/", "card": "https://rubric.hrih.si/og/2026-10-26/59.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5733, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7233, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.433, "judge_a": 0.4, "judge_b": 0.64, "mean": 0.4911, "spread": 0.24 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.545, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6083, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.42, "judge_a": 0.45, "judge_b": 0.74, "mean": 0.5367, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.573, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5142, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.76, "judge_a": 0.7, "judge_b": 0.35, "mean": 0.6033, "spread": 0.41 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0967, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.42, "judge_a": 0.25, "judge_b": 0.2, "mean": 0.29, "spread": 0.22 } } }, { "rank": 60, "id": "st_20261026_3084", "title": "Iran's January 2026 Internet Shutdown: Public Data and Censorship Methods", "url": "https://arxiv.org/abs/2603.28753", "domain": "arxiv.org", "site": "arXiv", "categories": [ "security", "it", "policy" ], "category_names": [ "Security", "IT & networking", "Policy & regulation" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-25T12:31:48.000Z", "jev": 0.4414, "judge_a": 0.474, "judge_b": 0.7476, "score": 0.5544, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "This paper analyzes Iran's January 2026 Internet shutdown during nationwide protests, drawing on grey literature from network monitoring initiatives and private transit-provider measurements. It examines how the disruption unfolded, how it was detected and whether it was foreseeable, the resulting post-shutdown control regime, and how censorship-circumvention tools performed.", "page": "https://rubric.hrih.si/editions/2026-10-26/60/", "card": "https://rubric.hrih.si/og/2026-10-26/60.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.525, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6483, "spread": 0.295 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.41, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.6367, "spread": 0.49 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.453, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.4911, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.35, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.4433, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.753, "judge_a": 0.75, "judge_b": 0.68, "mean": 0.7278, "spread": 0.073 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.81, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.73, "spread": 0.21 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.96, "judge_a": 0.4, "judge_b": 0.97, "mean": 0.7767, "spread": 0.57 }, "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": 0.38, "judge_a": 0.2, "judge_b": 0.3, "mean": 0.2933, "spread": 0.18 } } }, { "rank": 61, "id": "st_20261026_3100", "title": "Can machines be war criminals?", "url": "https://www.vox.com/future-perfect/507570/ai-weapons-war-crime-responsibility", "domain": "vox.com", "site": "Vox", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T13:01:23.000Z", "jev": 0.4508, "judge_a": 0.528, "judge_b": 0.684, "score": 0.5543, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "A Future Perfect interview with Stanford international law expert Allen Weiner examines who bears criminal responsibility when AI-assisted or autonomous weapons contribute to war crimes.", "page": "https://rubric.hrih.si/editions/2026-10-26/61/", "card": "https://rubric.hrih.si/og/2026-10-26/61.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.6, "judge_b": 0.74, "mean": 0.615, "spread": 0.235 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.69, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.6, "judge_b": 0.65, "mean": 0.6678, "spread": 0.153 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.365, "judge_a": 0.4, "judge_b": 0.57, "mean": 0.445, "spread": 0.205 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.287, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.4689, "spread": 0.333 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.338, "judge_a": 0.25, "judge_b": 0.38, "mean": 0.3225, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.8167, "spread": 0.33 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0467, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.41, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2633, "spread": 0.23 } } }, { "rank": 62, "id": "st_20261026_3184", "title": "If AI Can Copy Photoshop, Why Would Anyone Build the Next Figma?", "url": "https://www.vincentschmalbach.com/if-ai-can-copy-photoshop-why-build-the-next-figma/", "domain": "vincentschmalbach.com", "site": "Vincent Schmalbach", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-25T15:20:51.000Z", "jev": 0.5869, "judge_a": 0.4415, "judge_b": 0.625, "score": 0.551, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": null, "topic_penalty": null, "repeat_penalty": null, "summary": "Brandon Thomas released PhotoCraft, a free, early-alpha, AI-built clone of Photoshop, and seven other Adobe apps, which quickly drew over 72,000 GitHub stars. The piece asks whether cheap AI cloning makes building new software more or less likely.", "page": "https://rubric.hrih.si/editions/2026-10-26/62/", "card": "https://rubric.hrih.si/og/2026-10-26/62.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5742, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7133, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.877, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7656, "spread": 0.177 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.538, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5225, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.5, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4667, "spread": 0.2 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.438, "judge_a": 0.15, "judge_b": 0.28, "mean": 0.2892, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.5, "judge_b": 0.93, "mean": 0.7833, "spread": 0.43 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1633, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.35, "judge_b": 0.24, "mean": 0.2967, "spread": 0.11 } } } ] }