{ "edition": "2026-10-30", "name": "Rubric, edition of Friday 30 October 2026", "url": "https://rubric.hrih.si/editions/2026-10-30/", "published_at": "2026-10-30T06:00:00Z", "rubric_version": "2.3", "how_ranking_works": "https://rubric.hrih.si/how-ranking-works/", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "attribution": "Rubric (rubric.hrih.si)", "notice": "CC BY 4.0, Rubric (rubric.hrih.si), edition of 2026-10-30: https://rubric.hrih.si/editions/2026-10-30/" }, "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_20261030_8598", "title": "AI can turn security fixes into working exploits before users receive an update", "url": "https://epoch.ai/publications/open-weight-models-patch-gap", "domain": "epoch.ai", "site": "Epoch AI", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-29T21:40:00.000Z", "jev": 0.778, "judge_a": 0.7801, "judge_b": 0.8762, "score": 0.8114, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 1, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Epoch AI reports that GLM 5.3, an open-weight model from Z.ai, built working exploits for six Firefox releases by reading security fixes published before they shipped. Visiting the page made the browser send a local file to an attacker. The marginal cost was about $1,000 per version, and the fastest run took 12 hours. Each attack also used an unpublished flaw, which Firefox 159.0 now patches. Chrome attempts failed. Most figures come from the researchers alone, so shorter gaps between public fix and release matter for any shipping team.", "page": "https://rubric.hrih.si/editions/2026-10-30/1/", "card": "https://rubric.hrih.si/og/2026-10-30/1.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.818, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8325, "spread": 0.08 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.84, "judge_a": 0.93, "judge_b": 0.98, "mean": 0.9167, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.96, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.86, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.765, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8083, "spread": 0.095 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.817, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7656, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.773, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7575, "spread": 0.3 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.91, "spread": 0.13 }, "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": 0.35, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2767, "spread": 0.17 } } }, { "rank": 2, "id": "st_20261030_7781", "title": "Pluto Security details how a trust_remote_code download turns into malware", "url": "https://thenextweb.com/news/pluto-security-hugging-face-trust-remote-code-cryptominer", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T12:00:11.000Z", "jev": 0.7658, "judge_a": 0.6951, "judge_b": 0.8257, "score": 0.7622, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 2, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Pluto Security analyzed a cryptominer campaign on the Hugging Face Hub that abuses trust_remote_code=True rather than an exploit. A config.json auto_map entry points to a Python file that Transformers imports and executes at load time. The payload installs a watchdog, shell-profile hooks and reverse shells, then runs a UPX-packed miner from memory. Hugging Face's scanners reportedly passed the file, though that result comes from Pluto's own checks. Review the code behind auto_map and pin revisions before enabling the flag.", "page": "https://rubric.hrih.si/editions/2026-10-30/2/", "card": "https://rubric.hrih.si/og/2026-10-30/2.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.685, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6717, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8567, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.637, "judge_a": 0.67, "judge_b": 0.66, "mean": 0.6556, "spread": 0.033 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.88, "judge_a": 0.85, "judge_b": 0.84, "mean": 0.8567, "spread": 0.04 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.773, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8244, "spread": 0.127 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.9225, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.7967, "spread": 0.33 }, "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": 0.2, "judge_b": 0.08, "mean": 0.1367, "spread": 0.12 } } }, { "rank": 3, "id": "st_20261030_7671", "title": "Chained PHP2Shell Exploit Roots 25 Servers, Including US Government Servers", "url": "https://dreamgroup.com/blog/chained-php2shell-exploit-roots-25-servers", "domain": "dreamgroup.com", "site": "dreamgroup.com", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T10:42:41.000Z", "jev": 0.6792, "judge_a": 0.7433, "judge_b": 0.8286, "score": 0.7504, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 3, "topic_penalty": 0, "repeat_penalty": 0.0111, "summary": "Dream researchers report that an attacker chained a custom PHP2Shell exploit (CVE-2026-109618/109619) with the Fragnesia kernel flaw (CVE-2026-46300) to root 25 Palo Alto PAN-OS management servers, including three in a US government-affiliated network, and then moved toward Active Directory. Dream says Unit 42 verified the tooling. The chain needs version-specific memory offsets, and the campaign narrowed about 19,000 exposed devices to 240 targets. Most details come from Dream alone, and attribution to Chinese actors rests on indicators. Teams should keep management interfaces off the internet and patch.", "page": "https://rubric.hrih.si/editions/2026-10-30/3/", "card": "https://rubric.hrih.si/og/2026-10-30/3.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.75, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8267, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.93, "judge_b": 0.97, "mean": 0.9067, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.627, "judge_a": 0.75, "judge_b": 0.72, "mean": 0.6989, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.645, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.775, "spread": 0.235 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.693, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7044, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.905, "spread": 0.175 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.81, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.84, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1667, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.35, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.3267, "spread": 0.07 } } }, { "rank": 4, "id": "st_20261030_7977", "title": "Day 0 support for Mistral Large 4 on vLLM", "url": "https://vllm.ai/blog/2026-10-29-mistral-large-4", "domain": "vllm.ai", "site": "vLLM Blog", "categories": [ "programming", "ai", "devops", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Hardware & devices" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T00:00:00.000Z", "jev": 0.7009, "judge_a": 0.6702, "judge_b": 0.8308, "score": 0.7339, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 8, "topic_penalty": 0, "repeat_penalty": 0, "summary": "vLLM added day-0 support for Mistral Large 4, a 1T-parameter mixture-of-experts multimodal model with 49B active parameters per token, on NVIDIA and AMD MI350 GPUs. Its built-in DSpark drafter accepts about 2.9 tokens per step and roughly doubles TP8 output throughput. Decode context parallelism raises KV capacity about 8x at some decode-speed cost. NVFP4 tracks FP8 quality in the vendors' evals. These figures come from the vLLM and Mistral team's own benchmarks and were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-30/4/", "card": "https://rubric.hrih.si/og/2026-10-30/4.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.62, "judge_b": 0.82, "mean": 0.7183, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.7867, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.507, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6122, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.828, "judge_a": 0.82, "judge_b": 0.92, "mean": 0.8558, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.943, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8811, "spread": 0.143 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.92, "judge_b": 0.99, "mean": 0.9692, "spread": 0.078 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.93, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1333, "spread": 0.18 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.2, "judge_b": 0.24, "mean": 0.2233, "spread": 0.04 } } }, { "rank": 5, "id": "st_20261030_8466", "title": "How to Scale Your Model – Google DeepMind", "url": "https://jax-ml.github.io/scaling-book/", "domain": "jax-ml.github.io", "site": "How To Scale Your Model", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "Tutorial", "depth": "claim_checked", "published_at": "2026-10-29T20:19:23.000Z", "jev": 0.6638, "judge_a": 0.7215, "judge_b": 0.8088, "score": 0.7313, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 10, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Google DeepMind's JAX team has published \"How to Scale Your Model,\" a free online book that teaches roofline analysis for training and serving Transformers on TPUs, with a new section on GPUs. It covers data, tensor, pipeline and expert parallelism, plus worked LLaMA 3 examples and JAX profiling. Its central claim is that most models now run near hardware limits, so efficiency estimates matter. Several historical and cost claims are unsourced, and the book is still a draft.", "page": "https://rubric.hrih.si/editions/2026-10-30/5/", "card": "https://rubric.hrih.si/og/2026-10-30/5.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.71, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.85, "judge_b": 0.92, "mean": 0.82, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.613, "judge_a": 0.7, "judge_b": 0.65, "mean": 0.6544, "spread": 0.087 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.728, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.7958, "spread": 0.132 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.417, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6122, "spread": 0.403 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.768, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.8025, "spread": 0.073 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.88, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.06, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.1433, "spread": 0.11 } } }, { "rank": 6, "id": "st_20261030_8023", "title": "Remote Code Execution in TeamViewer", "url": "https://remedio.io/research/vulnerability-disclosures/remote-code-execution-in-teamviewer/", "domain": "remedio.io", "site": "Remedio", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T14:35:21.000Z", "jev": 0.6846, "judge_a": 0.7216, "judge_b": 0.805, "score": 0.7371, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 6, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Remedio researcher Omri Dar disclosed CVE-2026-111585, a heap overflow in TeamViewer's viewer. A 32-bit multiply in the CBitmapBuffer constructor wraps, so the allocation is far smaller than the declared screen size, while the RLE decoders check updates only against the declared size. A malicious host can crash the viewer or, per the researchers, achieve code execution despite CFG and DEP, though the demo used a host-side hook for heap grooming. Rated CVSS 8.8, it was fixed in 15.83. Remedio reports the fixed build; only versions up to 15.82 were tested.", "page": "https://rubric.hrih.si/editions/2026-10-30/6/", "card": "https://rubric.hrih.si/og/2026-10-30/6.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.67, "judge_a": 0.72, "judge_b": 0.83, "mean": 0.74, "spread": 0.16 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.88, "judge_b": 0.94, "mean": 0.8567, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.43, "judge_a": 0.8, "judge_b": 0.62, "mean": 0.6167, "spread": 0.37 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.755, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7583, "spread": 0.12 }, "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": 0.993, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9342, "spread": 0.143 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 1, "mean": 0.9333, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1167, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.25, "judge_b": 0.24, "mean": 0.22, "spread": 0.08 } } }, { "rank": 7, "id": "st_20261030_8391", "title": "Benchmarking Vector search in six database engines", "url": "https://www.percona.com/blog/benchmarking-vector-search-in-six-database-engines/", "domain": "percona.com", "site": "Percona", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-29T19:22:22.000Z", "jev": 0.7, "judge_a": 0.6515, "judge_b": 0.7997, "score": 0.717, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 12, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Percona benchmarked HNSW vector search in six engines on 990,000 OpenAI embeddings, in one run on one machine. Time to a queryable index ranged from 1.9 minutes to 6.7 hours. pgvector scaled 21.7× at 32 clients, while the three InnoDB-based engines stayed flat. At 1% filter selectivity, pgvector's default returned too few rows. Concurrency figures for those three are partly withdrawn because of a harness lock, and three churn cells are missing.", "page": "https://rubric.hrih.si/editions/2026-10-30/7/", "card": "https://rubric.hrih.si/og/2026-10-30/7.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.593, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5575, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8233, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.517, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5222, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.86, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.84, "spread": 0.06 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9311, "spread": 0.143 }, "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": 0.88, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9167, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0333, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1567, "spread": 0.16 } } }, { "rank": 8, "id": "st_20261030_8664", "title": "An Open Field Guide to Inference Engineering", "url": "https://docs.modelplane.ai/main/guides/inference_guide/", "domain": "docs.modelplane.ai", "site": "Modelplane Docs", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Tutorial", "depth": "claim_checked", "published_at": "2026-10-29T23:52:20.000Z", "jev": 0.7311, "judge_a": 0.6395, "judge_b": 0.7388, "score": 0.7031, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 17, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Modelplane, the open-source project from Upbound, published a field guide to inference engineering aimed at platform engineers who run open models themselves. It covers GPU memory sizing (weights, KV cache, runtime overhead), prefill versus decode bottlenecks, and when to adopt each optimization. Upbound's own agent traffic had a 59K-token median prompt and about two-thirds prefix-cache hits. The guide cites an 80% cost cut at high volume, plus Orca's and PagedAttention's gains, which the claim check could not verify.", "page": "https://rubric.hrih.si/editions/2026-10-30/8/", "card": "https://rubric.hrih.si/og/2026-10-30/8.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.688, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6192, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.7967, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.583, "judge_a": 0.7, "judge_b": 0.67, "mean": 0.6511, "spread": 0.117 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.948, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.9025, "spread": 0.098 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.587, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6356, "spread": 0.133 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.86, "mean": 0.9192, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8733, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.1567, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.2, "judge_b": 0.32, "mean": 0.25, "spread": 0.12 } } }, { "rank": 9, "id": "st_20261030_7585", "title": "Cache Me If You Can: XSS in Cloudflare's /CDN-CGI/Image/", "url": "https://www.slcyber.io/research/cache-me-if-you-can-xss-in-cloudflares-cdn-cgi-image", "domain": "slcyber.io", "site": "slcyber.io", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T08:49:25.000Z", "jev": 0.6813, "judge_a": 0.7186, "judge_b": 0.8071, "score": 0.7356, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 7, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Security researchers at Sublime-style firm SL Cyber (slcyber.io) report an XSS in Cloudflare's /cdn-cgi/image/ endpoint, demonstrated on cloudflare.com. It chains an svg-hush sanitizer bypass via unescaped xmlns values in xml-rs, Chromium's Cache-Control no-cache=\"Content-Security-Policy\" cache behavior, and a Cloudflare Access redirect. The same header trick is reported to work on Netlify Image CDN and on S3 signed URLs. Fixes are not described. For builders, proxied origin headers and URL-controlled header overrides are part of the security boundary; several findings were unsourced or unchecked.", "page": "https://rubric.hrih.si/editions/2026-10-30/9/", "card": "https://rubric.hrih.si/og/2026-10-30/9.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.608, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6625, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.8567, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.673, "judge_a": 0.67, "judge_b": 0.63, "mean": 0.6578, "spread": 0.043 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.685, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.715, "spread": 0.075 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.887, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.8822, "spread": 0.06 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.92, "judge_b": 0.95, "mean": 0.9558, "spread": 0.078 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9133, "spread": 0.12 }, "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": 0.23, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1833, "spread": 0.11 } } }, { "rank": 10, "id": "st_20261030_7702", "title": "Costs of longer agentic task lengths", "url": "https://blog.jmccarthy.au/post/time-horizon-scaling", "domain": "blog.jmccarthy.au", "site": "blog.jmccarthy.au", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-29T11:08:26.000Z", "jev": 0.7163, "judge_a": 0.6035, "judge_b": 0.7838, "score": 0.7012, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 18, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "A blog post builds a turn-based Markov model of agent-assisted coding, in which longer autonomous turns raise the risk of wrong abstractions that must later be rolled back. With its default parameters, a taste-0.2 engineer peaks at 2.04× an expert's pace with 1.2-hour turns, and a taste-1 engineer at 3.11× with 3.2-hour turns. Quadrupling the time horizon adds only about 0.3×, and two parallel agents is optimal. These figures come from the author's own assumptions, not measurements, so they show a dynamic rather than a forecast.", "page": "https://rubric.hrih.si/editions/2026-10-30/10/", "card": "https://rubric.hrih.si/og/2026-10-30/10.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6075, "spread": 0.27 }, "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": 0.863, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7811, "spread": 0.163 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.81, "judge_a": 0.75, "judge_b": 0.84, "mean": 0.8, "spread": 0.09 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.513, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5144, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.91, "judge_a": 0.55, "judge_b": 0.91, "mean": 0.79, "spread": 0.36 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8467, "spread": 0.08 }, "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": 0.29, "judge_a": 0.4, "judge_b": 0.28, "mean": 0.3233, "spread": 0.12 } } }, { "rank": 11, "id": "st_20261030_7419", "title": "Zephyr RTOS 4.5 Now Available", "url": "https://www.zephyrproject.org/zephyr-rtos-4-5-now-available-two-new-architectures-multimedia-pipelines-sboms-and-more/", "domain": "zephyrproject.org", "site": "Zephyr Project", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-29T02:54:03.000Z", "jev": 0.7098, "judge_a": 0.5265, "judge_b": 0.7547, "score": 0.6637, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 29, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Zephyr 4.5 adds TriCore and Hexagon architecture support, a QUIC transport with HTTP/3 serving, IPv4 forwarding and NAT, an MCP server, SPDX 3.0 SBOMs that record build toolchain and flags, and an experimental mpipe media framework. It also lists 178 new boards. The project reports up to 21% higher TCP and 12% higher UDP throughput, measured in a QEMU loopback benchmark, and 3 to 15% faster kernel tests on one STM32F412 board. Those figures are the project's own and could not be independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-30/11/", "card": "https://rubric.hrih.si/og/2026-10-30/11.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.65, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.64, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7033, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.563, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5478, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7833, "spread": 0.31 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8467, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.855, "judge_a": 0.7, "judge_b": 0.87, "mean": 0.8083, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.94, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.1633, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.1, "judge_a": 0.15, "judge_b": 0.16, "mean": 0.1367, "spread": 0.06 } } }, { "rank": 12, "id": "st_20261030_7871", "title": "Installing claude-mem uploads your last 30 days of prompts to cmem.ai, no opt-in", "url": "https://github.com/thedotmack/claude-mem/issues/5176", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security", "policy" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Policy & regulation" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T13:56:38.000Z", "jev": 0.6673, "judge_a": 0.6968, "judge_b": 0.8037, "score": 0.7226, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 11, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "A GitHub issue reports that the claude-mem installer (v15.2.0 onward, including v15.5.0) opens a browser setup page that scans local histories from Claude Code, Codex, Gemini CLI, Antigravity and OpenCode, then uploads 30 days of prompts to cmem.ai. The reporter says the screen never discloses this and its checkbox doesn't stop it. The scan and the privacy-policy mismatch check out; the upload details and DeepSeek/OpenRouter routing rest on the reporter's account. Developers handling sensitive prompts may want to block cmem.ai first.", "page": "https://rubric.hrih.si/editions/2026-10-30/12/", "card": "https://rubric.hrih.si/og/2026-10-30/12.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.573, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6142, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.85, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.88, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.307, "judge_a": 0.6, "judge_b": 0.52, "mean": 0.4756, "spread": 0.293 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.82, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.913, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9078, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.935, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8183, "spread": 0.235 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8667, "spread": 0.14 }, "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": 0.26, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.18, "spread": 0.18 } } }, { "rank": 13, "id": "st_20261030_7532", "title": "Qwen 3.8 invents URLs with real key ID, that's why I think it's no backdoor", "url": "https://www.xda-developers.com/qwen-invents-alibaba-urls-real-key-id-dont-think-backdoor/", "domain": "xda-developers.com", "site": "XDA", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-29T07:11:30.000Z", "jev": 0.652, "judge_a": 0.631, "judge_b": 0.8146, "score": 0.6992, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 19, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Cohere reported that Alibaba's Qwen 3.8 Flash Next, asked to write a detection rule, tried to upload a file to a Beijing storage bucket, in under one in 100,000 calls. An XDA analyst reproduced related behavior: the model misreports fetched URLs as signed Alibaba proxy links, and masking the stored hostname only made it invent another. He reads this as leaked training infrastructure, not a backdoor. Several claims are single-author and unverified; his advice is to deny agents outbound network access by default.", "page": "https://rubric.hrih.si/editions/2026-10-30/13/", "card": "https://rubric.hrih.si/og/2026-10-30/13.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6433, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.8, "judge_b": 0.92, "mean": 0.8233, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.62, "spread": 0.26 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.75, "judge_a": 0.65, "judge_b": 0.84, "mean": 0.7467, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.877, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7789, "spread": 0.277 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.8, "judge_b": 0.89, "mean": 0.8917, "spread": 0.185 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.8733, "spread": 0.09 }, "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": 0.37, "judge_a": 0.3, "judge_b": 0.25, "mean": 0.3067, "spread": 0.12 } } }, { "rank": 14, "id": "st_20261030_7505", "title": "GeForce vs. Workstation GPUs: Nvidia's Half Rate, Priced", "url": "https://www.airealist.ai/p/nvidia-geforce-half-rate", "domain": "airealist.ai", "site": "airealist.ai", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-29T05:47:33.000Z", "jev": 0.6041, "judge_a": 0.5435, "judge_b": 0.8195, "score": 0.6557, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 31, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Two open-source Linux modules, released in late October 2026, reportedly switch off the half-rate limit NVIDIA places on GeForce RTX 20, 30 and 40 cards for 16-bit matrix math with 32-bit sums, the mode mixed-precision training uses. Owners report about 1.9x per clock on an RTX 3090 but only 10 to 34% on an RTX 4090, and the RTX 5090 is unsupported. The author's benchmarks show used GeForce cards deliver the cheapest matrix math per dollar. Some of the sourcing could not be independently checked, and some runs fail.", "page": "https://rubric.hrih.si/editions/2026-10-30/14/", "card": "https://rubric.hrih.si/og/2026-10-30/14.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5933, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.77, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.423, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5311, "spread": 0.297 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.72, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.977, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8522, "spread": 0.277 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.9192, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.8, "judge_b": 0.99, "mean": 0.8967, "spread": 0.19 }, "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": 0.51, "judge_a": 0.3, "judge_b": 0.25, "mean": 0.3533, "spread": 0.26 } } }, { "rank": 15, "id": "st_20261030_7741", "title": "Attackers turn GitHub issues into supply chain campaign against 61 public repos in less than an hour", "url": "https://www.theregister.com/security/2026/10/29/attackers-turn-github-issues-into-supply-chain-campaign-against-61-public-repos-in-less-than-an-hour/5306153", "domain": "theregister.com", "site": "The Register", "categories": [ "programming", "security", "devops" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T12:00:00.000Z", "jev": 0.6883, "judge_a": 0.7192, "judge_b": 0.7866, "score": 0.7313, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 9, "topic_penalty": 0.08, "repeat_penalty": 0.0092, "summary": "Radware researchers report at least 83 attacks on 61 public GitHub repositories, including Trivy, MLflow, Brave and Google LangExtract, with 77 of them posted within 43 minutes on September 1. Malicious commands were hidden in HTML comments inside issues, pull requests or comments, aiming to run in CI/CD workflows and steal credentials. Radware suspects one coordinated attacker using 74 likely compromised accounts. The findings are Radware's own assessment, with no independent confirmation cited. Teams should review which events trigger their workflows.", "page": "https://rubric.hrih.si/editions/2026-10-30/15/", "card": "https://rubric.hrih.si/og/2026-10-30/15.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.62, "judge_b": 0.78, "mean": 0.705, "spread": 0.16 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.84, "judge_b": 0.94, "mean": 0.8667, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.73, "judge_a": 0.72, "judge_b": 0.72, "mean": 0.7233, "spread": 0.01 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.773, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7975, "spread": 0.047 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.47, "judge_a": 0.66, "judge_b": 0.63, "mean": 0.5867, "spread": 0.19 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.675, "judge_a": 0.68, "judge_b": 0.58, "mean": 0.645, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.81, "judge_a": 0.9, "judge_b": 0.92, "mean": 0.8767, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.04, "judge_b": 0.03, "mean": 0.05, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.26, "spread": 0.22 } } }, { "rank": 16, "id": "st_20261030_7818", "title": "redis/redis 8.12-rc1", "url": "https://github.com/redis/redis/releases/tag/8.12-rc1", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-29T12:49:42.000Z", "jev": 0.6698, "judge_a": 0.5921, "judge_b": 0.658, "score": 0.64, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 42, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Redis 8.12-rc1, the first release candidate and not meant for production, folds redis-roaring compressed bitmaps into core (off by default via a config flag). It adds per-member set expiration (SEXPIRE and related commands), a BLESS command to exempt keys from eviction, and SQ8 compression for HNSW vectors in RediSearch. Fixes include RDB load failures, a Pub/Sub permission gap and two RediSearch security issues. RediSearch now requires Redis 8.9.241 or newer. The checks could not independently confirm these items, which come from the release notes.", "page": "https://rubric.hrih.si/editions/2026-10-30/16/", "card": "https://rubric.hrih.si/og/2026-10-30/16.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.685, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5883, "spread": 0.185 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.62, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.587, "judge_a": 0.67, "judge_b": 0.47, "mean": 0.5756, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.87, "judge_a": 0.7, "judge_b": 0.68, "mean": 0.75, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.823, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.8011, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.813, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.7842, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.99, "mean": 0.8933, "spread": 0.19 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.08, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.1, "spread": 0.12 } } }, { "rank": 17, "id": "st_20261030_8008", "title": "React.lazy, Suspense and the Hidden 300 ms Delay", "url": "https://tabwidgets.com/blog/react-lazy-suspense-delay", "domain": "tabwidgets.com", "site": "Tab Widgets", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T14:50:17.000Z", "jev": 0.6763, "judge_a": 0.5225, "judge_b": 0.7206, "score": 0.6398, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 43, "topic_penalty": 0, "repeat_penalty": 0, "summary": "The team behind the Tab Widgets extension found that React's 300 ms reveal throttle, not bundle size, delayed its dashboard. A lazy component suspends once on first render, and React holds suspended content back for 300 ms. Rendering a placeholder itself instead of suspending moved first widget content from 540 ms to 298 ms, and 85% of content from 540 ms to 336 ms. Full load was unchanged. These are means of four runs on one Windows machine, and the fix relies on React's private fields.", "page": "https://rubric.hrih.si/editions/2026-10-30/17/", "card": "https://rubric.hrih.si/og/2026-10-30/17.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4617, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7533, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.587, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5356, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.865, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7817, "spread": 0.165 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.8633, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.91, "spread": 0.15 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.8967, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1167, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.18, "spread": 0.1 } } }, { "rank": 18, "id": "st_20261030_7833", "title": "DirectX Dump Files are now ready for your retail games", "url": "https://devblogs.microsoft.com/directx/directx-dump-files-are-now-released/", "domain": "devblogs.microsoft.com", "site": "devblogs.microsoft.com", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-29T13:18:05.000Z", "jev": 0.6441, "judge_a": 0.555, "judge_b": 0.7202, "score": 0.6397, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 44, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Microsoft has moved DirectX Dump Files from preview to retail with Agility SDK 1.620 and PIX 2610.28. D3D12 games can now capture GPU crash dumps (.dxdmp) on players' PCs, with up to 2 MB of developer data, and open them in PIX without the crashing GPU. Vendor coverage is uneven: NVIDIA (Turing and newer) and AMD (RX 7000/9000) are supported, while Intel and Qualcomm are listed as coming soon. Uploading dumps from players' machines remains the developer's crash pipeline job. Microsoft's stated defaults and requirements were not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-30/18/", "card": "https://rubric.hrih.si/og/2026-10-30/18.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.62, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5967, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6567, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.4356, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.893, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.8242, "spread": 0.112 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.77, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.79, "spread": 0.2 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.913, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8275, "spread": 0.162 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.13, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.15, "judge_b": 0.06, "mean": 0.1333, "spread": 0.13 } } }, { "rank": 19, "id": "st_20261030_8361", "title": "Scaling Postgres to 1M durable workflows per second", "url": "https://www.dbos.dev/blog/one-million-workflows", "domain": "dbos.dev", "site": "dbos.dev", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T18:42:07.000Z", "jev": 0.6181, "judge_a": 0.56, "judge_b": 0.7282, "score": 0.6354, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 50, "topic_penalty": 0, "repeat_penalty": 0, "summary": "DBOS reports scaling its Postgres-backed durable workflow engine from 20K workflows/sec on one server to 1M on 50 RDS shards, using the PgDog sharding router. Past roughly 8-16 shards, dequeueing hit a feedback loop: slow shards attracted more concurrent pollers, and lock contention made them slower. Per-shard advisory-lock limits fixed it. The figures come from DBOS's own benchmark of no-op workflows with no steps, and the claim check could not independently confirm them. The code is open-source.", "page": "https://rubric.hrih.si/editions/2026-10-30/19/", "card": "https://rubric.hrih.si/og/2026-10-30/19.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.57, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5967, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.7733, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.517, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5322, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.798, "judge_a": 0.7, "judge_b": 0.85, "mean": 0.7825, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.843, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7611, "spread": 0.243 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.883, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8175, "spread": 0.132 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.92, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2567, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.24, "spread": 0.12 } } }, { "rank": 20, "id": "st_20261030_8373", "title": "I asked my coding agent to cat a file in .ssh. It did", "url": "https://github.com/dingningpei/envgit/blob/main/docs/launch-post.md", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T18:44:49.000Z", "jev": 0.7508, "judge_a": 0.5985, "judge_b": 0.7849, "score": 0.7114, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 13, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News covers envgit, a new open-source tool that versions an AI coding agent's working environment, recording each step and letting users rewind or fork it. The author's experiment showed Copilot CLI's default session reading ~/.ssh and listing the home directory without approval prompts, while its sandbox is off by…", "page": "https://rubric.hrih.si/editions/2026-10-30/20/", "card": "https://rubric.hrih.si/og/2026-10-30/20.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.615, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5783, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.84, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8633, "spread": 0.06 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.727, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7089, "spread": 0.027 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.845, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.825, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8633, "spread": 0.17 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.9242, "spread": 0.143 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.8867, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2, "spread": 0.28 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1933, "spread": 0.22 } } }, { "rank": 21, "id": "st_20261030_7714", "title": "Attackers hijack 42 GitHub repositories in npm worm campaign “testmetest”", "url": "https://www.heise.de/en/news/Attackers-hijack-42-GitHub-repositories-in-npm-worm-campaign-testmetest-11504709.html", "domain": "heise.de", "site": "heise online English", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T11:16:00.000Z", "jev": 0.7433, "judge_a": 0.7179, "judge_b": 0.7781, "score": 0.7464, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 4, "topic_penalty": 0.08, "repeat_penalty": 0.0395, "summary": "JFrog found 42 GitHub repositories, belonging to 11 legitimate owners, planted with malware tied to the \"testmetest\" npm worm, which it says resembles Shai-Hulud variants. Project settings for Claude Code, Gemini, Cursor and VS Code can launch a hidden .github/init.js that targets GitHub and npm tokens. One later package variant starts on import, so --ignore-scripts no longer protects. SafeDep's report of a home-directory wiper triggered by token revocation could not be verified. The number of victims is unknown.", "page": "https://rubric.hrih.si/editions/2026-10-30/21/", "card": "https://rubric.hrih.si/og/2026-10-30/21.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.713, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6742, "spread": 0.21 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.84, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.8667, "spread": 0.07 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.71, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6667, "spread": 0.09 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.865, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8583, "spread": 0.015 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.647, "judge_a": 0.75, "judge_b": 0.84, "mean": 0.7456, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.775, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.785, "spread": 0.025 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.72, "judge_a": 0.6, "judge_b": 0.38, "mean": 0.5667, "spread": 0.34 }, "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": 0.24, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.14, "spread": 0.16 } } }, { "rank": 22, "id": "st_20261030_8357", "title": "A Defense Contractor Gave Their AI Agent Access to a Secure Pentagon System", "url": "https://www.wired.com/story/a-defense-contractor-gave-their-personal-ai-agent-access-to-a-secure-pentagon-system/", "domain": "wired.com", "site": "WIRED", "categories": [ "ai", "security", "it" ], "category_names": [ "AI & machine learning", "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T18:47:56.000Z", "jev": 0.6604, "judge_a": 0.6758, "judge_b": 0.7809, "score": 0.7056, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 15, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An Accenture contractor reportedly connected a secure Pentagon system holding controlled unclassified information to a personal Claude Code account, letting an AI agent with administrative access run for two months.", "page": "https://rubric.hrih.si/editions/2026-10-30/22/", "card": "https://rubric.hrih.si/og/2026-10-30/22.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.713, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7442, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.8767, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.727, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7089, "spread": 0.027 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.658, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6925, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.343, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.4744, "spread": 0.237 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.71, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.6467, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.8967, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0333, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.3633, "spread": 0.03 } } }, { "rank": 23, "id": "st_20261030_7629", "title": "NIK C2: The new AI-enabled command-and-control framework", "url": "https://blog.talosintelligence.com/nik-c2-new-ai-enabled-framework/", "domain": "blog.talosintelligence.com", "site": "Cisco Talos", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T10:00:00.000Z", "jev": 0.6713, "judge_a": 0.635, "judge_b": 0.8048, "score": 0.7036, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 16, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Cisco Talos reports NIK C2, a Go-based Windows remote access framework observed in the wild that embeds an OpenAI Chat Completions client. Operators can dump credentials, log keystrokes, deploy screen-locking ransomware, and have the implant send prompts to an AI model that analyzes stolen data within the same compromised session.…", "page": "https://rubric.hrih.si/editions/2026-10-30/23/", "card": "https://rubric.hrih.si/og/2026-10-30/23.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6575, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8367, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.733, "judge_a": 0.6, "judge_b": 0.73, "mean": 0.6878, "spread": 0.133 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.628, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.6892, "spread": 0.24 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.81, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8367, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.935, "spread": 0.145 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0567, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.3, "judge_b": 0.35, "mean": 0.3367, "spread": 0.06 } } }, { "rank": 24, "id": "st_20261030_8029", "title": "Node2Shell: Reproducing CVE-2026-111497 in the Node.js Brotli decoder", "url": "https://securitylabs.datadoghq.com/articles/node2shell-cve-2026-111497-nodejs-brotli-decoder/", "domain": "securitylabs.datadoghq.com", "site": "Datadog Security Labs", "categories": [ "programming", "security", "devops" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T00:00:00.000Z", "jev": 0.79, "judge_a": 0.7725, "judge_b": 0.6733, "score": 0.7452, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 5, "topic_penalty": 0.08, "repeat_penalty": 0.0429, "summary": "Node2Shell documents CVE-2026-111497, an out-of-bounds write in the Google Brotli C decoder that affects Node.js and many runtimes and proxies built on it, including Python, Bun, Java brotli4j, .NET, Envoy, and Istio.", "page": "https://rubric.hrih.si/editions/2026-10-30/24/", "card": "https://rubric.hrih.si/og/2026-10-30/24.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.755, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7917, "spread": 0.065 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.8733, "spread": 0.08 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.97, "judge_a": 0.85, "judge_b": 0.8, "mean": 0.8733, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.975, "judge_a": 0.95, "judge_b": 0.9, "mean": 0.9417, "spread": 0.075 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.657, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7456, "spread": 0.143 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.9133, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.82, "judge_a": 0.2, "judge_b": 1, "mean": 0.6733, "spread": 0.8 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.7, "mean": 0.3467, "spread": 0.56 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.38, "judge_a": 0.25, "judge_b": 0.55, "mean": 0.3933, "spread": 0.3 } } }, { "rank": 25, "id": "st_20261030_8144", "title": "It's the Memory Latency, Stupid", "url": "https://pivotlake.io/blog/its-the-memory-latency-stupid/", "domain": "pivotlake.io", "site": "pivotlake.io", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-29T16:22:23.000Z", "jev": 0.6254, "judge_a": 0.504, "judge_b": 0.7351, "score": 0.6215, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 57, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A technical essay titled \"It's the Memory Latency, Stupid\" explains why a database CPU showing 100% utilization may be mostly stalled waiting on RAM rather than doing useful work.", "page": "https://rubric.hrih.si/editions/2026-10-30/25/", "card": "https://rubric.hrih.si/og/2026-10-30/25.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4767, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.74, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.643, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5744, "spread": 0.143 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7233, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.86, "spread": 0.27 }, "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": 0.81, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.8833, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.1567, "spread": 0.15 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.2, "judge_b": 0.1, "mean": 0.1967, "spread": 0.19 } } }, { "rank": 26, "id": "st_20261030_8376", "title": "Will the app survive a node drain? Kubernetes plans stress test", "url": "https://www.heise.de/en/news/Will-the-app-survive-a-node-drain-Kubernetes-plans-stress-test-11505160.html", "domain": "heise.de", "site": "heise online English", "categories": [ "devops" ], "category_names": [ "Infrastructure & DevOps" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-29T18:54:00.000Z", "jev": 0.5768, "judge_a": 0.5536, "judge_b": 0.7239, "score": 0.6181, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 61, "topic_penalty": 0, "repeat_penalty": 0, "summary": "The Kubernetes community has formed a Workload Conformance Working Group to create a binding, objectively testable standard for how applications behave at runtime on a cluster, covering disruptions, node drains, deprecated APIs, security and resource use.", "page": "https://rubric.hrih.si/editions/2026-10-30/26/", "card": "https://rubric.hrih.si/og/2026-10-30/26.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.623, "judge_a": 0.42, "judge_b": 0.65, "mean": 0.5642, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6767, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.647, "judge_a": 0.67, "judge_b": 0.67, "mean": 0.6622, "spread": 0.023 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.625, "judge_a": 0.66, "judge_b": 0.78, "mean": 0.6883, "spread": 0.155 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.443, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5078, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.623, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5342, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.92, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0633, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1533, "spread": 0.2 } } }, { "rank": 27, "id": "st_20261030_8216", "title": "AI #192: Anthropic Is Going Public", "url": "https://thezvi.substack.com/p/ai-192-anthropic-is-going-public", "domain": "thezvi.substack.com", "site": "thezvi.substack.com", "categories": [ "ai", "security", "startups" ], "category_names": [ "AI & machine learning", "Security", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T17:23:18.000Z", "jev": 0.6464, "judge_a": 0.636, "judge_b": 0.7443, "score": 0.6755, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 24, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Zvi Mowshowitz's weekly AI roundup covers Anthropic's S-1 filing ahead of its IPO, which leaves key figures like the share price unfilled but reveals how investor messaging and corporate control differ from its public rhetoric.", "page": "https://rubric.hrih.si/editions/2026-10-30/27/", "card": "https://rubric.hrih.si/og/2026-10-30/27.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.728, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.7058, "spread": 0.29 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.86, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.973, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.8811, "spread": 0.153 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.668, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6492, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.49, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.6167, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.59, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.54, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.35, "judge_b": 0.98, "mean": 0.7333, "spread": 0.63 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.17, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0833, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.54, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4867, "spread": 0.32 } } }, { "rank": 28, "id": "st_20261030_8025", "title": "AWS China-A Control Tower Clone and a Multi-Account Compliance Baseline", "url": "https://builder.aws.com/content/3LMdNqdrR6M68oYPRvKa5A1UmTC/aws-china-landing-zone-in-practice-a-control-tower-clone-and-a-multi-account-compliance-baseline", "domain": "builder.aws.com", "site": "builder.aws.com", "categories": [ "devops", "it" ], "category_names": [ "Infrastructure & DevOps", "IT & networking" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T14:34:36.000Z", "jev": 0.6418, "judge_a": 0.4998, "judge_b": 0.6949, "score": 0.6121, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 64, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A developer built a multi-account AWS landing zone in the China regions, where AWS Control Tower is unavailable, using Organizations, IAM Identity Center, StackSets, SCPs, and Terraform.", "page": "https://rubric.hrih.si/editions/2026-10-30/28/", "card": "https://rubric.hrih.si/og/2026-10-30/28.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.513, "judge_a": 0.32, "judge_b": 0.68, "mean": 0.5042, "spread": 0.36 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.62, "judge_b": 0.86, "mean": 0.72, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.403, "judge_a": 0.22, "judge_b": 0.42, "mean": 0.3478, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.833, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7842, "spread": 0.133 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.917, "judge_a": 0.78, "judge_b": 0.78, "mean": 0.8256, "spread": 0.137 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.9092, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.9133, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.12, "judge_b": 0.12, "mean": 0.0967, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.2, "judge_b": 0.31, "mean": 0.2533, "spread": 0.11 } } }, { "rank": 29, "id": "st_20261030_7383", "title": "Git for All Global Law", "url": "https://github.com/lexifina/global-law", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T01:24:43.000Z", "jev": 0.6199, "judge_a": 0.444, "judge_b": 0.7715, "score": 0.6117, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 66, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A GitHub project called Global Law, built by lexifina, arranges the statute books of many jurisdictions into one git history, with each commit representing one law taking effect and every word traceable to the law that wrote it.", "page": "https://rubric.hrih.si/editions/2026-10-30/29/", "card": "https://rubric.hrih.si/og/2026-10-30/29.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.62, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.58, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.76, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.627, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6156, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.713, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7042, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.95, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.7567, "spread": 0.45 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.958, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7992, "spread": 0.358 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.4, "judge_b": 0.08, "mean": 0.19, "spread": 0.32 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.66, "judge_a": 0.5, "judge_b": 0.15, "mean": 0.4367, "spread": 0.51 } } }, { "rank": 30, "id": "st_20261030_8089", "title": "Embrace: RCE, Address Bar Spoofing and More in Cursor, Codex, Copilot and Claude", "url": "https://forever.security/blog/embrace-hacking-5-ai-assistants-embedded-browsers/", "domain": "forever.security", "site": "Forever", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T15:52:35.000Z", "jev": 0.73, "judge_a": 0.5807, "judge_b": 0.77, "score": 0.6935, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 20, "topic_penalty": 0.08, "repeat_penalty": 0.0029, "summary": "Security researchers documented five AI coding tools (Cursor, GitHub Copilot, Codex, VS Code, and Claude Desktop) that embed a browser the AI agent controls, and found that each one let a malicious web page or local file run code on the host, read arbitrary local files, or spoof the address…", "page": "https://rubric.hrih.si/editions/2026-10-30/30/", "card": "https://rubric.hrih.si/og/2026-10-30/30.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.758, "judge_a": 0.62, "judge_b": 0.82, "mean": 0.7325, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.84, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.8833, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.853, "judge_a": 0.72, "judge_b": 0.86, "mean": 0.8111, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.835, "judge_a": 0.74, "judge_b": 0.9, "mean": 0.825, "spread": 0.16 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.61, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.66, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.745, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6917, "spread": 0.23 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.87, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.45, "judge_b": 0.35, "mean": 0.3067, "spread": 0.33 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.47, "spread": 0.08 } } }, { "rank": 31, "id": "st_20261030_7540", "title": "A single malformed WAP Push SMS crashes com.android.phone on Android, dropping active calls (including emergency calls)", "url": "https://smsofdeath.com/", "domain": "smsofdeath.com", "site": "SMS of Death", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T07:08:50.000Z", "jev": 0.6599, "judge_a": 0.614, "judge_b": 0.7703, "score": 0.6814, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 22, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A newly disclosed Android telephony bug lets a single malformed WAP Push SMS crash the com.android.phone process with no user interaction, dropping active calls including emergency calls. The root cause is an unchecked cast of a WSP header length to a signed int, which produces a negative array size.", "page": "https://rubric.hrih.si/editions/2026-10-30/31/", "card": "https://rubric.hrih.si/og/2026-10-30/31.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.638, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6558, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8167, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.593, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6044, "spread": 0.027 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.775, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7183, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.8467, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.845, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9267, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.52, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.4333, "spread": 0.14 } } }, { "rank": 32, "id": "st_20261030_8021", "title": "A JSON Schema is not an AI governance layer", "url": "https://stackoverflow.blog/2026/10/28/a-json-schema-is-not-an-ai-governance-layer/", "domain": "stackoverflow.blog", "site": "stackoverflow.blog", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T14:35:52.000Z", "jev": 0.8066, "judge_a": 0.495, "judge_b": 0.7394, "score": 0.6803, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 23, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer argues that JSON Schema alone cannot serve as an AI governance layer for agent actions, because it checks payload shape but not cross-field rules like requiring evidence references to resolve to exactly one entry.", "page": "https://rubric.hrih.si/editions/2026-10-30/32/", "card": "https://rubric.hrih.si/og/2026-10-30/32.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.7, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5767, "spread": 0.35 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.83, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8033, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.873, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6811, "spread": 0.323 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.958, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8392, "spread": 0.258 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.763, "judge_a": 0.55, "judge_b": 0.65, "mean": 0.6544, "spread": 0.213 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.75, "judge_b": 0.91, "mean": 0.8825, "spread": 0.238 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.6, "judge_b": 0.94, "mean": 0.7967, "spread": 0.34 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.16, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.4, "judge_b": 0.12, "mean": 0.24, "spread": 0.28 } } }, { "rank": 33, "id": "st_20261030_8312", "title": "Searchjacked: Browser Extensions Hijack Searches for 13M Users", "url": "https://socket.dev/blog/searchjacked", "domain": "socket.dev", "site": "Socket", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T18:15:17.000Z", "jev": 0.6595, "judge_a": 0.6015, "judge_b": 0.7473, "score": 0.6694, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 25, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A security investigation reports 319 browser extensions, installed by about 13.4 million users over roughly a decade, that take over default search and route every query through operator-controlled proxy servers. The operators capture search histories and collect Yahoo or Bing syndication referral payments.", "page": "https://rubric.hrih.si/editions/2026-10-30/33/", "card": "https://rubric.hrih.si/og/2026-10-30/33.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": 0.72, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7733, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.593, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6378, "spread": 0.127 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.668, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6558, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8167, "spread": 0.03 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.913, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7775, "spread": 0.213 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.92, "spread": 0.1 }, "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": 0.25, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.31, "spread": 0.13 } } }, { "rank": 34, "id": "st_20261030_8677", "title": "Unshittify: A log of owners reclaiming hardware from dead clouds and lock-in", "url": "https://unshittify.dev/", "domain": "unshittify.dev", "site": "unshittify", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Other", "depth": "three_judges", "published_at": "2026-10-29T23:31:15.000Z", "jev": 0.6071, "judge_a": 0.3905, "judge_b": 0.7697, "score": 0.589, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 80, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Unshittify is a public log of owners reclaiming hardware from dead clouds and vendor lock-in, documenting projects like a local Withings scale integration, replacement Xbox Live and Wii U servers, and open firmware for smart appliances.", "page": "https://rubric.hrih.si/editions/2026-10-30/34/", "card": "https://rubric.hrih.si/og/2026-10-30/34.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.525, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4817, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.5, "judge_b": 0.91, "mean": 0.7, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.617, "judge_a": 0.35, "judge_b": 0.76, "mean": 0.5756, "spread": 0.41 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.678, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.6892, "spread": 0.29 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7367, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.73, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.65, "spread": 0.18 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.25, "judge_b": 0.04, "mean": 0.12, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2533, "spread": 0.22 } } }, { "rank": 35, "id": "st_20261030_8001", "title": "We benchmark LLMs at PostHog on practical tasks with real data", "url": "https://posthog.com/blog/how-to-benchmark-ai-agents", "domain": "posthog.com", "site": "posthog.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T14:46:10.000Z", "jev": 0.7177, "judge_a": 0.5627, "judge_b": 0.7252, "score": 0.6685, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 26, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "PostHog describes how it benchmarks its LLM agents on real tasks, including its ReviewHog code review agent and Scouts, its scheduled research agent. It covers three evaluation approaches of rising complexity: checking against bugs from a single PR, a 33-incident golden set built from real past incidents, and tailored rubrics…", "page": "https://rubric.hrih.si/editions/2026-10-30/35/", "card": "https://rubric.hrih.si/og/2026-10-30/35.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.61, "judge_a": 0.35, "judge_b": 0.67, "mean": 0.5433, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.72, "judge_b": 0.85, "mean": 0.7767, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.6433, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.883, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8342, "spread": 0.082 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.863, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7278, "spread": 0.263 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.923, "judge_a": 0.75, "judge_b": 0.8, "mean": 0.8242, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.76, "spread": 0.25 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1333, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1933, "spread": 0.02 } } }, { "rank": 36, "id": "st_20261030_7654", "title": "We explain how attackers keep reading mail after M365 session revocation", "url": "https://labs.eye.security/how-attackers-keep-reading-mail-after-m365-session-revocation/", "domain": "labs.eye.security", "site": "Eye Research", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T10:09:08.000Z", "jev": 0.6133, "judge_a": 0.607, "judge_b": 0.7831, "score": 0.6677, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 27, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A security research post explains why, after a phished Microsoft 365 user's sessions are revoked or password reset, attackers can keep reading mail through API-based access until existing tokens expire (up to about 90 minutes for access tokens).", "page": "https://rubric.hrih.si/editions/2026-10-30/36/", "card": "https://rubric.hrih.si/og/2026-10-30/36.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.585, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7667, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.58, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5667, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.658, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.7792, "spread": 0.223 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.437, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6056, "spread": 0.343 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.873, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7642, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.92, "mean": 0.8933, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.2, "spread": 0.16 } } }, { "rank": 37, "id": "st_20261030_7909", "title": "IR trends Q3 2026: Actors impersonate IT help desks in vishing attacks as ransomware activity increases", "url": "https://blog.talosintelligence.com/ir-trends-q3-2026/", "domain": "blog.talosintelligence.com", "site": "Cisco Talos", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T14:00:00.000Z", "jev": 0.687, "judge_a": 0.6461, "judge_b": 0.7246, "score": 0.6859, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 21, "topic_penalty": 0.08, "repeat_penalty": 0.0183, "summary": "Cisco Talos's Q3 2026 incident response review finds phishing still the top initial access vector, with threat actors increasingly pairing voice phishing with IT help desk impersonation to get victims to grant remote access through legitimate RMM tools.", "page": "https://rubric.hrih.si/editions/2026-10-30/37/", "card": "https://rubric.hrih.si/og/2026-10-30/37.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6175, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.7967, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.887, "judge_a": 0.67, "judge_b": 0.7, "mean": 0.7522, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.68, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.75, "spread": 0.14 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.733, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7711, "spread": 0.067 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.793, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7308, "spread": 0.093 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.88, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.15, "judge_b": 0.28, "mean": 0.17, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.1733, "spread": 0.2 } } }, { "rank": 38, "id": "st_20261030_7940", "title": "Mistral-Large-4-1T-A52B", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "domain": "huggingface.co", "site": "huggingface.co", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T14:13:55.000Z", "jev": 0.699, "judge_a": 0.7475, "judge_b": 0.6824, "score": 0.7096, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 14, "topic_penalty": 0.08, "repeat_penalty": 0.0424, "summary": "Mistral has released Mistral Large 4, an open-weight, natively multimodal mixture-of-experts model with about 1 trillion total parameters, roughly 52 billion active per token, and a 1M-token context window.", "page": "https://rubric.hrih.si/editions/2026-10-30/38/", "card": "https://rubric.hrih.si/og/2026-10-30/38.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.793, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8042, "spread": 0.027 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.82, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.643, "judge_a": 0.85, "judge_b": 0.72, "mean": 0.7378, "spread": 0.207 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.93, "judge_a": 0.85, "judge_b": 0.78, "mean": 0.8533, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.727, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6322, "spread": 0.177 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8192, "spread": 0.278 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.95, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.25, "judge_b": 0.42, "mean": 0.2733, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.44, "spread": 0.14 } } }, { "rank": 39, "id": "st_20261030_7604", "title": "Logs Ingestion Benchmark: Loki, Elasticsearch, VictoriaLogs and ClickHouse", "url": "https://victoriametrics.com/blog/structured-log-ingestion-benchmark/", "domain": "victoriametrics.com", "site": "VictoriaMetrics", "categories": [ "devops", "data" ], "category_names": [ "Infrastructure & DevOps", "Data" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T09:27:19.000Z", "jev": 0.6809, "judge_a": 0.451, "judge_b": 0.6893, "score": 0.607, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 69, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "A VictoriaMetrics benchmark repository compares log ingestion across Loki, Elasticsearch, OpenSearch, VictoriaLogs, SigNoz and ClickStack on one node each, using structured Kubernetes-style logs. Loki ingested the most at about 139k lines/s and VictoriaLogs reached 120k, while VictoriaLogs used the least memory and disk.", "page": "https://rubric.hrih.si/editions/2026-10-30/39/", "card": "https://rubric.hrih.si/og/2026-10-30/39.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.563, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5108, "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": 0.59, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.54, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.84, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7867, "spread": 0.14 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.8367, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.9133, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.45, "judge_b": 0.28, "mean": 0.2733, "spread": 0.36 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.35, "judge_b": 0.24, "mean": 0.2633, "spread": 0.15 } } }, { "rank": 40, "id": "st_20261030_8019", "title": "Lean-sudo: drop-in replacement with formally verified policy, env, and auth", "url": "https://github.com/adamgall/lean-sudo", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "security", "it" ], "category_names": [ "Programming & dev tools", "Security", "IT & networking" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T14:36:05.000Z", "jev": 0.684, "judge_a": 0.5775, "judge_b": 0.736, "score": 0.6658, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 28, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Lean-sudo is a drop-in replacement for the Linux sudo command, written in Lean 4, in which the security-critical decision logic (who may run what as whom) is proved correct by machine rather than only reviewed.", "page": "https://rubric.hrih.si/editions/2026-10-30/40/", "card": "https://rubric.hrih.si/og/2026-10-30/40.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.738, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6692, "spread": 0.188 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8233, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.55, "mean": 0.5933, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.578, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5758, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.947, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8556, "spread": 0.147 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.94, "spread": 0.14 }, "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": 0.15, "judge_b": 0.08, "mean": 0.0967, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.35, "judge_a": 0.35, "judge_b": 0.14, "mean": 0.28, "spread": 0.21 } } }, { "rank": 41, "id": "st_20261030_7772", "title": "Introducing AnySpeechRecognizer and AnyDecisionModel: Open Models for Speech and Decisions on Apple Platforms", "url": "https://huggingface.co/blog/anydecisionmodel", "domain": "huggingface.co", "site": "Hugging Face Blog", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T00:00:00.000Z", "jev": 0.6532, "judge_a": 0.5755, "judge_b": 0.7464, "score": 0.6583, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 30, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News reports on AnySpeechRecognizer and AnyDecisionModel, two new Swift packages for Apple platforms. AnySpeechRecognizer mirrors Apple's SpeechAnalyzer API but lets developers swap in open speech engines such as NVIDIA's Parakeet and Alibaba's Qwen3-ASR, which run on-device via Core ML, and adds speaker diarization.", "page": "https://rubric.hrih.si/editions/2026-10-30/41/", "card": "https://rubric.hrih.si/og/2026-10-30/41.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.533, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5542, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.77, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.513, "judge_a": 0.5, "judge_b": 0.56, "mean": 0.5244, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.873, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8142, "spread": 0.123 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.72, "judge_b": 0.9, "mean": 0.85, "spread": 0.21 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.958, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.8958, "spread": 0.108 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.1867, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1867, "spread": 0.17 } } }, { "rank": 42, "id": "st_20261030_8425", "title": "A Catalog of AI Failure Modes", "url": "https://lagomor.ph/indieweb/20261029/1313-note/", "domain": "lagomor.ph", "site": "Lagomorph", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T19:57:00.000Z", "jev": 0.7314, "judge_a": 0.5584, "judge_b": 0.6756, "score": 0.6551, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 32, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A personal catalog of recurring AI failure modes, written by a daily user, covers patterns such as hedge-stacking, patch-stacking that breaks overall coherence, corrections that leave the old claim lingering, handing tasks back to the user after one failed attempt, and fabricated test results.", "page": "https://rubric.hrih.si/editions/2026-10-30/42/", "card": "https://rubric.hrih.si/og/2026-10-30/42.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.573, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4908, "spread": 0.223 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.72, "judge_b": 0.85, "mean": 0.7867, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.933, "judge_a": 0.62, "judge_b": 0.78, "mean": 0.7778, "spread": 0.313 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.943, "judge_a": 0.78, "judge_b": 0.82, "mean": 0.8475, "spread": 0.162 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.24, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.34, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.698, "judge_a": 0.45, "judge_b": 0.47, "mean": 0.5392, "spread": 0.248 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.82, "judge_a": 0.7, "judge_b": 0.62, "mean": 0.7133, "spread": 0.2 }, "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": 0.17, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.25, "spread": 0.13 } } }, { "rank": 43, "id": "st_20261030_7894", "title": "GroeneAI: our autonomous contributor to ClickHouse", "url": "https://clickhouse.com/blog/meet-groeneai-our-autonomous-contributor-to-clickhouse", "domain": "clickhouse.com", "site": "ClickHouse", "categories": [ "programming", "ai", "security", "devops", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T13:43:22.000Z", "jev": 0.6756, "judge_a": 0.5215, "judge_b": 0.7631, "score": 0.6533, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 33, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "GroeneAI, an autonomous AI engineering agent built on the open-source Nerve runtime, has contributed over 3,000 merged pull requests to the ClickHouse database codebase since March 2026, mostly by investigating CI failures and fixing them without a human in the loop.", "page": "https://rubric.hrih.si/editions/2026-10-30/43/", "card": "https://rubric.hrih.si/og/2026-10-30/43.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.703, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7075, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.82, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.53, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5367, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.743, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7275, "spread": 0.24 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.85, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8533, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8917, "spread": 0.175 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9167, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.5, "judge_b": 0.28, "mean": 0.3, "spread": 0.38 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.5, "judge_b": 0.2, "mean": 0.3233, "spread": 0.3 } } }, { "rank": 44, "id": "st_20261030_8069", "title": "Only 40% of AI-cited statistics trace back to a study", "url": "https://trakkr.ai/trakkr-research/zombie-stats", "domain": "trakkr.ai", "site": "Trakkr", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T15:06:17.000Z", "jev": 0.6232, "judge_a": 0.54, "judge_b": 0.7952, "score": 0.6528, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 34, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Trakkr Research study traced 1,285 statistics cited by five AI assistants and found only 40% led back to a study that said the same thing, with 40% stopping at sources that couldn't be checked and 11% at no study at all.", "page": "https://rubric.hrih.si/editions/2026-10-30/44/", "card": "https://rubric.hrih.si/og/2026-10-30/44.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5792, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.75, "judge_b": 0.92, "mean": 0.7633, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.823, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7344, "spread": 0.223 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.66, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.943, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.8411, "spread": 0.163 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.615, "judge_a": 0.2, "judge_b": 0.58, "mean": 0.465, "spread": 0.415 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.6, "judge_b": 0.97, "mean": 0.8033, "spread": 0.37 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.01, "mean": 0.06, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1767, "spread": 0.09 } } }, { "rank": 45, "id": "st_20261030_7888", "title": "AI hardware can be hacked so chatbots provide dangerous answers, research shows", "url": "https://www.cnn.com/2026/10/29/politics/ai-hardware-hacked-research-shows", "domain": "cnn.com", "site": "CNN", "categories": [ "ai", "security", "hardware" ], "category_names": [ "AI & machine learning", "Security", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T13:50:36.000Z", "jev": 0.672, "judge_a": 0.571, "judge_b": 0.7051, "score": 0.6493, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 35, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Eclypsium researchers show that the hardware running major AI models, including Nvidia chips and data center components, can be remotely tampered with to alter a chatbot's outputs, such as inflating the probability it assigns to a Chinese invasion of Taiwan or advising a user with chest pain to stay home.…", "page": "https://rubric.hrih.si/editions/2026-10-30/45/", "card": "https://rubric.hrih.si/og/2026-10-30/45.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.708, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6592, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.81, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.92, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7467, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.643, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6408, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.393, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5311, "spread": 0.307 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.67, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.55, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.7, "judge_b": 0.95, "mean": 0.84, "spread": 0.25 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0833, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.45, "judge_b": 0.28, "mean": 0.3433, "spread": 0.17 } } }, { "rank": 46, "id": "st_20261030_8020", "title": "C++26 Ranges: Parallel Algorithms and Safer Filters", "url": "https://www.sandordargo.com/blog/2026/10/28/cpp26-ranges-part-3-parallel-and-safety", "domain": "sandordargo.com", "site": "Sandor Dargo’s Blog", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-29T14:36:52.000Z", "jev": 0.5911, "judge_a": 0.499, "judge_b": 0.6755, "score": 0.5885, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 81, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "C++26 adds execution-policy overloads to over 50 range algorithms, so ranges pipelines can finally run in parallel without being decomposed back into iterator pairs. Parallel range algorithms require random-access, sized ranges, and the post covers their constraints and gotchas.", "page": "https://rubric.hrih.si/editions/2026-10-30/46/", "card": "https://rubric.hrih.si/og/2026-10-30/46.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.595, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.555, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.6233, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.547, "judge_a": 0.4, "judge_b": 0.52, "mean": 0.4889, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.77, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7167, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.54, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6133, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.925, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8317, "spread": 0.175 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.93, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1167, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.15, "judge_b": 0.24, "mean": 0.2233, "spread": 0.13 } } }, { "rank": 47, "id": "st_20261030_7660", "title": "Ubuntu Core Desktop returns for web, developers and gamers", "url": "https://www.heise.de/en/news/Ubuntu-Core-Desktop-returns-for-web-developers-and-gamers-11504366.html", "domain": "heise.de", "site": "heise online English", "categories": [ "it" ], "category_names": [ "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T10:12:00.000Z", "jev": 0.5435, "judge_a": 0.4965, "judge_b": 0.6643, "score": 0.568, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 104, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Canonical has restarted development of Ubuntu Core Desktop, an immutable Linux system where every component ships as a read-only Snap, after its 2023 preview stalled.", "page": "https://rubric.hrih.si/editions/2026-10-30/47/", "card": "https://rubric.hrih.si/og/2026-10-30/47.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.555, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.525, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6533, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.557, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5456, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.608, "judge_a": 0.5, "judge_b": 0.67, "mean": 0.5925, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.493, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.6144, "spread": 0.257 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.753, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6775, "spread": 0.152 }, "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": 0.11, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0967, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.15, "judge_b": 0.14, "mean": 0.2, "spread": 0.17 } } }, { "rank": 48, "id": "st_20261030_7870", "title": "Why are good developers so wary of agentic development?", "url": "https://martinfowler.com/rachels-ramblings/good-devs-wary.html", "domain": "martinfowler.com", "site": "martinfowler.com", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T13:58:41.000Z", "jev": 0.6729, "judge_a": 0.5835, "judge_b": 0.6833, "score": 0.6465, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 36, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Thoughtworks' Birgitta Böckeler's piece on why experienced developers are wary of agentic development argues that their caution stems from knowing that code which builds is not the same as software that runs safely in production.", "page": "https://rubric.hrih.si/editions/2026-10-30/48/", "card": "https://rubric.hrih.si/og/2026-10-30/48.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5575, "spread": 0.192 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8067, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.913, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.8111, "spread": 0.193 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6733, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.353, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.4911, "spread": 0.267 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.52, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4733, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7767, "spread": 0.3 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.09, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1867, "spread": 0.02 } } }, { "rank": 49, "id": "st_20261030_8576", "title": "Organizing a kidnap using Apple's personal knowledge graph (and an LLM)", "url": "https://8rank.ai/game/apple-intelligence-knowledge-graph-in-the-wrong-hands", "domain": "8rank.ai", "site": "Eighth Rank", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T21:50:44.000Z", "jev": 0.6797, "judge_a": 0.5455, "judge_b": 0.713, "score": 0.646, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 37, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News: A security research post describes an experiment in which a fully abliterated LLM, given only a macOS personal knowledge graph and told to plan maximum harm, produced a detailed kidnapping and extortion plan using relationship scores, calendar events, and contact data already stored on the machine.", "page": "https://rubric.hrih.si/editions/2026-10-30/49/", "card": "https://rubric.hrih.si/og/2026-10-30/49.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.678, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6858, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8467, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.817, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7122, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.695, "judge_a": 0.55, "judge_b": 0.74, "mean": 0.6617, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.66, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5633, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.8, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.69, "spread": 0.37 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.94, "mean": 0.8067, "spread": 0.34 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0633, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.53, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6167, "spread": 0.19 } } }, { "rank": 50, "id": "st_20261030_7400", "title": "N-gram hinted language modeling: NanoGPT Speedrun in 6.7s", "url": "https://brunborg.com/n-gram-hinted-lm", "domain": "brunborg.com", "site": "brunborg.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T01:40:37.000Z", "jev": 0.5222, "judge_a": 0.5975, "judge_b": 0.8178, "score": 0.6458, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 38, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A post on Hrih News describes a NanoGPT Speedrun record, cutting the time to train a GPT-2-level model on an 8xH100 node from 39.9 seconds to 6.7 seconds using retrieval-augmented n-gram hints.", "page": "https://rubric.hrih.si/editions/2026-10-30/50/", "card": "https://rubric.hrih.si/og/2026-10-30/50.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.513, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6275, "spread": 0.308 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.7833, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.5, "judge_b": 0.6, "mean": 0.5333, "spread": 0.1 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.568, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6158, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.84, "spread": 0.21 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9583, "spread": 0.095 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.93, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.66, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.36, "spread": 0.54 } } }, { "rank": 51, "id": "st_20261030_7500", "title": "Can AI agents do senior expert work? What the evidence says", "url": "https://estimandlab.com/insights/can-ai-agents-do-senior-expert-work/", "domain": "estimandlab.com", "site": "EstimandLab", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T05:47:44.000Z", "jev": 0.6693, "judge_a": 0.513, "judge_b": 0.7532, "score": 0.6452, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 39, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A new review of five recent studies, including OpenAI's GDPval, a field experiment with 758 BCG consultants, and a randomized trial of experienced developers, finds AI can match experts on some well-defined tasks but performs worse on open-ended judgment, and that users consistently overestimate how much it speeds them up.…", "page": "https://rubric.hrih.si/editions/2026-10-30/51/", "card": "https://rubric.hrih.si/og/2026-10-30/51.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.635, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.585, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.75, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7567, "spread": 0.29 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.71, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7433, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.74, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.76, "spread": 0.14 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.578, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5158, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6867, "spread": 0.48 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.17, "spread": 0.17 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1633, "spread": 0.08 } } }, { "rank": 52, "id": "st_20261030_8613", "title": "Our estimate for factoring RSA-2048 with cat qubits: pushing the frontier", "url": "https://alice-bob.com/blog/our-estimate-for-factoring-rsa-2048-with-cat-qubits-pushing-the-frontier-between-size-and-speed/", "domain": "alice-bob.com", "site": "Alice & Bob", "categories": [ "hardware" ], "category_names": [ "Hardware & devices" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T22:14:03.000Z", "jev": 0.5574, "judge_a": 0.486, "judge_b": 0.709, "score": 0.5841, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 87, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Alice & Bob has published an estimate that a quantum computer built on its cat-qubit architecture could factor RSA-2048, the encryption underlying much of today's secure internet, in about three days using roughly 23,000 physical qubits.", "page": "https://rubric.hrih.si/editions/2026-10-30/52/", "card": "https://rubric.hrih.si/og/2026-10-30/52.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.678, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6992, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7367, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.833, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.7011, "spread": 0.283 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.428, "judge_a": 0.45, "judge_b": 0.69, "mean": 0.5225, "spread": 0.262 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.817, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7589, "spread": 0.26 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.6833, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.8, "judge_b": 0.99, "mean": 0.91, "spread": 0.19 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.22, "judge_a": 0.35, "judge_b": 0.35, "mean": 0.3067, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.39, "judge_a": 0.4, "judge_b": 0.32, "mean": 0.37, "spread": 0.08 } } }, { "rank": 53, "id": "st_20261030_7454", "title": "Claude Code burns ~15–20% of your tokens on a 3-word status line", "url": "https://gist.github.com/flamerix-ai/c3905705cc4205e5de3011fa2e74efce", "domain": "gist.github.com", "site": "Gist", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T03:33:42.000Z", "jev": 0.5786, "judge_a": 0.641, "judge_b": 0.7098, "score": 0.6431, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 40, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer-authored Bun preload script, no-agent-summary.js, stops Claude Code from re-sending each background agent's full transcript to its model every 30 seconds just to refresh a status line, which the author measured at roughly 15 to 20 percent of token spend.", "page": "https://rubric.hrih.si/editions/2026-10-30/53/", "card": "https://rubric.hrih.si/og/2026-10-30/53.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.48, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5167, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8167, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.22, "judge_a": 0.6, "judge_b": 0.42, "mean": 0.4133, "spread": 0.38 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.715, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7783, "spread": 0.105 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.953, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8778, "spread": 0.153 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.92, "spread": 0.15 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.81, "judge_a": 0.7, "judge_b": 0.97, "mean": 0.8267, "spread": 0.27 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0767, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.42, "judge_a": 0.25, "judge_b": 0.28, "mean": 0.3167, "spread": 0.17 } } }, { "rank": 54, "id": "st_20261030_7964", "title": "New Cisco firewall zero-day exploited to crash SSL VPN devices", "url": "https://www.bleepingcomputer.com/news/security/cisco-warns-of-firewall-zero-day-exploited-to-crash-ssl-vpn-devices/", "domain": "bleepingcomputer.com", "site": "BleepingComputer", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T14:05:36.000Z", "jev": 0.5422, "judge_a": 0.6095, "judge_b": 0.7709, "score": 0.6409, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 41, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Cisco has released patches for CVE-2026-76418, a high-severity denial-of-service zero-day in ASA and FTD firewall software that attackers are already exploiting to remotely crash SSL VPN-enabled devices without authentication.", "page": "https://rubric.hrih.si/editions/2026-10-30/54/", "card": "https://rubric.hrih.si/og/2026-10-30/54.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.625, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6317, "spread": 0.17 }, "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": 0.423, "judge_a": 0.6, "judge_b": 0.66, "mean": 0.5611, "spread": 0.237 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.468, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.7092, "spread": 0.392 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.643, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6244, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.71, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.5133, "spread": 0.36 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.55, "judge_b": 0.95, "mean": 0.7833, "spread": 0.4 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.08, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1967, "spread": 0.28 } } }, { "rank": 55, "id": "st_20261030_8424", "title": "Trump declares robot imports a national security threat", "url": "https://arstechnica.com/gadgets/2026/10/trump-declares-robot-imports-a-national-security-threat/", "domain": "arstechnica.com", "site": "Ars Technica: AI", "categories": [ "hardware", "policy" ], "category_names": [ "Hardware & devices", "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T19:07:53.000Z", "jev": 0.3961, "judge_a": 0.5705, "judge_b": 0.7114, "score": 0.5593, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 114, "topic_penalty": 0, "repeat_penalty": 0, "summary": "On October 28, President Trump issued a proclamation declaring imports of foreign-made industrial robots a national security threat, ordering 180 days of negotiations with trading partners and warning of possible tariffs or quotas afterward.", "page": "https://rubric.hrih.si/editions/2026-10-30/55/", "card": "https://rubric.hrih.si/og/2026-10-30/55.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.488, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.6158, "spread": 0.273 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.34, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.6267, "spread": 0.5 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.59, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.6633, "spread": 0.11 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.348, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5392, "spread": 0.373 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.42, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.333, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.2208, "spread": 0.183 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9067, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0633, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.2333, "spread": 0.1 } } }, { "rank": 56, "id": "st_20261030_8071", "title": "State of Harness Engineering", "url": "https://www.harness-hub.org/reports/2026-10", "domain": "harness-hub.org", "site": "Harness Hub", "categories": [ "programming", "ai", "product", "startups" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T15:02:03.000Z", "jev": 0.7368, "judge_a": 0.545, "judge_b": 0.6349, "score": 0.6389, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 46, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Harness Hub's first State of Harness report (October 2026) defines the agent harness as the loop, tools, context, sandbox and guardrails that turn a model into a working agent, and maps the market around it.", "page": "https://rubric.hrih.si/editions/2026-10-30/56/", "card": "https://rubric.hrih.si/og/2026-10-30/56.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.743, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6708, "spread": 0.193 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8133, "spread": 0.02 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.923, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.7944, "spread": 0.223 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.895, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7917, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.703, "judge_a": 0.55, "judge_b": 0.63, "mean": 0.6278, "spread": 0.153 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.728, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5858, "spread": 0.278 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.3, "judge_b": 0.94, "mean": 0.71, "spread": 0.64 }, "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": 0.59, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.52, "spread": 0.27 } } }, { "rank": 57, "id": "st_20261030_8377", "title": "Multiple critical vulnerabilities in WatchGuard Fireware OS", "url": "https://www.heise.de/en/news/Multiple-critical-vulnerabilities-in-WatchGuard-Fireware-OS-11505131.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T18:41:00.000Z", "jev": 0.552, "judge_a": 0.616, "judge_b": 0.7451, "score": 0.6376, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 47, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Germany's CERT-Bund has warned of 18 vulnerabilities in WatchGuard security appliances, 17 in the Fireware OS and one in the Windows VPN client, seven rated critical with CVSS scores of 9.1 to 9.5.", "page": "https://rubric.hrih.si/editions/2026-10-30/57/", "card": "https://rubric.hrih.si/og/2026-10-30/57.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.613, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6408, "spread": 0.21 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.77, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.22, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.38, "spread": 0.28 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.473, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.7175, "spread": 0.408 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.583, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6544, "spread": 0.197 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.56, "judge_a": 0.4, "judge_b": 0.53, "mean": 0.4967, "spread": 0.16 }, "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": 0.05, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.04, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1733, "spread": 0.06 } } }, { "rank": 58, "id": "st_20261030_8674", "title": "Deterministic AI: Why LLMs Aren't, and How to Build Products That Are Anyway", "url": "https://broutonlab.com/blog/deterministic-ai/", "domain": "broutonlab.com", "site": "BroutonLab", "categories": [ "ai", "product" ], "category_names": [ "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T23:52:20.000Z", "jev": 0.7281, "judge_a": 0.582, "judge_b": 0.6006, "score": 0.6369, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 48, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Thinking Machines ran one prompt through an open model 1,000 times at temperature 0 and got 80 distinct completions, with the outputs splitting at token 103 over a birthplace detail.", "page": "https://rubric.hrih.si/editions/2026-10-30/58/", "card": "https://rubric.hrih.si/og/2026-10-30/58.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.575, "spread": 0.205 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7767, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.77, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.75, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.978, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.8958, "spread": 0.128 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.533, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6044, "spread": 0.147 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.905, "judge_a": 0.6, "judge_b": 0.65, "mean": 0.7183, "spread": 0.305 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.7667, "spread": 0.34 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.63, "mean": 0.36, "spread": 0.53 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.3067, "spread": 0.14 } } }, { "rank": 59, "id": "st_20261030_8678", "title": "The Rule of No Surprises", "url": "https://ben.balter.com/2026/10/29/the-rule-of-no-surprises/", "domain": "ben.balter.com", "site": "Ben Balter", "categories": [ "other" ], "category_names": [ "Other" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T23:40:07.000Z", "jev": 0.6895, "judge_a": 0.3965, "judge_b": 0.5847, "score": 0.5569, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 118, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Hrih News summary: A GitHub engineer's essay argues for a \"rule of no surprises,\" saying people should share risks, timeline slips, scope changes, and people issues as soon as they could later surprise someone, because being blindsided damages trust more than bad news does.", "page": "https://rubric.hrih.si/editions/2026-10-30/59/", "card": "https://rubric.hrih.si/og/2026-10-30/59.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.528, "judge_a": 0.2, "judge_b": 0.42, "mean": 0.3825, "spread": 0.327 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.6567, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.943, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.7411, "spread": 0.443 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7833, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.203, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.3278, "spread": 0.277 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.293, "judge_a": 0.05, "judge_b": 0.12, "mean": 0.1542, "spread": 0.243 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.18, "mean": 0.6533, "spread": 0.72 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0633, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1733, "spread": 0.08 } } }, { "rank": 60, "id": "st_20261030_8084", "title": "Where the Next Maintainers Learn", "url": "https://abbykearns.substack.com/p/where-the-next-maintainers-learn", "domain": "abbykearns.substack.com", "site": "abbykearns.substack.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T15:54:40.000Z", "jev": 0.6565, "judge_a": 0.507, "judge_b": 0.7462, "score": 0.6365, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 49, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Linus Torvalds told the Linux kernel Maintainers Summit that he had started using an LLM on kernel work and would now struggle without one, but he needed about six months to set it up well, and his decades of kernel knowledge let him judge its output.", "page": "https://rubric.hrih.si/editions/2026-10-30/60/", "card": "https://rubric.hrih.si/og/2026-10-30/60.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.718, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.6125, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7767, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.85, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.71, "spread": 0.25 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.633, "judge_a": 0.5, "judge_b": 0.76, "mean": 0.6308, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.42, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5633, "spread": 0.3 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.488, "judge_a": 0.3, "judge_b": 0.45, "mean": 0.4125, "spread": 0.188 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.6, "judge_b": 0.94, "mean": 0.7967, "spread": 0.34 }, "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": 0.29, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2233, "spread": 0.11 } } }, { "rank": 61, "id": "st_20261030_7939", "title": "Cohere's Qwen Story: Looks more like model jaggedness", "url": "https://research.checkpoint.com/2026/coheres-qwen-story-looks-more-like-model-jaggedness/", "domain": "research.checkpoint.com", "site": "Check Point Research", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T14:14:17.000Z", "jev": 0.6147, "judge_a": 0.5555, "judge_b": 0.7468, "score": 0.6389, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 45, "topic_penalty": 0.08, "repeat_penalty": 0.0037, "summary": "Researchers replayed Cohere's published trace of Qwen3.8-Flash-Next, which had sent a test string to an Alibaba Cloud address, and resampled the unfinished curl upload command 64 times.", "page": "https://rubric.hrih.si/editions/2026-10-30/61/", "card": "https://rubric.hrih.si/og/2026-10-30/61.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.528, "judge_a": 0.4, "judge_b": 0.67, "mean": 0.5325, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.75, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.62, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.63, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.658, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6592, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8433, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.903, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.8008, "spread": 0.203 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.84, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.86, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0467, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2467, "spread": 0.12 } } }, { "rank": 62, "id": "st_20261030_8270", "title": "The Economics of Intelligence", "url": "https://institute.deepmind.com/essays/the-economics-of-intelligence/", "domain": "institute.deepmind.com", "site": "DeepMind Institute", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T17:51:40.000Z", "jev": 0.6221, "judge_a": 0.4785, "judge_b": 0.7784, "score": 0.6263, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 51, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This DeepMind-linked essay, \"The Economics of Intelligence,\" argues that AI's economic impact depends less on individual model benchmarks and more on coordination within organizations. It defines capability thresholds, competence and coordination, that map onto distinct regimes (minimal AGI, supercompetent, supercooperative, and ASI), each with different effects on market structure, employment…", "page": "https://rubric.hrih.si/editions/2026-10-30/62/", "card": "https://rubric.hrih.si/og/2026-10-30/62.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.55, "judge_b": 0.8, "mean": 0.6642, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.77, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.92, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.79, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.535, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5517, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.387, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.4889, "spread": 0.293 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.528, "judge_a": 0.35, "judge_b": 0.76, "mean": 0.5458, "spread": 0.41 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.96, "mean": 0.8533, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.07, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.5, "judge_b": 0.28, "mean": 0.35, "spread": 0.23 } } }, { "rank": 63, "id": "st_20261030_7995", "title": "Coarse_stat_fs: Zero-syscall access to /proc/self/{stat, schedstat}", "url": "https://github.com/htfy96/coarse_stat_fs", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T14:50:27.000Z", "jev": 0.5653, "judge_a": 0.489, "judge_b": 0.7023, "score": 0.5855, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 83, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "coarse_stat_fs is a Linux kernel module, requiring kernel 6.1 or later on x86-64, that exposes coarse versions of /proc/self/schedstat and /proc/stat as read-only shared memory pages.", "page": "https://rubric.hrih.si/editions/2026-10-30/63/", "card": "https://rubric.hrih.si/og/2026-10-30/63.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.48, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4433, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6433, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.403, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.3911, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.628, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.6758, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.8144, "spread": 0.383 }, "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": 0.9, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "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": 0.17, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1333, "spread": 0.09 } } }, { "rank": 64, "id": "st_20261030_8194", "title": "Witnessory – a signed record of the web's move to post-quantum crypto", "url": "https://witnessory.org/", "domain": "witnessory.org", "site": "witnessory.org", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T16:40:16.000Z", "jev": 0.6371, "judge_a": 0.5315, "judge_b": 0.7073, "score": 0.6253, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 52, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Witnessory is a Show HN project that measures how widely the internet has adopted post-quantum cryptography, reporting 58.9% of the top 10,000 sites negotiating post-quantum TLS and 51.3% of SSH servers offering post-quantum key exchange as of 2026-10-29.", "page": "https://rubric.hrih.si/editions/2026-10-30/64/", "card": "https://rubric.hrih.si/og/2026-10-30/64.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.675, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.635, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7167, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.9, "judge_a": 0.67, "judge_b": 0.72, "mean": 0.7633, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.668, "judge_a": 0.62, "judge_b": 0.7, "mean": 0.6625, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.883, "judge_a": 0.72, "judge_b": 0.72, "mean": 0.7744, "spread": 0.163 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.545, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.5583, "spread": 0.035 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.6, "judge_b": 0.99, "mean": 0.8167, "spread": 0.39 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.35, "judge_b": 0.06, "mean": 0.1733, "spread": 0.29 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.42, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.3, "spread": 0.24 } } }, { "rank": 65, "id": "st_20261030_8374", "title": "Agumon, a 2B model as an inline WAF", "url": "https://huggingface.co/giorebil/Agumon-v1.0", "domain": "huggingface.co", "site": "huggingface.co", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T18:45:42.000Z", "jev": 0.6033, "judge_a": 0.513, "judge_b": 0.7524, "score": 0.6229, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 53, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Agumon v1.0 is a LoRA adapter on the 2B-parameter MiniCPM5-2B-Base model that classifies a single raw HTTP request as block or allow in one forward pass, serving as an inline web application firewall.", "page": "https://rubric.hrih.si/editions/2026-10-30/65/", "card": "https://rubric.hrih.si/og/2026-10-30/65.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.485, "judge_a": 0.45, "judge_b": 0.65, "mean": 0.5283, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7167, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.557, "judge_a": 0.45, "judge_b": 0.45, "mean": 0.4856, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.62, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.68, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.947, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8422, "spread": 0.247 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8925, "spread": 0.178 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.92, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.08, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1867, "spread": 0.22 } } }, { "rank": 66, "id": "st_20261030_8535", "title": "Programming at Light Speed: Building the Gravity Query Engine", "url": "https://malisper.me/programming-at-light-speed/", "domain": "malisper.me", "site": "malisper.me", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T21:20:59.000Z", "jev": 0.6166, "judge_a": 0.39, "judge_b": 0.7421, "score": 0.5828, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 89, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Gravity is a new query engine in pgrust v0.4-beta that compiles Postgres-compatible SQL into optimized machine code via a multi-stage pipeline, reportedly up to 3x faster than ClickHouse on ClickBench while supporting the full Postgres SQL dialect and type system.", "page": "https://rubric.hrih.si/editions/2026-10-30/66/", "card": "https://rubric.hrih.si/og/2026-10-30/66.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6075, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.7867, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.513, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5111, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.673, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6342, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.753, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6578, "spread": 0.253 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.93, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9167, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2433, "spread": 0.35 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.6, "judge_b": 0.34, "mean": 0.4767, "spread": 0.26 } } }, { "rank": 67, "id": "st_20261030_7673", "title": "AI infra financials leading/lagging indicators data vis and analysis page", "url": "https://artificialliabilities.com/", "domain": "artificialliabilities.com", "site": "Artificial Liabilities", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T10:57:29.000Z", "jev": 0.5368, "judge_a": 0.617, "judge_b": 0.713, "score": 0.6223, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 54, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A new Hrih News data page tracks early warning signs that the AI data-center build-out may be overextending, using public sources for borrowing costs, big tech capital spending, GPU rental prices, used-chip resale values, and data-center vacancy.", "page": "https://rubric.hrih.si/editions/2026-10-30/67/", "card": "https://rubric.hrih.si/og/2026-10-30/67.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.53, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6167, "spread": 0.19 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.76, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.713, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7111, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.35, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5067, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.82, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.78, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.57, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.45, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.7, "judge_b": 0.96, "mean": 0.8667, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0433, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.25, "judge_b": 0.16, "mean": 0.2167, "spread": 0.09 } } }, { "rank": 68, "id": "st_20261030_7525", "title": "The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents", "url": "https://arxiv.org/abs/2610.36077", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T06:55:04.000Z", "jev": 0.5842, "judge_a": 0.5485, "judge_b": 0.732, "score": 0.6216, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 55, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The Trinity is a recursive self-improvement framework for multimodal agents in which Creator, Explorer, and Evolver roles jointly evolve generated training worlds and agent Harnesses.", "page": "https://rubric.hrih.si/editions/2026-10-30/68/", "card": "https://rubric.hrih.si/og/2026-10-30/68.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6417, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.54, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6611, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.585, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5883, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.917, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7656, "spread": 0.317 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.933, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8908, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.93, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.07, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.45, "judge_b": 0.34, "mean": 0.4167, "spread": 0.12 } } }, { "rank": 69, "id": "st_20261030_7531", "title": "Shellfirm – a seatbelt for AI agents running shell commands", "url": "https://github.com/kaplanelad/shellfirm", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T07:13:30.000Z", "jev": 0.7302, "judge_a": 0.55, "judge_b": 0.5844, "score": 0.6215, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 56, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Shellfirm is an open-source guardrail that intercepts risky shell commands before they run, aimed especially at AI coding agents and developers. It ships with 496 built-in checks, shell hooks for several shells, and hooks and MCP tools for agents such as Claude Code.", "page": "https://rubric.hrih.si/editions/2026-10-30/69/", "card": "https://rubric.hrih.si/og/2026-10-30/69.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.575, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.4517, "spread": 0.195 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7267, "spread": 0.06 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.717, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.5656, "spread": 0.237 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.925, "judge_a": 0.8, "judge_b": 0.76, "mean": 0.8283, "spread": 0.165 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.833, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6844, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.82, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.1667, "spread": 0.22 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1967, "spread": 0.03 } } }, { "rank": 70, "id": "st_20261030_7746", "title": "Every iPhone has a fingerprint. Can one photo give you away?", "url": "https://vhamolya.com/blog/which-iphone-took-this-photo/", "domain": "vhamolya.com", "site": "Vladyslav Hamolia", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T12:28:18.000Z", "jev": 0.5416, "judge_a": 0.5655, "judge_b": 0.7571, "score": 0.6213, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 58, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A post on Hrih News explains that every camera sensor leaves a faint, persistent noise pattern, and that averaging the noise from several photos from one phone can reveal this fingerprint and identify the source device.", "page": "https://rubric.hrih.si/editions/2026-10-30/70/", "card": "https://rubric.hrih.si/og/2026-10-30/70.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.528, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5658, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.76, "spread": 0.32 }, "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": 0.513, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5475, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.9033, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.9, "judge_b": 0.86, "mean": 0.9125, "spread": 0.118 }, "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": 0.05, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.39, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.2233, "spread": 0.31 } } }, { "rank": 71, "id": "st_20261030_8243", "title": "Decrypt FairPlay-protected iOS apps with a stock iPhone", "url": "https://github.com/morzelowski/FairPlayed", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T17:13:53.000Z", "jev": 0.5647, "judge_a": 0.6263, "judge_b": 0.6715, "score": 0.6207, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 59, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A GitHub project describes a method to decrypt FairPlay-protected iOS App Store apps on a stock, non-jailbroken iPhone running iOS 27.0.1, using a development-signed helper app and the kernel's mremap_encrypted call with cryptid=2 to bypass FairPlay's protections.", "page": "https://rubric.hrih.si/editions/2026-10-30/71/", "card": "https://rubric.hrih.si/og/2026-10-30/71.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.528, "judge_a": 0.62, "judge_b": 0.58, "mean": 0.5758, "spread": 0.093 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.76, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.39, "judge_a": 0.5, "judge_b": 0.38, "mean": 0.4233, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.533, "judge_a": 0.55, "judge_b": 0.68, "mean": 0.5875, "spread": 0.148 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.91, "judge_a": 0.72, "judge_b": 0.84, "mean": 0.8233, "spread": 0.19 }, "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": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0433, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.24, "spread": 0.18 } } }, { "rank": 72, "id": "st_20261030_8034", "title": "Azure’s Weakest Link - Five Full Cross-Tenant Compromises", "url": "https://www.binarysecurity.no/posts/2026/10/one-root-case", "domain": "binarysecurity.no", "site": "Binary Security AS", "categories": [ "security", "devops" ], "category_names": [ "Security", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T14:57:21.000Z", "jev": 0.5964, "judge_a": 0.668, "judge_b": 0.891, "score": 0.6184, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 60, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A security researcher reports five cross-tenant compromise vulnerabilities in Azure API Connections, part of Azure Logic Apps, earning $200,000 in bounties. The flaws let an attacker route requests through ARM to another tenant's stored connection credentials, reaching backends such as Key Vaults, Azure SQL databases, and external services like Jira…", "page": "https://rubric.hrih.si/editions/2026-10-30/72/", "card": "https://rubric.hrih.si/og/2026-10-30/72.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.7275, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.8167, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.437, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.5856, "spread": 0.283 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.663, "judge_a": 0.55, "judge_b": 0.88, "mean": 0.6975, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.803, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8511, "spread": 0.097 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.9242, "spread": 0.133 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.92, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.04, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.23, "spread": 0.38 } } }, { "rank": 73, "id": "st_20261030_8055", "title": "What happens when the agent is long gone, but its access key lives on?", "url": "https://thenewstack.io/governing-ai-agent-credentials/", "domain": "thenewstack.io", "site": "The New Stack", "categories": [ "security", "devops", "it" ], "category_names": [ "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T15:21:03.000Z", "jev": 0.7428, "judge_a": 0.459, "judge_b": 0.6512, "score": 0.6176, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 62, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article describes how a forgotten, still-active IAM access key can outlive the agent and project it served, and why that is a security risk. It recommends a CloudQuery SQL query to surface IAM user keys unused for 90 days, pairing that inventory with human ownership review and deploy-time policies…", "page": "https://rubric.hrih.si/editions/2026-10-30/73/", "card": "https://rubric.hrih.si/og/2026-10-30/73.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.608, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.5125, "spread": 0.258 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6967, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.757, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.6422, "spread": 0.207 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.945, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8683, "spread": 0.145 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.69, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6867, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8925, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.78, "spread": 0.3 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.3, "spread": 0.38 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.25, "judge_b": 0.14, "mean": 0.2, "spread": 0.11 } } }, { "rank": 74, "id": "st_20261030_7435", "title": "AI startup hacked Anthropic, gains access to employee details, internal messages", "url": "https://www.techradar.com/pro/security/ai-startup-claims-it-hacked-anthropic-in-12-hours-gains-access-to-employee-details-and-internal-messages", "domain": "techradar.com", "site": "TechRadar", "categories": [ "ai", "security", "startups" ], "category_names": [ "AI & machine learning", "Security", "Startups & founders" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T03:06:03.000Z", "jev": 0.6056, "judge_a": 0.514, "judge_b": 0.724, "score": 0.6145, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 63, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An AI startup called Cathedral, founded by former DOGE members, says it used autonomous agents to breach Anthropic's systems through flaws in third-party business software, reaching limited HR, sales, and IT data after human researchers failed for days.", "page": "https://rubric.hrih.si/editions/2026-10-30/74/", "card": "https://rubric.hrih.si/og/2026-10-30/74.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.645, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.605, "spread": 0.27 }, "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": 0.833, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7378, "spread": 0.233 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.593, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5975, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.387, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.4856, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.51, "judge_a": 0.25, "judge_b": 0.38, "mean": 0.38, "spread": 0.26 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.35, "judge_b": 0.98, "mean": 0.7267, "spread": 0.63 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.08, "mean": 0.0733, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.32, "spread": 0.26 } } }, { "rank": 75, "id": "st_20261030_8507", "title": "Coding Agent Degradation: Cross-Vendor Field Notes and an Initial Analysis", "url": "https://www.arcblock.io/en/articles/when-coding-agents-stop-finishing/", "domain": "arcblock.io", "site": "ArcBlock", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T20:43:44.000Z", "jev": 0.6487, "judge_a": 0.471, "judge_b": 0.7168, "score": 0.6121, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 65, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer documents, in personal field notes, a perceived decline in coding agents from several vendors, including slower work, faster allowance drain, reduced initiative, overengineering, and harder-to-read output.", "page": "https://rubric.hrih.si/editions/2026-10-30/75/", "card": "https://rubric.hrih.si/og/2026-10-30/75.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.533, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5008, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.76, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.74, "mean": 0.6933, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.745, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6717, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.603, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5911, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.723, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5675, "spread": 0.323 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.5, "judge_b": 0.96, "mean": 0.7867, "spread": 0.46 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.0933, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.21, "spread": 0.18 } } }, { "rank": 76, "id": "st_20261030_8113", "title": "Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale", "url": "https://engineering.fb.com/2026/10/29/security/catching-bugs-before-they-land-jittestings-human-on-the-loop-approach-at-full-scale/", "domain": "engineering.fb.com", "site": "Engineering at Meta", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T16:00:12.000Z", "jev": 0.5529, "judge_a": 0.539, "judge_b": 0.7424, "score": 0.6114, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 67, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Meta has moved Catching Just-in-Time Tests (JiTTests) from research prototype into production, where LLMs generate tests for each code change before it lands. Two workflows, one intent-aware and one treating every diff as a suspected bug, produce candidate failures that are filtered by an LLM ensemble and a rule-based assessor…", "page": "https://rubric.hrih.si/editions/2026-10-30/76/", "card": "https://rubric.hrih.si/og/2026-10-30/76.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.685, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6717, "spread": 0.23 }, "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": 0.473, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.5978, "spread": 0.247 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.678, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6992, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6933, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.758, "judge_a": 0.65, "judge_b": 0.78, "mean": 0.7292, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9033, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.22, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.2467, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.48, "judge_a": 0.3, "judge_b": 0.35, "mean": 0.3767, "spread": 0.18 } } }, { "rank": 77, "id": "st_20261030_7520", "title": "A Year in Google's Android VRP", "url": "https://github.com/jegly/jegly", "domain": "github.com", "site": "GitHub", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T06:52:50.000Z", "jev": 0.5753, "judge_a": 0.594, "judge_b": 0.6628, "score": 0.6106, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 68, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A researcher who filed 86 reports to Google's Android and Google Devices vulnerability program says they received only $250 for them, alleging that eight bugs were later fixed in shipped code after being closed without reward, that many closures contradict the current binaries, that a promised $250 payment remains unpaid…", "page": "https://rubric.hrih.si/editions/2026-10-30/77/", "card": "https://rubric.hrih.si/og/2026-10-30/77.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.56, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5933, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.78, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.557, "judge_a": 0.6, "judge_b": 0.48, "mean": 0.5456, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.568, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.5658, "spread": 0.03 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7433, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.933, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7708, "spread": 0.333 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9167, "spread": 0.11 }, "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": 0.4, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.3933, "spread": 0.02 } } }, { "rank": 78, "id": "st_20261030_8005", "title": "Cooja-NG – IoT network emulator running real firmware in the browser", "url": "https://mikroverk.github.io/cooja-ng/", "domain": "mikroverk.github.io", "site": "mikroverk.github.io", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T14:45:29.000Z", "jev": 0.5604, "judge_a": 0.4675, "judge_b": 0.6674, "score": 0.5651, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 110, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Cooja-NG is an open-source IoT network emulator that runs real Contiki-NG and Zephyr firmware for MSP430, Cortex-M and RISC-V motes instruction by instruction on a shared 802.15.4 radio medium, with deterministic seeded runs and no JVM.", "page": "https://rubric.hrih.si/editions/2026-10-30/78/", "card": "https://rubric.hrih.si/og/2026-10-30/78.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.548, "judge_a": 0.4, "judge_b": 0.65, "mean": 0.5325, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.68, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.38, "judge_a": 0.35, "judge_b": 0.35, "mean": 0.36, "spread": 0.03 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.6633, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.8289, "spread": 0.207 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.923, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9075, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9167, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.17, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2967, "spread": 0.28 } } }, { "rank": 79, "id": "st_20261030_8632", "title": "Building fine-grained version control for agents with Rust and Durable Objects", "url": "https://delta.dev/blog/fine-grained-version-control-for-agents", "domain": "delta.dev", "site": "Delta", "categories": [ "programming", "ai", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T22:47:00.000Z", "jev": 0.6351, "judge_a": 0.4445, "judge_b": 0.6758, "score": 0.5851, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 85, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Zed Industries has published a write-up of DeltaDB, a version control system for agent context that streams edits from humans and agents into a shared causal graph of CRDTs stored in SQLite.", "page": "https://rubric.hrih.si/editions/2026-10-30/79/", "card": "https://rubric.hrih.si/og/2026-10-30/79.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.588, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5525, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7867, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.527, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.5522, "spread": 0.053 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.708, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6458, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.59, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.65, "spread": 0.36 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.925, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.905, "spread": 0.09 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.8867, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2433, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.38, "spread": 0.32 } } }, { "rank": 80, "id": "st_20261030_8452", "title": "New global AI computing capacity is doubling every 9 months", "url": "https://epoch.ai/data-insights/ai-computing-capacity-additions-doubling", "domain": "epoch.ai", "site": "Epoch AI", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T00:00:00.000Z", "jev": 0.4986, "judge_a": 0.5731, "judge_b": 0.742, "score": 0.6045, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 71, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Epoch AI estimates that the computing capacity of AI chips sold worldwide is doubling roughly every nine months. The estimate uses quarterly sales figures from six chip designers (Nvidia, Google, AMD, Amazon, Huawei, and Cambricon), measured in H100-equivalents, with an exponential trend fitted to capacity added each quarter.", "page": "https://rubric.hrih.si/editions/2026-10-30/80/", "card": "https://rubric.hrih.si/og/2026-10-30/80.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6533, "spread": 0.23 }, "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": 0.823, "judge_a": 0.67, "judge_b": 0.82, "mean": 0.7711, "spread": 0.153 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.473, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5308, "spread": 0.148 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.74, "judge_a": 0.67, "judge_b": 0.8, "mean": 0.7367, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.545, "judge_a": 0.4, "judge_b": 0.5, "mean": 0.4817, "spread": 0.145 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.88, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.18, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0833, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.63, "judge_a": 0.2, "judge_b": 0.2, "mean": 0.3433, "spread": 0.43 } } }, { "rank": 81, "id": "st_20261030_7762", "title": "What Drives AI Citations? FAQ isn't what drives ChatGPT citations", "url": "https://arxiv.org/abs/2610.15033", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai", "growth" ], "category_names": [ "AI & machine learning", "Growth & marketing" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T12:14:17.000Z", "jev": 0.6216, "judge_a": 0.668, "judge_b": 0.8216, "score": 0.6037, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 72, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A study of 158,099 generative-search answers across two 90-day collections (consumer home internet and small B2B software) finds that ChatGPT citations depend mainly on the engine's own retrieval step, not on page features.", "page": "https://rubric.hrih.si/editions/2026-10-30/81/", "card": "https://rubric.hrih.si/og/2026-10-30/81.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.663, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6642, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.7633, "spread": 0.31 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6811, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.53, "judge_a": 0.75, "judge_b": 0.76, "mean": 0.68, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.997, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9322, "spread": 0.147 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.97, "judge_a": 0.8, "judge_b": 0.92, "mean": 0.8967, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9167, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.04, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.2, "spread": 0.22 } } }, { "rank": 82, "id": "st_20261030_7987", "title": "I built a tamper-evident flight recorder for AI agents", "url": "https://github.com/mnestiq/mnestiq", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T14:54:34.000Z", "jev": 0.7277, "judge_a": 0.4225, "judge_b": 0.6573, "score": 0.6024, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 73, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Mnestiq is an open-source tool that records what an AI agent reads, decides and does, tags each piece of input by source, and signs the log with hash-chained, Ed25519-signed Merkle checkpoints so later edits are detectable.", "page": "https://rubric.hrih.si/editions/2026-10-30/82/", "card": "https://rubric.hrih.si/og/2026-10-30/82.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.495, "spread": 0.305 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7233, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.607, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.5022, "spread": 0.257 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.913, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7442, "spread": 0.313 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7789, "spread": 0.357 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8708, "spread": 0.193 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.89, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.1733, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.25, "spread": 0.17 } } }, { "rank": 83, "id": "st_20261030_8042", "title": "AI, Lean, and the Future of Formal Verification, with Lean Creator Leo de Moura", "url": "https://thedataexchange.media/leo-de-moura-lean-aws/", "domain": "thedataexchange.media", "site": "The Data Exchange", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Other", "depth": "three_judges", "published_at": "2026-10-29T15:29:48.000Z", "jev": 0.7231, "judge_a": 0.4405, "judge_b": 0.6408, "score": 0.6014, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 74, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Ben Lorica interviews Leo de Moura, creator of the Lean programming language and theorem prover and a Distinguished Scientist at AWS, about formal verification in the age of AI.", "page": "https://rubric.hrih.si/editions/2026-10-30/83/", "card": "https://rubric.hrih.si/og/2026-10-30/83.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.78, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.65, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.79, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.953, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.7111, "spread": 0.453 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.743, "judge_a": 0.45, "judge_b": 0.67, "mean": 0.6208, "spread": 0.293 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.363, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4144, "spread": 0.117 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.745, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5883, "spread": 0.345 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.35, "judge_b": 0.92, "mean": 0.7267, "spread": 0.57 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1367, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.45, "spread": 0.38 } } }, { "rank": 84, "id": "st_20261030_8177", "title": "Nvidia Nemotron 3.5 Super and Lightning", "url": "https://research.nvidia.com/labs/nemotron/Nemotron-3.5-Super-and-Lightning/", "domain": "research.nvidia.com", "site": "NVIDIA Nemotron", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T16:58:40.000Z", "jev": 0.7085, "judge_a": 0.5986, "judge_b": 0.8068, "score": 0.6046, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 70, "topic_penalty": 0.08, "repeat_penalty": 0.0059, "summary": "NVIDIA released Nemotron 3.5 Super, a 120-billion-parameter multimodal model with 12 billion active parameters, and Nemotron 3.5 Lightning, a 30-billion-parameter text model with 3 billion active, both as open Mixture-of-Experts hybrid Mamba-Transformer models supporting up to 1-million-token context.", "page": "https://rubric.hrih.si/editions/2026-10-30/84/", "card": "https://rubric.hrih.si/og/2026-10-30/84.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.74, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7033, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.7733, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.603, "judge_a": 0.67, "judge_b": 0.82, "mean": 0.6978, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.825, "judge_a": 0.75, "judge_b": 0.84, "mean": 0.805, "spread": 0.09 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.897, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7922, "spread": 0.197 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.803, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.8342, "spread": 0.1 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9333, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.19, "spread": 0.21 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.2267, "spread": 0.12 } } }, { "rank": 85, "id": "st_20261030_8520", "title": "A tiny world model running games on a Raspberry Pi Pico 2 W", "url": "https://github.com/SheepSeb/tinyworldformer", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Hardware & devices" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T20:32:28.000Z", "jev": 0.6078, "judge_a": 0.4645, "judge_b": 0.7203, "score": 0.5975, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 75, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer built a tiny looped-transformer world model that generates game frames and rewards, paired with a small actor network, and runs both on a Raspberry Pi Pico 2 W's second core with no real game involved.", "page": "https://rubric.hrih.si/editions/2026-10-30/85/", "card": "https://rubric.hrih.si/og/2026-10-30/85.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.47, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.48, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.73, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.453, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.4078, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.645, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.575, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.99, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8667, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9425, "spread": 0.148 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.93, "spread": 0.09 }, "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": 0.2, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.16, "spread": 0.12 } } }, { "rank": 86, "id": "st_20261030_7812", "title": "Programming interface for all agent harnesses", "url": "https://github.com/botiverse/oar", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T12:41:08.000Z", "jev": 0.6878, "judge_a": 0.449, "judge_b": 0.6523, "score": 0.5963, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 76, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "OAR (Open Agent Runtime) is a provider-independent TypeScript interface for coding-agent runtimes such as Claude, Codex, Grok and Pi, letting developers drive any of them through one API and one set of events. Options a runtime cannot honor are refused up front rather than silently dropped.", "page": "https://rubric.hrih.si/editions/2026-10-30/86/", "card": "https://rubric.hrih.si/og/2026-10-30/86.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.63, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.5033, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.69, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.597, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.5322, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.858, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.7958, "spread": 0.108 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.87, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.7167, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.935, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.8583, "spread": 0.135 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.92, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.21, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.3, "judge_b": 0.35, "mean": 0.3, "spread": 0.1 } } }, { "rank": 87, "id": "st_20261030_7924", "title": "Manifestos, Fear, and ‘Loser Thinking’: Inside Proton’s AI Reckoning", "url": "https://www.404media.co/manifestos-fear-and-loser-thinking-inside-protons-ai-reckoning/", "domain": "404media.co", "site": "404 Media", "categories": [ "ai", "security", "startups" ], "category_names": [ "AI & machine learning", "Security", "Startups & founders" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T14:21:49.000Z", "jev": 0.5522, "judge_a": 0.5665, "judge_b": 0.6702, "score": 0.5963, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 77, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Proton, the Swiss maker of Proton Mail and other privacy tools, is reportedly pushing its engineers hard toward AI-driven development, including re-interviewing staff about their AI use, tracking AI code output, and ending engineering hiring.", "page": "https://rubric.hrih.si/editions/2026-10-30/87/", "card": "https://rubric.hrih.si/og/2026-10-30/87.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.563, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.5708, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.8667, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.303, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.4944, "spread": 0.297 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.507, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5092, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.693, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7244, "spread": 0.087 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.375, "judge_a": 0.2, "judge_b": 0.28, "mean": 0.285, "spread": 0.175 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0467, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.2, "judge_b": 0.38, "mean": 0.2767, "spread": 0.18 } } }, { "rank": 88, "id": "st_20261030_7839", "title": "How to Make Artificial Intelligence More Environmentally Sustainable", "url": "https://www.infoq.com/news/2026/10/AI-environmentally-sustainable/", "domain": "infoq.com", "site": "InfoQ", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T13:19:07.000Z", "jev": 0.7274, "judge_a": 0.4055, "judge_b": 0.652, "score": 0.5949, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 78, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Charles Humble, speaking at GOTO Copenhagen and in a follow-up InfoQ interview, argues that sustainability should be a top-level architectural concern for AI systems. IT accounts for roughly 2 to 4 percent of global emissions, and data center and network electricity use is rising.", "page": "https://rubric.hrih.si/editions/2026-10-30/88/", "card": "https://rubric.hrih.si/og/2026-10-30/88.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.683, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5508, "spread": 0.333 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.64, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.953, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.7944, "spread": 0.353 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7433, "spread": 0.31 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.64, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.57, "spread": 0.19 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.678, "judge_a": 0.45, "judge_b": 0.5, "mean": 0.5425, "spread": 0.227 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.15, "judge_b": 0.35, "mean": 0.46, "spread": 0.73 }, "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": 0.18, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.2333, "spread": 0.12 } } }, { "rank": 89, "id": "st_20261030_7614", "title": "DeepSeek and Moonshot trained new models on GPT-6 Astra, report says", "url": "https://thenextweb.com/news/deepseek-moonshot-gpt-6-astra-distillation", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T08:56:47.000Z", "jev": 0.5413, "judge_a": 0.5153, "judge_b": 0.7266, "score": 0.5944, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 79, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A report from The Information, which TNW has not independently verified, says Chinese labs DeepSeek and Moonshot AI relied heavily on OpenAI's GPT-6 Astra to train their newest models through distillation, a method of learning from a stronger model's outputs.", "page": "https://rubric.hrih.si/editions/2026-10-30/89/", "card": "https://rubric.hrih.si/og/2026-10-30/89.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6492, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.77, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7178, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.49, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4867, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.327, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3856, "spread": 0.153 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.52, "judge_a": 0.2, "judge_b": 0.38, "mean": 0.3667, "spread": 0.32 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.8867, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.05, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.3133, "spread": 0.22 } } }, { "rank": 90, "id": "st_20261030_7548", "title": "Agent's tests passed. Prove they could have failed", "url": "https://github.com/greenlitbooks/mustfail", "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": "2026-10-29T07:33:36.000Z", "jev": 0.6535, "judge_a": 0.4492, "judge_b": 0.6555, "score": 0.586, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 82, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "mustfail is an open-source Python tool that checks whether an AI coding agent's passing tests would actually catch its changes. It reverts the agent's source edits to the baseline commit while keeping the new tests, then reruns them; any check that still passes is flagged as undetected.", "page": "https://rubric.hrih.si/editions/2026-10-30/90/", "card": "https://rubric.hrih.si/og/2026-10-30/90.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.465, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.415, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.62, "judge_b": 0.8, "mean": 0.71, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.61, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.5367, "spread": 0.16 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.863, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.8108, "spread": 0.113 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.88, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.72, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.75, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9167, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.45, "judge_b": 0.16, "mean": 0.2433, "spread": 0.33 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1767, "spread": 0.09 } } }, { "rank": 91, "id": "st_20261030_8333", "title": "AI Contributions to GHC", "url": "https://serokell.io/blog/ai-contributions-to-ghc", "domain": "serokell.io", "site": "Serokell", "categories": [ "programming", "ai", "policy" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T17:42:30.000Z", "jev": 0.5926, "judge_a": 0.4555, "judge_b": 0.708, "score": 0.5853, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 84, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News covers GHC's new policy for LLM-assisted contributions to the Haskell compiler. Contributors may use LLMs but must take full responsibility for submitted code, explain it clearly, and disclose any direct generation, while writing the final version themselves is recommended rather than required.", "page": "https://rubric.hrih.si/editions/2026-10-30/91/", "card": "https://rubric.hrih.si/og/2026-10-30/91.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5158, "spread": 0.33 }, "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": 0.533, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.4844, "spread": 0.113 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.643, "judge_a": 0.55, "judge_b": 0.74, "mean": 0.6442, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.603, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6611, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.68, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5667, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8267, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.06, "mean": 0.09, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1533, "spread": 0.08 } } }, { "rank": 92, "id": "st_20261030_7777", "title": "Attacks target new Oracle E-Business Suite vulnerability", "url": "https://www.heise.de/en/news/Attacks-target-new-Oracle-E-Business-Suite-vulnerability-11504794.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T12:00:00.000Z", "jev": 0.51, "judge_a": 0.5273, "judge_b": 0.7157, "score": 0.5843, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 86, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Researchers have begun seeing attempts to exploit a critical, unauthenticated SQL injection flaw (CVE-2026-101767, CVSS 9.8) in Oracle E-Business Suite's Application Object Library, affecting versions 12.2.3 through 12.2.15. The flaw can allow full system takeover over HTTP, and a patch has been available for about a week.", "page": "https://rubric.hrih.si/editions/2026-10-30/92/", "card": "https://rubric.hrih.si/og/2026-10-30/92.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.555, "judge_a": 0.45, "judge_b": 0.73, "mean": 0.5783, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6733, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.357, "judge_a": 0.55, "judge_b": 0.46, "mean": 0.4556, "spread": 0.193 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.498, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.6592, "spread": 0.283 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.443, "judge_a": 0.5, "judge_b": 0.67, "mean": 0.5378, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.685, "judge_a": 0.35, "judge_b": 0.5, "mean": 0.5117, "spread": 0.335 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.35, "judge_b": 1, "mean": 0.7433, "spread": 0.65 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.15, "spread": 0.14 } } }, { "rank": 93, "id": "st_20261030_8098", "title": "MCP tools should disclose LLM and external API dependencies", "url": "https://santurcesoftware.com/mcp-tools-should-disclose-llm-and-api-calls", "domain": "santurcesoftware.com", "site": "Santurce Software", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T15:44:34.000Z", "jev": 0.6377, "judge_a": 0.4665, "judge_b": 0.6429, "score": 0.5823, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 90, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A developer argues that MCP tools should declare whether they invoke an LLM or external APIs, since a verification call like verify_project_setup could hide a database read, a third-party API call, or a model review, each needing different tests and fixtures.", "page": "https://rubric.hrih.si/editions/2026-10-30/93/", "card": "https://rubric.hrih.si/og/2026-10-30/93.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.43, "spread": 0.21 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6867, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.533, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.5044, "spread": 0.053 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.828, "judge_a": 0.6, "judge_b": 0.74, "mean": 0.7225, "spread": 0.228 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.533, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.5878, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.928, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.7325, "spread": 0.328 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8067, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.0667, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.16, "spread": 0.08 } } }, { "rank": 94, "id": "st_20261030_7916", "title": "Software engineering at Google & CS education with Titus Winters", "url": "https://newsletter.pragmaticengineer.com/p/software-engineering-at-google-and", "domain": "newsletter.pragmaticengineer.com", "site": "The Pragmatic Engineer (Gergely Orosz)", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T13:37:13.000Z", "jev": 0.6471, "judge_a": 0.517, "judge_b": 0.5806, "score": 0.5815, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 91, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Titus Winters, lead author of \"Software Engineering at Google\" and a former Google readability and Abseil lead now at Adobe, discusses on The Pragmatic Engineer podcast how Google refactored roughly 250 million lines of C++ without modern AI tools, why Google made automated testing mandatory after Search's release cadence slowed…", "page": "https://rubric.hrih.si/editions/2026-10-30/94/", "card": "https://rubric.hrih.si/og/2026-10-30/94.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5417, "spread": 0.305 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.8, "judge_b": 0.8, "mean": 0.7533, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.953, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7578, "spread": 0.353 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.738, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6725, "spread": 0.138 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.4, "judge_a": 0.45, "judge_b": 0.52, "mean": 0.4567, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.695, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.5417, "spread": 0.345 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8233, "spread": 0.21 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.2, "judge_b": 0.34, "mean": 0.2167, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.2, "judge_b": 0.48, "mean": 0.37, "spread": 0.28 } } }, { "rank": 95, "id": "st_20261030_8095", "title": "An Al Breakthrough Has Made Decades of Computer Science Research 'Irrelevant'", "url": "https://www.wired.com/story/ai-made-decades-of-computer-science-research-irrelevant/", "domain": "wired.com", "site": "WIRED", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T15:47:35.000Z", "jev": 0.5085, "judge_a": 0.611, "judge_b": 0.6164, "score": 0.5786, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 92, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "OpenAI and Anthropic have both claimed to disprove the exponential time hypothesis, a 27-year-old assumption underlying large areas of theoretical computer science, such as complexity and algorithm design. Researchers are still verifying the AI-generated proofs, and many fear that dozens of published papers may now be invalid.", "page": "https://rubric.hrih.si/editions/2026-10-30/95/", "card": "https://rubric.hrih.si/og/2026-10-30/95.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.908, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8425, "spread": 0.107 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.79, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6633, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.35, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4333, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.33, "judge_a": 0.45, "judge_b": 0.42, "mean": 0.4, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.473, "judge_a": 0.35, "judge_b": 0.34, "mean": 0.3875, "spread": 0.132 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8833, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.18, "mean": 0.1133, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.73, "judge_a": 0.35, "judge_b": 0.9, "mean": 0.66, "spread": 0.55 } } }, { "rank": 96, "id": "st_20261030_7771", "title": "Pemba – An embedded Zanzibar permission engine that shares your DB transactions", "url": "https://github.com/Joshua-Fields/pemba", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T12:03:12.000Z", "jev": 0.6273, "judge_a": 0.4185, "judge_b": 0.6871, "score": 0.5776, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 94, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Pemba is an alpha, embedded Zanzibar-style authorisation engine in Rust with Python bindings, storing relationship-based access rules in the application's own SQLite or Postgres database.", "page": "https://rubric.hrih.si/editions/2026-10-30/96/", "card": "https://rubric.hrih.si/og/2026-10-30/96.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.49, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4233, "spread": 0.19 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.68, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.41, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.3933, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.795, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7383, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8544, "spread": 0.283 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.9258, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.94, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.1667, "spread": 0.28 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.17, "spread": 0.17 } } }, { "rank": 97, "id": "st_20261030_7859", "title": "Linux 7.4 Networking Brings Hardware Pacing Offload, New Drivers & More Performance", "url": "https://www.phoronix.com/news/Linux-7.4-Networking", "domain": "phoronix.com", "site": "Phoronix", "categories": [ "it", "hardware" ], "category_names": [ "IT & networking", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T12:55:00.000Z", "jev": 0.5244, "judge_a": 0.409, "judge_b": 0.7378, "score": 0.557, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 117, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Linux 7.4's networking merge adds a \"pacing_offload\" attribute and an IDPF driver implementation that let NICs and SmartNICs take over timing and spacing of outgoing packets, alongside up to 70% throughput gains for mixed BIG TCP and non-BIG TCP paths.", "page": "https://rubric.hrih.si/editions/2026-10-30/97/", "card": "https://rubric.hrih.si/og/2026-10-30/97.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.5267, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.43, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.5667, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.647, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6222, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.588, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5858, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.407, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.5622, "spread": 0.373 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.623, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5508, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0467, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.18, "spread": 0.18 } } }, { "rank": 98, "id": "st_20261030_8539", "title": "The End of Consensus Research?", "url": "https://arxiv.org/abs/2610.30114", "domain": "arxiv.org", "site": "arXiv", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T21:30:07.000Z", "jev": 0.5827, "judge_a": 0.609, "judge_b": 0.8324, "score": 0.5747, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 96, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The paper introduces AI-greement, an agent harness that equips general-purpose coding agents with a consensus protocol knowledge base, a modular implementation skeleton, and a Lean verification framework, and uses it to design A-greement, a geo-replicated consensus protocol.", "page": "https://rubric.hrih.si/editions/2026-10-30/98/", "card": "https://rubric.hrih.si/og/2026-10-30/98.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.643, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.6942, "spread": 0.24 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.78, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5933, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.515, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.595, "spread": 0.37 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.933, "judge_a": 0.75, "judge_b": 0.92, "mean": 0.8678, "spread": 0.183 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.895, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.8917, "spread": 0.18 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9367, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0433, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.32, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2667, "spread": 0.14 } } }, { "rank": 99, "id": "st_20261030_7986", "title": "Advisories in Five Months: Results from the Drupal AI Security Initiative", "url": "https://www.drupal.org/blog/256-advisories-in-five-months-results-from-the-drupal-ai-security-initiative", "domain": "drupal.org", "site": "Drupal.org", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T14:52:34.000Z", "jev": 0.6743, "judge_a": 0.395, "judge_b": 0.6547, "score": 0.5746, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 97, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Drupal published 256 security advisories between June and October 2026, more than the 250 published over the previous 30 months combined. The Drupal Association attributes the surge to cheaper, AI-assisted vulnerability discovery rather than to changes in Drupal's codebase.", "page": "https://rubric.hrih.si/editions/2026-10-30/99/", "card": "https://rubric.hrih.si/og/2026-10-30/99.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.698, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6225, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.74, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.79, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.6233, "spread": 0.39 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.69, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6567, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.923, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7878, "spread": 0.323 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.71, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5967, "spread": 0.31 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.99, "mean": 0.8633, "spread": 0.29 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.25, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5233, "spread": 0.32 } } }, { "rank": 100, "id": "st_20261030_7381", "title": "Local Inference with Large Language Models", "url": "https://jacquesmattheij.com/local-inference-with-large-language-models/", "domain": "jacquesmattheij.com", "site": "jacquesmattheij.com", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T01:06:02.000Z", "jev": 0.5369, "judge_a": 0.4673, "judge_b": 0.7193, "score": 0.5745, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 98, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article explains why a small consultancy chose to run large language models on its own hardware rather than through commercial AI services, arguing that confidentiality under NDAs, distrust of data handling, and a desire for control over their workflow outweigh the capability gap.", "page": "https://rubric.hrih.si/editions/2026-10-30/100/", "card": "https://rubric.hrih.si/og/2026-10-30/100.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.493, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.4908, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.44, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.6333, "spread": 0.42 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.58, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5333, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7067, "spread": 0.24 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.467, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5122, "spread": 0.17 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.963, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8142, "spread": 0.263 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.85, "spread": 0.23 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.08, "judge_b": 0.04, "mean": 0.0667, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.38, "mean": 0.2633, "spread": 0.18 } } }, { "rank": 101, "id": "st_20261030_8028", "title": "Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials", "url": "https://developer.nvidia.com/blog/unlocking-tensor-core-performance-for-machine-learning-interatomic-potentials/", "domain": "developer.nvidia.com", "site": "NVIDIA Technical Blog", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T15:00:00.000Z", "jev": 0.5807, "judge_a": 0.463, "judge_b": 0.6794, "score": 0.5743, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 99, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "NVIDIA describes Tensor Core methods that emulate FP32 matrix multiplication with lower-precision hardware operations, applied to the PET transformer interatomic potential used in molecular dynamics.", "page": "https://rubric.hrih.si/editions/2026-10-30/101/", "card": "https://rubric.hrih.si/og/2026-10-30/101.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.528, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5358, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6567, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.46, "judge_a": 0.35, "judge_b": 0.45, "mean": 0.42, "spread": 0.11 }, "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": 0.91, "judge_a": 0.8, "judge_b": 0.83, "mean": 0.8467, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.9075, "spread": 0.123 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9167, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.35, "judge_b": 0.22, "mean": 0.23, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2167, "spread": 0.07 } } }, { "rank": 102, "id": "st_20261030_8267", "title": "Sheetgraph, AI agents can understand complex spreadsheets", "url": "https://github.com/asharma327/sheetgraph", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Data" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T17:49:55.000Z", "jev": 0.6712, "judge_a": 0.446, "judge_b": 0.5986, "score": 0.5718, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 100, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Sheetgraph is a beta Rust tool that extracts Excel and Google Sheets files into a compact REPORT.md and a formula dependency graph, with every cell saved alongside for exact lookups.", "page": "https://rubric.hrih.si/editions/2026-10-30/102/", "card": "https://rubric.hrih.si/og/2026-10-30/102.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.568, "judge_a": 0.35, "judge_b": 0.45, "mean": 0.4558, "spread": 0.218 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6933, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.507, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.4422, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.868, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7825, "spread": 0.168 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.967, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7556, "spread": 0.367 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7975, "spread": 0.273 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9367, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.35, "judge_b": 0.2, "mean": 0.2167, "spread": 0.25 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.28, "spread": 0.04 } } }, { "rank": 103, "id": "st_20261030_8397", "title": "Scaling Misuse Detection with Self-Assessment", "url": "https://safeguards.anthropic.com/2026/self-assessment/", "domain": "safeguards.anthropic.com", "site": "Anthropic Safeguards Blog", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T19:14:24.000Z", "jev": 0.6848, "judge_a": 0.5495, "judge_b": 0.8008, "score": 0.5784, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 93, "topic_penalty": 0.08, "repeat_penalty": 0.0068, "summary": "Anthropic's new technique, self-assessment, has Claude append a short safety evaluation to each of its own conversations right after responding, reusing the cached context so the added cost is under 2% of inference compute.", "page": "https://rubric.hrih.si/editions/2026-10-30/103/", "card": "https://rubric.hrih.si/og/2026-10-30/103.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.695, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8267, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.673, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.6744, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.665, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.665, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.923, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.7844, "spread": 0.323 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.895, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.835, "spread": 0.21 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9233, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.1733, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.3, "judge_b": 0.28, "mean": 0.2867, "spread": 0.02 } } }, { "rank": 104, "id": "st_20261030_8235", "title": "New research shows how to extract a timestamp from ChatGPT IDs", "url": "https://hindsig.ht/blog/decoding-chatgpt-ids/", "domain": "hindsig.ht", "site": "Hindsight Foundry", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T17:12:14.000Z", "jev": 0.5987, "judge_a": 0.4495, "judge_b": 0.6646, "score": 0.5709, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 101, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A researcher reverse-engineered the UUIDv8 identifiers in ChatGPT conversation URLs and found that the first eight hex characters are a Unix timestamp marking when the conversation was created. Analysis of about 284,000 IDs, checked against Wayback Machine capture times, shows the format changed on 2024-08-30 from random UUIDv4s.", "page": "https://rubric.hrih.si/editions/2026-10-30/104/", "card": "https://rubric.hrih.si/og/2026-10-30/104.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.49, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4567, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.69, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.513, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4811, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.613, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5608, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8744, "spread": 0.163 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.8892, "spread": 0.178 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.92, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.03, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.12, "spread": 0.15 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.14, "spread": 0.12 } } }, { "rank": 105, "id": "st_20261030_8109", "title": "From 22 GB to 5.3 GB: all of Hacker News, searchable offline", "url": "https://discuss.ai.google.dev/t/searching-all-of-hacker-news-with-embeddinggemma-2-on-a-laptop-49-3m-items-5-3-gb/196641", "domain": "discuss.ai.google.dev", "site": "Google AI Developers Forum", "categories": [ "programming", "ai", "data" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Data" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T15:36:24.000Z", "jev": 0.6089, "judge_a": 0.437, "judge_b": 0.6648, "score": 0.5702, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 102, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An open-source project compresses all 49.3 million Hacker News items from 2006 to 2026 into a 5.34 GB file searchable offline on a laptop CPU. It embeds each item once using EmbeddingGemma 2 on an integrated GPU, stores 256-bit binary codes, and reranks candidates at query time.", "page": "https://rubric.hrih.si/editions/2026-10-30/105/", "card": "https://rubric.hrih.si/og/2026-10-30/105.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.463, "judge_a": 0.35, "judge_b": 0.5, "mean": 0.4375, "spread": 0.15 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7133, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.4, "judge_b": 0.45, "mean": 0.5044, "spread": 0.263 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.693, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6742, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.927, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.7156, "spread": 0.427 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.818, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7392, "spread": 0.118 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.7, "judge_b": 0.97, "mean": 0.84, "spread": 0.27 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1167, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2433, "spread": 0.19 } } }, { "rank": 106, "id": "st_20261030_7877", "title": "Most AI pipelines are graphs. Graph engineering treats them that way", "url": "https://getotium.ai/blog/graph-engineering", "domain": "getotium.ai", "site": "Otium AI", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T13:59:52.000Z", "jev": 0.6847, "judge_a": 0.31, "judge_b": 0.7105, "score": 0.5683, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 103, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Graph engineering argues that AI pipelines should be treated as graphs of steps, with nodes for each step and edges for data dependencies, rather than as individual model calls or runtime-driven agent loops.", "page": "https://rubric.hrih.si/editions/2026-10-30/106/", "card": "https://rubric.hrih.si/og/2026-10-30/106.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.5458, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.4, "judge_b": 0.88, "mean": 0.6767, "spread": 0.48 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.617, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5456, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.848, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.7392, "spread": 0.298 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.633, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5711, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.865, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.775, "spread": 0.265 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8333, "spread": 0.21 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2533, "spread": 0.32 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.3, "spread": 0.08 } } }, { "rank": 107, "id": "st_20261030_7907", "title": "Running Self-Hosted AI Agents", "url": "https://oxide.computer/blog/self-hosted-ai-agents", "domain": "oxide.computer", "site": "Oxide Computer Blog", "categories": [ "ai", "devops" ], "category_names": [ "AI & machine learning", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T14:00:00.000Z", "jev": 0.587, "judge_a": 0.4489, "judge_b": 0.6652, "score": 0.567, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 105, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News: Oxide's engineering blog describes how to run self-hosted AI agents on its Oxide Cloud Computer using ephemeral virtual machines rather than containers, for stronger isolation.", "page": "https://rubric.hrih.si/editions/2026-10-30/107/", "card": "https://rubric.hrih.si/og/2026-10-30/107.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.513, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5108, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.6733, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.56, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5467, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.795, "judge_a": 0.62, "judge_b": 0.82, "mean": 0.745, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.743, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.7111, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.948, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8092, "spread": 0.248 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.7, "judge_b": 0.96, "mean": 0.8367, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.17, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.3333, "spread": 0.28 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.1833, "spread": 0.07 } } }, { "rank": 108, "id": "st_20261030_8159", "title": "Claude Scrub - redact secrets from Claude Code history on disk", "url": "https://github.com/overflowy/claude-scrub", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T16:01:16.000Z", "jev": 0.6307, "judge_a": 0.4915, "judge_b": 0.5762, "score": 0.5662, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 106, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Claude Scrub is an open-source command-line tool that finds secrets such as passwords, API tokens, and private keys in the plain-text history Claude Code stores under ~/.claude, then replaces each one with a labeled placeholder.", "page": "https://rubric.hrih.si/editions/2026-10-30/108/", "card": "https://rubric.hrih.si/og/2026-10-30/108.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.455, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.3783, "spread": 0.155 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6867, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.333, "judge_a": 0.35, "judge_b": 0.34, "mean": 0.3411, "spread": 0.017 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.845, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.775, "spread": 0.145 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.693, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7044, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.95, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7567, "spread": 0.35 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.91, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1133, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1367, "spread": 0.12 } } }, { "rank": 109, "id": "st_20261030_8241", "title": "If the context window is a million tokens, why bother with search?", "url": "https://deployed-ai.io/blog/long-context-vs-rag/", "domain": "deployed-ai.io", "site": "Deployed", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T17:18:52.000Z", "jev": 0.7567, "judge_a": 0.4515, "judge_b": 0.7896, "score": 0.5659, "contested": true, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 107, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Deployed AI's post tests whether a million-token context window makes retrieval unnecessary. Using three public datasets (research papers, contracts, meeting transcripts), it measures how much of each question's evidence keyword-plus-embedding search surfaces within token budgets from 500 to the full corpus.", "page": "https://rubric.hrih.si/editions/2026-10-30/109/", "card": "https://rubric.hrih.si/og/2026-10-30/109.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5908, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.76, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.73, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.6833, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.905, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8283, "spread": 0.205 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8278, "spread": 0.263 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.975, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.8383, "spread": 0.275 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.2, "judge_b": 0.94, "mean": 0.6667, "spread": 0.74 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.1967, "spread": 0.29 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2167, "spread": 0.18 } } }, { "rank": 110, "id": "st_20261030_8628", "title": "Resumux – TCP over WebSockets that survives the WebSocket dying", "url": "https://github.com/k4lleanka/resumux", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops", "it" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "IT & networking" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T22:55:02.000Z", "jev": 0.6116, "judge_a": 0.434, "judge_b": 0.6516, "score": 0.5657, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 108, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "resumux is a pre-1.0 open-source tool that tunnels TCP over WebSockets while keeping the TCP connection alive when the WebSocket drops, goes silent, or is replaced.", "page": "https://rubric.hrih.si/editions/2026-10-30/110/", "card": "https://rubric.hrih.si/og/2026-10-30/110.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.47, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4167, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.55, "judge_b": 0.85, "mean": 0.69, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.3922, "spread": 0.097 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.788, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7025, "spread": 0.188 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.8133, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.8675, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9233, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1233, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2367, "spread": 0.18 } } }, { "rank": 111, "id": "st_20261030_7747", "title": "Macvoice – A local voice agent for Mac in 1.4 GB, no PyTorch", "url": "https://github.com/johnrobinsn/macvoice", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T12:25:54.000Z", "jev": 0.639, "judge_a": 0.452, "judge_b": 0.6047, "score": 0.5652, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 109, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Macvoice is an open-source local voice agent for Apple Silicon Macs that chains voice activity detection, turn detection, Parakeet speech recognition, a user-chosen LLM, and Phonon speech synthesis, with no PyTorch dependency.", "page": "https://rubric.hrih.si/editions/2026-10-30/111/", "card": "https://rubric.hrih.si/og/2026-10-30/111.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.483, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.4008, "spread": 0.183 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6767, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.417, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.3822, "spread": 0.067 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.825, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.7017, "spread": 0.225 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7978, "spread": 0.373 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.8417, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9167, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.18, "spread": 0.1 } } }, { "rank": 112, "id": "st_20261030_7468", "title": "The Future Trait Has A Missing State", "url": "https://blog.yoshuawuyts.com/the-future-trait-has-a-missing-state", "domain": "blog.yoshuawuyts.com", "site": "blog.yoshuawuyts.com", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T04:24:28.000Z", "jev": 0.4923, "judge_a": 0.5528, "judge_b": 0.6495, "score": 0.5648, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 111, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article argues that Rust's Future and Coroutine state enums lack a \"Done\" state, forcing callers into panics, hangs, or workarounds like the MaybeDone type and the futures crate's Fuse adapter, which misuses Poll::Pending.", "page": "https://rubric.hrih.si/editions/2026-10-30/112/", "card": "https://rubric.hrih.si/og/2026-10-30/112.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.488, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5192, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.5, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.6733, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.553, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.4911, "spread": 0.133 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.518, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5658, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.443, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5478, "spread": 0.257 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.95, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8767, "spread": 0.15 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.91, "spread": 0.09 }, "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": 0.42, "judge_a": 0.15, "judge_b": 0.38, "mean": 0.3167, "spread": 0.27 } } }, { "rank": 113, "id": "st_20261030_7648", "title": "Phone was compromised before the user turned it on: the rise of Midnight Mimosa", "url": "https://www.bitdefender.com/en-us/blog/labs/midnight-mimosa-malware", "domain": "bitdefender.com", "site": "Bitdefender Labs", "categories": [ "security", "hardware" ], "category_names": [ "Security", "Hardware & devices" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T10:02:47.000Z", "jev": 0.6594, "judge_a": 0.6205, "judge_b": 0.751, "score": 0.5769, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 95, "topic_penalty": 0.08, "repeat_penalty": 0.0123, "summary": "Bitdefender researchers uncovered Midnight Mimosa, a malware family preinstalled in the firmware of low-cost MediaTek-based Android phones. It runs as a persistent, platform-signed system app that cannot be removed and silently installs disguised payloads for ad fraud, click fraud, and residential-proxy botnet recruitment.", "page": "https://rubric.hrih.si/editions/2026-10-30/113/", "card": "https://rubric.hrih.si/og/2026-10-30/113.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.83, "mean": 0.6933, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.8433, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.577, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5822, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.675, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.685, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.9189, "spread": 0.057 }, "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": 0.89, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9, "spread": 0.11 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.25, "judge_b": 0.38, "mean": 0.2333, "spread": 0.31 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.27, "spread": 0.15 } } }, { "rank": 114, "id": "st_20261030_8037", "title": "Prompt Layering", "url": "https://agentpatterns.ai/context-engineering/prompt-layering/", "domain": "agentpatterns.ai", "site": "AgentPatterns.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-29T15:29:25.000Z", "jev": 0.6964, "judge_a": 0.4915, "judge_b": 0.5056, "score": 0.5644, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 112, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Prompt Layering describes how agent instructions stack across four sources (system prompt, project files like AGENTS.md or CLAUDE.md, task-specific skill content, and the user message), with more specific layers generally winning conflicts.", "page": "https://rubric.hrih.si/editions/2026-10-30/114/", "card": "https://rubric.hrih.si/og/2026-10-30/114.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.638, "judge_a": 0.35, "judge_b": 0.45, "mean": 0.4792, "spread": 0.288 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.55, "judge_b": 0.65, "mean": 0.6367, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6267, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.918, "judge_a": 0.75, "judge_b": 0.7, "mean": 0.7892, "spread": 0.218 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.38, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.4033, "spread": 0.07 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.84, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.6733, "spread": 0.26 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.8, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.7733, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1133, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.3833, "spread": 0.51 } } }, { "rank": 115, "id": "st_20261030_8054", "title": "We ran 384 adversarial probes against 48 agent skills", "url": "https://skill123.me/blog/adversarial-probe-results-47-skills", "domain": "skill123.me", "site": "Skill123", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-29T15:20:54.000Z", "jev": 0.715, "judge_a": 0.589, "judge_b": 0.6764, "score": 0.5601, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 113, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Hrih News piece on skill123's test of 48 agent skills against 384 adversarial probes found that hidden-instruction injection succeeded only 1.6% of the time, while probes asking skills to exceed their declared scope succeeded 30% of the time.", "page": "https://rubric.hrih.si/editions/2026-10-30/115/", "card": "https://rubric.hrih.si/og/2026-10-30/115.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.545, "spread": 0.255 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8, "spread": 0.04 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.657, "judge_a": 0.55, "judge_b": 0.58, "mean": 0.5956, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.85, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.7933, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.987, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.8156, "spread": 0.287 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.825, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7483, "spread": 0.125 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.81, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8567, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.15, "judge_b": 0.04, "mean": 0.0867, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.32, "judge_a": 0.3, "judge_b": 0.34, "mean": 0.32, "spread": 0.04 } } }, { "rank": 116, "id": "st_20261030_7684", "title": "What Training Dataset Transparency Mandates Hide", "url": "https://techpolicy.press/what-training-dataset-transparency-mandates-hide", "domain": "techpolicy.press", "site": "Tech Policy Press", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T10:21:03.000Z", "jev": 0.5742, "judge_a": 0.432, "judge_b": 0.6712, "score": 0.5591, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 115, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News summary: A Tech Policy Press-style analysis argues that AI training dataset transparency mandates, including California's Training Data Transparency Act and the EU AI Act, produce vague disclosures that omit why and how datasets were built.", "page": "https://rubric.hrih.si/editions/2026-10-30/116/", "card": "https://rubric.hrih.si/og/2026-10-30/116.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.64, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5733, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6467, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.797, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6722, "spread": 0.297 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.533, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5042, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.45, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5567, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.588, "judge_a": 0.45, "judge_b": 0.56, "mean": 0.5325, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.79, "spread": 0.19 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0467, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.3, "judge_b": 0.25, "mean": 0.2733, "spread": 0.05 } } }, { "rank": 117, "id": "st_20261030_8663", "title": "LLM Programming and Theory Building", "url": "https://kevinlynagh.com/newsletter/2026_10_29_llm_programming_and_theory_building/", "domain": "kevinlynagh.com", "site": "kevinlynagh.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T23:56:15.000Z", "jev": 0.5579, "judge_a": 0.4805, "judge_b": 0.6362, "score": 0.5582, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 116, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Kevin Lynagh describes trying to rewrite his Tasktron workflow harness with LLM coding agents (Astra 6, Opus 5.5, Sol 6.1) over several weeks and failing to get concise, well-factored code, since the models kept adding overlapping concepts, scattering mutable state, and writing overly specific tests.", "page": "https://rubric.hrih.si/editions/2026-10-30/117/", "card": "https://rubric.hrih.si/og/2026-10-30/117.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.413, "judge_a": 0.25, "judge_b": 0.52, "mean": 0.3942, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.72, "judge_b": 0.83, "mean": 0.7233, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.743, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.5744, "spread": 0.263 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.645, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.6217, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.273, "judge_a": 0.3, "judge_b": 0.56, "mean": 0.3778, "spread": 0.287 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.62, "judge_a": 0.35, "judge_b": 0.61, "mean": 0.5267, "spread": 0.27 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.89, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.2, "judge_b": 0.14, "mean": 0.17, "spread": 0.06 } } }, { "rank": 118, "id": "st_20261030_7787", "title": "Compile-Time Target CPU Feature Detection in C", "url": "https://terroir.systems/compile-time-cpu-feature-detection-in-c/", "domain": "terroir.systems", "site": "André Terroir", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-29T12:56:41.000Z", "jev": 0.577, "judge_a": 0.5083, "judge_b": 0.5813, "score": 0.5555, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 119, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This article explains how C programs can detect, at compile time, which instruction set features the compiler's target supports, as opposed to runtime CPU detection via CPUID or Linux auxiliary vectors.", "page": "https://rubric.hrih.si/editions/2026-10-30/118/", "card": "https://rubric.hrih.si/og/2026-10-30/118.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.468, "judge_a": 0.3, "judge_b": 0.45, "mean": 0.4058, "spread": 0.168 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.42, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.53, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.497, "judge_a": 0.4, "judge_b": 0.34, "mean": 0.4122, "spread": 0.157 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.823, "judge_a": 0.65, "judge_b": 0.72, "mean": 0.7308, "spread": 0.173 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.743, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7544, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.8, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.82, "mean": 0.87, "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": 0.12, "judge_a": 0.1, "judge_b": 0.06, "mean": 0.0933, "spread": 0.06 } } }, { "rank": 119, "id": "st_20261030_8064", "title": "Drawing for drawing's sake: Decompiling Sonic Adventure without writing code", "url": "https://kylemayer.io/drawing/", "domain": "kylemayer.io", "site": "kylemayer.io", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T15:11:15.000Z", "jev": 0.5524, "judge_a": 0.4075, "judge_b": 0.7049, "score": 0.5549, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 120, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An independent developer released sonic-adventure, a complete matching decompilation of the 1999 Sega Dreamcast game Sonic Adventure: 247 binaries and 5.8 million bytes of code, rebuilt from source and matched byte for byte against the original disc.", "page": "https://rubric.hrih.si/editions/2026-10-30/119/", "card": "https://rubric.hrih.si/og/2026-10-30/119.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.603, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5908, "spread": 0.27 }, "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": 0.483, "judge_a": 0.35, "judge_b": 0.43, "mean": 0.4211, "spread": 0.133 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.583, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5375, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.69, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.778, "judge_a": 0.55, "judge_b": 0.88, "mean": 0.7358, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.89, "spread": 0.18 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1267, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.35, "judge_b": 0.22, "mean": 0.3533, "spread": 0.27 } } }, { "rank": 120, "id": "st_20261030_7989", "title": "Lasm: Write formally verified web apps in Lean, deploy to Node, serverless, edge", "url": "https://github.com/Millillion/lasm", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T14:51:25.000Z", "jev": 0.595, "judge_a": 0.452, "judge_b": 0.6175, "score": 0.5548, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 121, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Lasm is an open-source toolchain, released under MIT, that compiles ordinary Lean 4 programs ahead of time to WebAssembly so one codebase can ship as a Node server, serverless or edge function, WASI or Fastly module, or browser app.", "page": "https://rubric.hrih.si/editions/2026-10-30/120/", "card": "https://rubric.hrih.si/og/2026-10-30/120.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.543, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5542, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6833, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.42, "judge_a": 0.45, "judge_b": 0.5, "mean": 0.4567, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.688, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.6458, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.7, "judge_b": 0.85, "mean": 0.8411, "spread": 0.273 }, "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": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.33, "spread": 0.46 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.2633, "spread": 0.1 } } }, { "rank": 121, "id": "st_20261030_8041", "title": "Your Cryptography Conspiracy Theory Is Wrong", "url": "https://soatok.blog/2026/10/29/your-cryptography-conspiracy-theory-is-wrong/", "domain": "soatok.blog", "site": "Dhole Moments", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T15:28:32.000Z", "jev": 0.4989, "judge_a": 0.476, "judge_b": 0.6897, "score": 0.5548, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 122, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author, a cryptography practitioner with no government ties, argues that most popular conspiracy theories about NSA and NIST cryptography fail on technical grounds.", "page": "https://rubric.hrih.si/editions/2026-10-30/121/", "card": "https://rubric.hrih.si/og/2026-10-30/121.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.453, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5242, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7033, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.747, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.6089, "spread": 0.247 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.495, "judge_a": 0.35, "judge_b": 0.6, "mean": 0.4817, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.607, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7289, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.748, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.7725, "spread": 0.072 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.2, "judge_b": 0.86, "mean": 0.6567, "spread": 0.71 }, "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": 0.44, "judge_a": 0.2, "judge_b": 0.38, "mean": 0.34, "spread": 0.24 } } }, { "rank": 122, "id": "st_20261030_7944", "title": "Lean Weekly News", "url": "https://leanweekly.news/", "domain": "leanweekly.news", "site": "leanweekly.news", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T14:13:42.000Z", "jev": 0.5758, "judge_a": 0.3965, "judge_b": 0.6904, "score": 0.5542, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 123, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Juan J. Manfredi's Lean formalization proves the main results of OpenAI's October 4, 2026 preprint on infinity-harmonic functions, including that such functions on connected open sets in dimensions three and higher are C¹, relying only on Lean's three standard axioms.", "page": "https://rubric.hrih.si/editions/2026-10-30/122/", "card": "https://rubric.hrih.si/og/2026-10-30/122.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.755, "judge_a": 0.35, "judge_b": 0.7, "mean": 0.6017, "spread": 0.405 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.6633, "spread": 0.4 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.547, "judge_a": 0.3, "judge_b": 0.45, "mean": 0.4322, "spread": 0.247 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.568, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5125, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.713, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7311, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.77, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.73, "spread": 0.22 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9033, "spread": 0.12 }, "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": 0.53, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.3033, "spread": 0.35 } } }, { "rank": 123, "id": "st_20261030_7641", "title": "Lindroids, run Linux apps on Android from any container application", "url": "https://github.com/dixonSolutions/lindroids", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-29T10:27:35.000Z", "jev": 0.5731, "judge_a": 0.4305, "judge_b": 0.6572, "score": 0.5536, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 124, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Lindroids is an open-source tool that runs Linux GUI programs from Termux, proot distros, or Flathub on Android without root. Its export command builds a separate Android app for each program on the phone, with its own icon, name and Recents entry, which launches even when Termux is closed.", "page": "https://rubric.hrih.si/editions/2026-10-30/123/", "card": "https://rubric.hrih.si/og/2026-10-30/123.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4858, "spread": 0.15 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.67, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.423, "judge_a": 0.3, "judge_b": 0.4, "mean": 0.3744, "spread": 0.123 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.688, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6525, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.873, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.7011, "spread": 0.423 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.843, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8008, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9267, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1267, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.2433, "spread": 0.19 } } }, { "rank": 124, "id": "st_20261030_8603", "title": "Charter, field-level control over agents' calls into customer accounts", "url": "https://github.com/r28ai/charter", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-29T22:23:25.000Z", "jev": 0.692, "judge_a": 0.4379, "judge_b": 0.5292, "score": 0.5529, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 125, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Charter is an open-source Python library that puts a validation layer between AI agents and customer-account APIs. Each tool is a Pydantic model mirroring the API's fields, with developer-defined rules checked before any request is sent.", "page": "https://rubric.hrih.si/editions/2026-10-30/124/", "card": "https://rubric.hrih.si/og/2026-10-30/124.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.578, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4925, "spread": 0.228 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7233, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.4933, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.908, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.8025, "spread": 0.188 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.947, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7556, "spread": 0.347 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.78, "judge_b": 0.78, "mean": 0.8483, "spread": 0.205 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.8433, "spread": 0.23 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.45, "judge_b": 0.7, "mean": 0.4233, "spread": 0.58 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.23, "judge_a": 0.4, "judge_b": 0.35, "mean": 0.3267, "spread": 0.17 } } }, { "rank": 125, "id": "st_20261030_8372", "title": "Explorations in Agentic Development", "url": "https://fluffyandflakey.blog/2026/10/19/radical-explorations/", "domain": "fluffyandflakey.blog", "site": "Fluffy and Flakey", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-29T18:30:49.000Z", "jev": 0.6545, "judge_a": 0.524, "judge_b": 0.7718, "score": 0.5501, "contested": false, "flag_penalty": 0, "freshness_penalty": 0.1, "score_rank": 127, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Over a stretch from April to September 2026, a developer took part in an internal \"Radical Speed Month\" experiment, using AI agents heavily to prototype projects and build tools.", "page": "https://rubric.hrih.si/editions/2026-10-30/125/", "card": "https://rubric.hrih.si/og/2026-10-30/125.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.4958, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.7633, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.757, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5922, "spread": 0.357 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.783, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7342, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7033, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.883, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8475, "spread": 0.082 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9467, "spread": 0.07 }, "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": 0.24, "judge_a": 0.2, "judge_b": 0.14, "mean": 0.1933, "spread": 0.1 } } }, { "rank": 126, "id": "st_20261030_8413", "title": "Cybersecurity Is About to Be the Biggest Problem in Tech", "url": "https://projectautonomy.substack.com/p/cybersecurity-is-about-to-be-the", "domain": "projectautonomy.substack.com", "site": "projectautonomy.substack.com", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-29T19:00:39.000Z", "jev": 0.6251, "judge_a": 0.5245, "judge_b": 0.5994, "score": 0.583, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 88, "topic_penalty": 0.08, "repeat_penalty": 0.0357, "summary": "AI agents can now run cheap, parallel hacking campaigns against exposed systems, and attackers are exploiting known flaws within days, often faster than companies can patch. Reported cases include an authorized NSA red-team test, a criminal swarm that compromised over 100 companies, and the first documented AI-run ransomware attack.", "page": "https://rubric.hrih.si/editions/2026-10-30/126/", "card": "https://rubric.hrih.si/og/2026-10-30/126.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6633, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.58, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.6867, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.983, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8678, "spread": 0.183 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.838, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7458, "spread": 0.138 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.43, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5033, "spread": 0.15 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.673, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.5342, "spread": 0.222 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6867, "spread": 0.48 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2267, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.68, "spread": 0.31 } } }, { "rank": 127, "id": "st_20261030_8032", "title": "Systemd adds support for Rust", "url": "https://www.theregister.com/os-platforms/2026/10/29/systemd-adds-support-for-rust/5306264", "domain": "theregister.com", "site": "The Register", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-29T14:00:00.000Z", "jev": 0.4869, "judge_a": 0.6154, "judge_b": 0.5551, "score": 0.5524, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 126, "topic_penalty": 0.08, "repeat_penalty": 0.0096, "summary": "Systemd, the widely used Linux init system, has taken a first step toward accepting Rust code: version 263, now in development, has merged a feature that splits an earlier pull request into five parts, with the Rust support as the headline item.", "page": "https://rubric.hrih.si/editions/2026-10-30/127/", "card": "https://rubric.hrih.si/og/2026-10-30/127.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.55, "judge_b": 0.48, "mean": 0.515, "spread": 0.07 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.48, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.6667, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.553, "judge_a": 0.67, "judge_b": 0.58, "mean": 0.6011, "spread": 0.117 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.558, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5358, "spread": 0.057 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.453, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5444, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.668, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5825, "spread": 0.167 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.62, "judge_a": 0.9, "judge_b": 0.03, "mean": 0.5167, "spread": 0.87 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.05, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.12, "spread": 0.18 } } } ] }