{ "edition": "2026-10-29", "name": "Rubric, edition of Thursday 29 October 2026", "url": "https://rubric.hrih.si/editions/2026-10-29/", "published_at": "2026-10-29T06:00:00Z", "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 2026-10-29: https://rubric.hrih.si/editions/2026-10-29/" }, "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_20261029_6406", "title": "Apple's git still trusts the compromised eMudhra certificates", "url": "https://emudhra.we-have-a-problem.com/apple.html", "domain": "emudhra.we-have-a-problem.com", "site": "we-have-a-problem.com", "categories": [ "programming", "security", "it" ], "category_names": [ "Programming & dev tools", "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T11:03:40.000Z", "jev": 0.7723, "judge_a": 0.7881, "judge_b": 0.786, "score": 0.7821, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 1, "topic_penalty": 0, "repeat_penalty": 0, "summary": "As of October 28, git, curl and python3 shipped with macOS reportedly still trust eight eMudhra intermediate CAs whose P-384 private keys can be recovered from their public certificates. Safari and apps on Apple's frameworks are blocked, but tools that read /etc/ssl/cert.pem are not. One independent test on a Mac found Apple's git and curl accepted a forged chain, and Homebrew's git did not help. Builders should remove the roots by hand, recheck after each update, and review macOS CI runners. Apple's response and the macOS 27.2 claim remain unverified.", "page": "https://rubric.hrih.si/editions/2026-10-29/1/", "card": "https://rubric.hrih.si/og/2026-10-29/1.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.735, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.745, "spread": 0.06 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.85, "spread": 0.1 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.8, "judge_b": 0.65, "mean": 0.7044, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.975, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.925, "spread": 0.075 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.773, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8111, "spread": 0.087 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.893, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.8042, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.78, "judge_a": 0.8, "judge_b": 0.72, "mean": 0.7667, "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": 0.25, "judge_a": 0.2, "judge_b": 0.28, "mean": 0.2433, "spread": 0.08 } } }, { "rank": 2, "id": "st_20261029_6317", "title": "Claude Code's default telemetry sends Anthropic SHA-256 hashes of files it reads", "url": "https://agenticbench.org/ant-1-claude-code-telemetry.html", "domain": "agenticbench.org", "site": "AgenticBench", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T09:28:30.000Z", "jev": 0.7089, "judge_a": 0.7375, "judge_b": 0.7931, "score": 0.7465, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 2, "topic_penalty": 0, "repeat_penalty": 0, "summary": "AgenticBench reports that Claude Code, including build 2.1.312 on 27 October, sends api.anthropic.com an unsalted SHA-256 hash of each file it reads, with device and session IDs, even with a DeepSeek model and no Anthropic login. Weak secrets in templated files such as .env can be guessed and checked offline; the authors recovered a fake password in about 3.5 seconds. Setting CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 stopped telemetry in their tests. Findings rest on one unit per condition, and Anthropic has sent only an automated acknowledgement.", "page": "https://rubric.hrih.si/editions/2026-10-29/2/", "card": "https://rubric.hrih.si/og/2026-10-29/2.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.635, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6517, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.87, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.29, "judge_a": 0.72, "judge_b": 0.67, "mean": 0.56, "spread": 0.43 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.888, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8358, "spread": 0.087 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8811, "spread": 0.183 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.8, "judge_b": 0.75, "mean": 0.8483, "spread": 0.245 }, "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": 0.04, "judge_a": 0.04, "judge_b": 0.01, "mean": 0.03, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1767, "spread": 0.17 } } }, { "rank": 3, "id": "st_20261029_7334", "title": "GitLab security incident: attempted exploitation of CVE-2026-97229 | Alpine Linux", "url": "https://alpinelinux.org/posts/GitLab-security-incident-CVE-2026-97229.html", "domain": "alpinelinux.org", "site": "alpinelinux.org", "categories": [ "security", "devops" ], "category_names": [ "Security", "Infrastructure & DevOps" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T23:01:26.000Z", "jev": 0.7146, "judge_a": 0.7028, "judge_b": 0.8162, "score": 0.7445, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 3, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Alpine Linux says an attacker tried to exploit GitLab's CVE-2026-97229 (CVSS 10.0, a regex bug in Ruby's Onigmo engine) on October 20 and 21, crashing workers eight times. Alpine found no evidence of code execution but rebuilt the instance anyway. Its checks found no tampering with packages or releases. The lesson for self-hosters: GitLab's fix is a Ruby patch applied only in official packages, so custom images need Ruby 3.3.13, 3.4.12 or 4.0.9 as well. Most figures come from Alpine alone.", "page": "https://rubric.hrih.si/editions/2026-10-29/3/", "card": "https://rubric.hrih.si/og/2026-10-29/3.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.608, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6258, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.79, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.8467, "spread": 0.11 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.46, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.58, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.898, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.8525, "spread": 0.097 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.877, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.8956, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.888, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.7958, "spread": 0.188 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.7, "judge_b": 0.98, "mean": 0.8533, "spread": 0.28 }, "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": 0.17, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.1, "spread": 0.14 } } }, { "rank": 4, "id": "st_20261029_7016", "title": "PyTorch 2.15 Release Blog", "url": "https://pytorch.org/blog/pytorch-2-15-release-blog/", "domain": "pytorch.org", "site": "PyTorch Blog", "categories": [ "programming", "ai", "devops", "hardware" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "Hardware & devices" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-28T17:44:20.000Z", "jev": 0.7175, "judge_a": 0.6485, "judge_b": 0.8523, "score": 0.7393, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 4, "topic_penalty": 0, "repeat_penalty": 0, "summary": "PyTorch 2.15 makes the rewritten NCCL backend the default for every nccl process group, with TORCH_DIST_USE_NCCL2=0 or nccl-legacy as rollbacks, and adds a flight recorder that dumps on hangs. It also drops Python 3.10 and macOS 14 support, and changes FSDP2 gradient dtype defaults. The release adds NIXL one-sided transports, FP8 and faster decode on Apple Silicon, and CUDA 13.2 as the default wheel. The MPS speedups, such as 1.32–1.34x MoE decode on an M3 Ultra, are the project's own figures and could not be independently checked.", "page": "https://rubric.hrih.si/editions/2026-10-29/4/", "card": "https://rubric.hrih.si/og/2026-10-29/4.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.758, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7325, "spread": 0.24 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.7433, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.617, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.7456, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.843, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8142, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.823, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8111, "spread": 0.21 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.873, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8375, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.11, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.2, "judge_b": 0.06, "mean": 0.13, "spread": 0.14 } } }, { "rank": 5, "id": "st_20261029_6467", "title": "A Measurement Study of Contextual Leakage in AI Chat Platforms", "url": "https://arxiv.org/abs/2610.37467", "domain": "arxiv.org", "site": "arXiv", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T12:15:16.000Z", "jev": 0.7002, "judge_a": 0.6422, "judge_b": 0.8466, "score": 0.7296, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 5, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Researchers at Stony Brook audited the web interfaces of 42 AI chatbots, 25 general-purpose and 17 in health, legal and education, and found that 21 send conversation titles to third-party trackers and nine send verbatim prompts. In the general-purpose group, 11 of 13 leaking platforms also send the user's email, linking content to identity. Trackers read auto-generated page titles as ordinary metadata. Testing used fresh accounts, two prompts, one US vantage point and web interfaces only. Builders embedding analytics scripts in chat UIs should audit what those scripts can read.", "page": "https://rubric.hrih.si/editions/2026-10-29/5/", "card": "https://rubric.hrih.si/og/2026-10-29/5.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7042, "spread": 0.22 }, "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": 0.733, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7044, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.75, "judge_a": 0.55, "judge_b": 0.83, "mean": 0.71, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.8, "judge_b": 0.84, "mean": 0.87, "spread": 0.17 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.888, "judge_a": 0.75, "judge_b": 0.91, "mean": 0.8492, "spread": 0.16 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.86, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9133, "spread": 0.12 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.03, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.37, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.2467, "spread": 0.25 } } }, { "rank": 6, "id": "st_20261029_6973", "title": "Clustered Geometry, Partitioned TLAS, Indirect Acceleration Structure Builds", "url": "https://devblogs.microsoft.com/directx/clas-ptlas-preview/", "domain": "devblogs.microsoft.com", "site": "devblogs.microsoft.com", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-28T17:49:21.000Z", "jev": 0.6453, "judge_a": 0.65, "judge_b": 0.8096, "score": 0.7016, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 9, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Microsoft released a preview of DirectX Raytracing Clustered Geometry and Partitioned TLAS, with GPU-driven build and update calls, in Agility SDK 1.722 and Shader Model 6.10. Clusters of up to 256 triangles are assembled into BLAS, and PTLAS rebuilds only changed scene regions. In Microsoft's own AMD RX 9070 sample, frame rate rose from 1.2 to 79 FPS, though scratch memory grew and the test covers one configuration. Support currently spans RTX, Radeon RX 9000 and Intel Arc B-series, with Qualcomm expected in 2027.", "page": "https://rubric.hrih.si/editions/2026-10-29/6/", "card": "https://rubric.hrih.si/og/2026-10-29/6.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6867, "spread": 0.22 }, "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": 0.543, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5711, "spread": 0.077 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.79, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7833, "spread": 0.16 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8967, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.95, "spread": 0.09 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.95, "judge_b": 0.99, "mean": 0.9567, "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": 0.33, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2367, "spread": 0.15 } } }, { "rank": 7, "id": "st_20261029_7208", "title": "The case of the 9 bit AES key", "url": "https://v12.sh/blog/the-case-of-the-9bit-aes-key", "domain": "v12.sh", "site": "V12", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T20:51:44.000Z", "jev": 0.6937, "judge_a": 0.6023, "judge_b": 0.8621, "score": 0.7194, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 6, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Security firm V12 reports that several PDF tools generate AES-256 file keys from tiny, guessable state. In MuPDF before 1.28.5, only nine bits of the timestamp seed affect the key, so 512 candidates cover every case and a Python search takes about 11 ms. Foxit Reader hashes a timestamp with zero-filled bytes, and a Rust search covers every second since 1970 in under five seconds. OpenPDF and iText are also affected. MuPDF is fixed; the findings are the authors' own and unverified independently.", "page": "https://rubric.hrih.si/editions/2026-10-29/7/", "card": "https://rubric.hrih.si/og/2026-10-29/7.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.59, "judge_a": 0.45, "judge_b": 0.86, "mean": 0.6333, "spread": 0.41 }, "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": 0.583, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5344, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.765, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7483, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.987, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.9256, "spread": 0.137 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9367, "spread": 0.15 }, "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": 0.04, "judge_a": 0.03, "judge_b": 0.02, "mean": 0.03, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1667, "spread": 0.14 } } }, { "rank": 8, "id": "st_20261029_6439", "title": "Analysts followed AI agent's randomly assigned preferences in 86% of decisions", "url": "https://www.anthropic.com/research/agents-judgment-calls", "domain": "anthropic.com", "site": "anthropic.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T11:33:10.000Z", "jev": 0.6934, "judge_a": 0.5205, "judge_b": 0.8205, "score": 0.6781, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 14, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Anthropic ran a randomized study of 50 professional analysts doing a farming-cooperative analysis with Claude Code, where each agent was secretly assigned a coin-flip lean on four open analytical choices. Analysts matched the lean 86% of the time, and 77% reused the agent's arguments but only 15% credited it. The effect on the final recommendation was smaller (54% vs 36%). The study covers one task, one model and deliberately strengthened leans, and the figures are the authors' own, not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-29/8/", "card": "https://rubric.hrih.si/og/2026-10-29/8.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.718, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6325, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.7, "judge_b": 0.95, "mean": 0.7867, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.813, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6978, "spread": 0.313 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.64, "judge_a": 0.55, "judge_b": 0.8, "mean": 0.6633, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.943, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8411, "spread": 0.243 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.738, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5692, "spread": 0.388 }, "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": 0.1, "judge_b": 0.01, "mean": 0.0533, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1933, "spread": 0.14 } } }, { "rank": 9, "id": "st_20261029_6849", "title": "Anthropic Threw Away Git", "url": "https://jackiechen.blog/2026/10/22/anthropic-threw-away-git-what-broke-first-is-the-part-i-cant-stop-thinking-about/", "domain": "jackiechen.blog", "site": "Jackie Chen's IT Workshop", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Analysis", "depth": "claim_checked", "published_at": "2026-10-28T16:02:45.000Z", "jev": 0.6463, "judge_a": 0.589, "judge_b": 0.7732, "score": 0.6695, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 15, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Anthropic says it replaced git for its monorepo with Scion, a centralised, server-authoritative system built by seven people in five months. Merged changes rose from about 2,500 a day in May to over 30,000 in September, and CI alone runs 25 million checkouts daily. Anthropic reports a 200x smaller footprint than its old mirror fleet and checkouts two orders of magnitude faster. It is not open-sourcing Scion, and it suggests git forges could expose server-side APIs instead.", "page": "https://rubric.hrih.si/editions/2026-10-29/9/", "card": "https://rubric.hrih.si/og/2026-10-29/9.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.665, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.665, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.82, "judge_b": 0.93, "mean": 0.8533, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.62, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6367, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.693, "judge_a": 0.5, "judge_b": 0.76, "mean": 0.6508, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.58, "judge_a": 0.45, "judge_b": 0.79, "mean": 0.6067, "spread": 0.34 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.905, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8383, "spread": 0.21 }, "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": 0.08, "judge_a": 0.12, "judge_b": 0.03, "mean": 0.0767, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.55, "judge_a": 0.3, "judge_b": 0.29, "mean": 0.38, "spread": 0.26 } } }, { "rank": 10, "id": "st_20261029_6502", "title": "AI crawlers in robots.txt: a study of the top 10k sites (2026)", "url": "https://hitmind.ai/research/ai-crawler-access-2026/", "domain": "hitmind.ai", "site": "HitMind", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T12:57:54.000Z", "jev": 0.7407, "judge_a": 0.5175, "judge_b": 0.7432, "score": 0.6671, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 16, "topic_penalty": 0, "repeat_penalty": 0, "summary": "HitMind analyzed robots.txt files from the top 10,000 domains and found that 329 of 1,105 files (29.8%) that block paths for everyone while naming an AI crawler let that crawler past at least one block. Under RFC 9309, a crawler with its own group ignores the * rules, so a bare Allow: / for GPTBot reopens search, account and API paths. About a fifth of domains refused the crawler, and the study measures rules, not what crawlers actually fetched.", "page": "https://rubric.hrih.si/editions/2026-10-29/10/", "card": "https://rubric.hrih.si/og/2026-10-29/10.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.593, "judge_a": 0.4, "judge_b": 0.65, "mean": 0.5475, "spread": 0.25 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7467, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.66, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6433, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.91, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.8133, "spread": 0.16 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.9078, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.973, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8175, "spread": 0.273 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.91, "spread": 0.12 }, "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": 0.16, "judge_a": 0.2, "judge_b": 0.06, "mean": 0.14, "spread": 0.14 } } }, { "rank": 11, "id": "st_20261029_7272", "title": "Inside KeyDrift: Analyzing historical UAT-11473 activity on Cisco SD-WAN", "url": "https://blog.talosintelligence.com/uat-11473-keydrift/", "domain": "blog.talosintelligence.com", "site": "Cisco Talos", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T21:32:02.000Z", "jev": 0.5946, "judge_a": 0.769, "judge_b": 0.7843, "score": 0.716, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 7, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Cisco Talos describes UAT-11473, an actor that exploited CVE-2026-20127, an authentication bypass in Cisco SD-WAN Catalyst Controller, to install a malware framework called KeyDrift. Its PolyFlame module runs inside vdaemon processes, while LittleFilch hooks SSL_read to extract assessed IPsec data-plane keys from decrypted control traffic. Talos reports high-confidence infrastructure overlap with Volt Typhoon and targeting of space, defense and policy organizations; the report cites no independent sources for these points. Cisco has released patches and a hardening guide.", "page": "https://rubric.hrih.si/editions/2026-10-29/11/", "card": "https://rubric.hrih.si/og/2026-10-29/11.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.72, "judge_b": 0.82, "mean": 0.715, "spread": 0.215 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.88, "judge_b": 0.9, "mean": 0.8333, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.327, "judge_a": 0.6, "judge_b": 0.52, "mean": 0.4822, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.655, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.775, "spread": 0.195 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.763, "judge_a": 0.82, "judge_b": 0.9, "mean": 0.8278, "spread": 0.137 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9508, "spread": 0.093 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.92, "judge_b": 0.95, "mean": 0.93, "spread": 0.03 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.04, "judge_b": 0.08, "mean": 0.0533, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.28, "spread": 0.28 } } }, { "rank": 12, "id": "st_20261029_6691", "title": "The AI cost of JavaScript frameworks", "url": "https://nolanlawson.com/2026/10/28/the-ai-cost-of-javascript-frameworks/", "domain": "nolanlawson.com", "site": "Nolan Lawson", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T14:31:03.000Z", "jev": 0.6595, "judge_a": 0.557, "judge_b": 0.7246, "score": 0.647, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 22, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Nolan Lawson benchmarked 12 JavaScript frameworks by having Claude Code (Opus 5.5) build the same Pokémon app 3–5 times each. Median cost ran from $2.67 for Hono+htmx to $4.97 for Angular, which also wrote the most code (about 2,000 lines versus 1,370). Lighthouse performance and accessibility scores were near ceiling everywhere; JavaScript shipped ranged from 0 KiB (Astro) to 142 KiB (Next.js). Caveats: one content-heavy app, high run-to-run variance, and Lighthouse favors multi-page sites.", "page": "https://rubric.hrih.si/editions/2026-10-29/12/", "card": "https://rubric.hrih.si/og/2026-10-29/12.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.558, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5092, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.76, "spread": 0.19 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.667, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6489, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.755, "judge_a": 0.65, "judge_b": 0.74, "mean": 0.715, "spread": 0.105 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.98, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.8, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.923, "judge_a": 0.7, "judge_b": 0.74, "mean": 0.7875, "spread": 0.223 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.97, "mean": 0.8867, "spread": 0.17 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0467, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.27, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2667, "spread": 0.17 } } }, { "rank": 13, "id": "st_20261029_6522", "title": "Model is being retired. Are you ready to upgrade?", "url": "https://mahalsystems.ai/research/what-survives-a-model-swap/", "domain": "mahalsystems.ai", "site": "mahalsystems.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T12:39:38.000Z", "jev": 0.7507, "judge_a": 0.57, "judge_b": 0.6673, "score": 0.6626, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 17, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Mahal Systems swapped the model under six small agents (recommendation, tool routing, SQL, retrieval, extraction, error recovery) and tested Claude Haiku 4.5 to 5.5 and GPT-5.4 nano to GPT-6 Luna. The lab's successor lowered the score in 3 of 12 upgrades, and 11 of 12 regressed some cases. Switching labs matched or beat the successor in 10 of 12. Rules in backend code mostly survived; prompts and tool descriptions did not. The tests used small models, reasoning off, 20 cases, and figures come from the vendor's own benchmarks.", "page": "https://rubric.hrih.si/editions/2026-10-29/13/", "card": "https://rubric.hrih.si/og/2026-10-29/13.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.693, "judge_a": 0.45, "judge_b": 0.73, "mean": 0.6242, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.8, "judge_b": 0.86, "mean": 0.82, "spread": 0.06 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.693, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.6544, "spread": 0.093 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.93, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.85, "spread": 0.13 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.867, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6956, "spread": 0.267 }, "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": 0.88, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.88, "spread": 0.16 }, "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": 0.29, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4067, "spread": 0.29 } } }, { "rank": 14, "id": "st_20261029_6630", "title": "Making SIMD Code Scale to Any Vector Width", "url": "https://streamhpc.com/blog/2026-10-28/making-simd-code-scale-to-any-vector-width/", "domain": "streamhpc.com", "site": "Stream HPC", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-28T14:23:20.000Z", "jev": 0.6175, "judge_a": 0.5533, "judge_b": 0.7252, "score": 0.632, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 30, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Stream HPC shows how to make SIMD code scale with vector width by storing vector components as lanes, one object per lane, instead of one vec3/vec4 per object. In a frustum-culling example on a Ryzen 7 5800 with Clang, cost fell from 13.4 ns to 0.67 ns per box on AVX2 and 1.84 ns on SSE. Gains came from splatting planes once outside the loop and writing results a block at a time.", "page": "https://rubric.hrih.si/editions/2026-10-29/14/", "card": "https://rubric.hrih.si/og/2026-10-29/14.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.502, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4742, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.72, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.4056, "spread": 0.097 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.745, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7417, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.99, "judge_a": 0.75, "judge_b": 0.9, "mean": 0.88, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 1, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.95, "spread": 0.1 }, "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": 0.04, "judge_a": 0.03, "judge_b": 0.01, "mean": 0.0267, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1633, "spread": 0.13 } } }, { "rank": 15, "id": "st_20261029_6808", "title": "26,400 live credentials found scanning 7.25M repos on China's Gitee", "url": "https://www.mongodb.com/company/blog/technical/scanning-7-point-25-million-public-repositories-chinas-largest-git-platform-for-secrets", "domain": "mongodb.com", "site": "MongoDB", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T15:31:59.000Z", "jev": 0.7047, "judge_a": 0.6201, "judge_b": 0.789, "score": 0.7045, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 8, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A researcher scanned 7,256,531 public Gitee repositories with TruffleHog and Kingfisher and found 26,400 unique credentials that authenticated against vendor APIs, with Alibaba Cloud keys the most common. The run took about a month and cost $3,097.64, covered by Modal credits. That works out to 3.64 per 1,000 repositories, versus 3.11 for GitLab and 2.39 for Bitbucket, but those earlier scans used TruffleHog alone; TruffleHog-only, Gitee falls to 1.76. Each scanner alone found about half, so detector coverage matters. Revocation of disclosed keys is unconfirmed.", "page": "https://rubric.hrih.si/editions/2026-10-29/15/", "card": "https://rubric.hrih.si/og/2026-10-29/15.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.668, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6492, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8167, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.67, "judge_b": 0.68, "mean": 0.7011, "spread": 0.083 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.683, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7208, "spread": 0.098 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.887, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8156, "spread": 0.187 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.985, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8483, "spread": 0.285 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9133, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.0967, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2167, "spread": 0.13 } } }, { "rank": 16, "id": "st_20261029_6812", "title": "UK to look at making software companies liable for cyber breaches", "url": "https://therecord.media/uk-budget-proposal-software-companies-liable-cyber-breaches", "domain": "therecord.media", "site": "The Record (Recorded Future News)", "categories": [ "security", "policy" ], "category_names": [ "Security", "Policy & regulation" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T15:37:00.000Z", "jev": 0.6424, "judge_a": 0.511, "judge_b": 0.7121, "score": 0.6218, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 38, "topic_penalty": 0, "repeat_penalty": 0, "summary": "The UK budget commits the government to consult next year on where liability for cyber breaches from insecure technology should sit, building on a Law Commission review that would extend product law to software and AI. Scope, lead department and whether it means civil liability or statutory duties remain unspecified. The EU is already moving on fines and strict liability for software, and vendor groups are expected to resist. The budget also allocates £154 million for AI-based cyber defenses, including £70 million for an NCSC vulnerability centre.", "page": "https://rubric.hrih.si/editions/2026-10-29/16/", "card": "https://rubric.hrih.si/og/2026-10-29/16.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.728, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6658, "spread": 0.178 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6967, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7333, "spread": 0.28 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.585, "judge_a": 0.45, "judge_b": 0.65, "mean": 0.5617, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.53, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.63, "spread": 0.23 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.323, "judge_a": 0.15, "judge_b": 0.38, "mean": 0.2842, "spread": 0.23 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.8833, "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": 0.24, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1733, "spread": 0.16 } } }, { "rank": 17, "id": "st_20261029_6861", "title": "GitHub powering up four million extra cores to stop its service going down", "url": "https://www.theregister.com/ai-and-ml/2026/10/28/github-powering-up-four-million-extra-cores-to-stop-its-service-going-down/5305863", "domain": "theregister.com", "site": "The Register", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T16:30:00.000Z", "jev": 0.5541, "judge_a": 0.5805, "judge_b": 0.7185, "score": 0.6176, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 41, "topic_penalty": 0, "repeat_penalty": 0, "summary": "GitHub said at its Universe conference that it handled 7.4 billion commits in September, about five times the volume of a year earlier, driven largely by AI agents, and that the load has caused repeated outages. It reports adding four million CPU cores, 160 petabytes of high-speed storage and more Actions capacity. It is also separating compute from storage, which internal benchmarks suggest could raise write throughput up to 35x, and expects its multi-region Azure migration to finish early next year. These figures are GitHub's own and unverified.", "page": "https://rubric.hrih.si/editions/2026-10-29/17/", "card": "https://rubric.hrih.si/og/2026-10-29/17.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.713, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6608, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.7733, "spread": 0.25 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.657, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7456, "spread": 0.143 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.548, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6192, "spread": 0.213 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.46, "judge_a": 0.45, "judge_b": 0.61, "mean": 0.5067, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.523, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4575, "spread": 0.25 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.5, "judge_b": 0.99, "mean": 0.7967, "spread": 0.49 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.19, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.19, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2467, "spread": 0.18 } } }, { "rank": 18, "id": "st_20261029_6837", "title": "Nvidia Nemotron 3.5 Transcribe", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "domain": "huggingface.co", "site": "huggingface.co", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "claim_checked", "published_at": "2026-10-28T16:17:58.000Z", "jev": 0.6981, "judge_a": 0.4625, "judge_b": 0.8098, "score": 0.6567, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 18, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "Nvidia released Nemotron 3.5 Transcribe, an open-weight offline model (34B total, about 4.3B active parameters) covering transcription in 40 languages, translation, speaker labels and audio Q&A for recordings up to an hour. Its own tables show a 4.71 mean WER on nine English Open ASR sets, against 4.94 for Qwen3-ASR-1.7B. The 2,238x throughput figure comes from Nvidia's NIM on one Blackwell GPU; the open-source vLLM path is about two times slower. These results are Nvidia's own and not independently verified.", "page": "https://rubric.hrih.si/editions/2026-10-29/18/", "card": "https://rubric.hrih.si/og/2026-10-29/18.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.71, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6467, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.6833, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5667, "spread": 0.24 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.923, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.8008, "spread": 0.323 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.97, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8433, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8767, "spread": 0.29 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.1733, "spread": 0.2 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2367, "spread": 0.17 } } }, { "rank": 19, "id": "st_20261029_6821", "title": "Who Is the Agent Acting As? Why AI Agents Become Confused Deputies", "url": "https://dvlog.dev/posts/agent-authorization-confused-deputy/", "domain": "dvlog.dev", "site": "dvlog", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-28T16:26:35.000Z", "jev": 0.7792, "judge_a": 0.563, "judge_b": 0.7457, "score": 0.696, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 10, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A blog post argues that an AI agent running on one broad service account becomes a confused deputy: a prompt injection in a support ticket can produce a fully authorized write to another tenant. Its fix is a gateway outside the model that checks each tool call against user, agent and tenant permissions, denies by default, and takes the tenant from verified identity. It favors delegation over impersonation, per OAuth token exchange. These are the author's recommendations, not independently tested results.", "page": "https://rubric.hrih.si/editions/2026-10-29/19/", "card": "https://rubric.hrih.si/og/2026-10-29/19.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.715, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5983, "spread": 0.315 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.8, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7733, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6367, "spread": 0.09 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.95, "judge_a": 0.75, "judge_b": 0.86, "mean": 0.8533, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.783, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7044, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.8433, "spread": 0.29 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.83, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.77, "spread": 0.28 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.0767, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1867, "spread": 0.02 } } }, { "rank": 20, "id": "st_20261029_6881", "title": "Pre-auth admin takeover on 28 Xiaomi/Redmi routers via hardcoded key", "url": "https://github.com/ADCDS/xiaomi-ax3000t-cabmeshd-disclosure", "domain": "github.com", "site": "GitHub", "categories": [ "security", "it", "hardware" ], "category_names": [ "Security", "IT & networking", "Hardware & devices" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-28T16:49:19.000Z", "jev": 0.6537, "judge_a": 0.652, "judge_b": 0.7808, "score": 0.6955, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 11, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A researcher published an unpatched disclosure for Xiaomi's mesh daemon cab_meshd, including working exploit code. A hardcoded HMAC key, identical in 28 Xiaomi and Redmi firmware images, lets a LAN or Wi-Fi client obtain admin access without a password. A separate command injection gave root on the AX3000T (RD03v2) on hardware, but Xiaomi's normal setup blocked the CAP-side routes. Runtime takeover was not tested on every model. Xiaomi had not confirmed reproduction after 45 days, so owners have no fix to apply.", "page": "https://rubric.hrih.si/editions/2026-10-29/20/", "card": "https://rubric.hrih.si/og/2026-10-29/20.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.675, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6983, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.76, "judge_a": 0.85, "judge_b": 0.93, "mean": 0.8467, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.343, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.4211, "spread": 0.157 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.723, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7008, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.99, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.9267, "spread": 0.14 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9592, "spread": 0.098 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9267, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0367, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.3, "judge_b": 0.22, "mean": 0.3167, "spread": 0.21 } } }, { "rank": 21, "id": "st_20261029_7340", "title": "Amazon-Themed ClickFix Campaign Serves Up Finger-Licking Malware", "url": "https://www.huntress.com/blog/amazon-clickfix-finger", "domain": "huntress.com", "site": "Huntress", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "claim_checked", "published_at": "2026-10-28T23:57:03.000Z", "jev": 0.6636, "judge_a": 0.6696, "judge_b": 0.7388, "score": 0.6907, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 12, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Huntress researchers documented an Amazon-themed ClickFix campaign that lured victims via a sponsored Google result to a spoofed Amazon login page, then tricked them into pasting a concealed command into the Windows Run dialog.", "page": "https://rubric.hrih.si/editions/2026-10-29/21/", "card": "https://rubric.hrih.si/og/2026-10-29/21.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.595, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.605, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8133, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.73, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6733, "spread": 0.11 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.635, "judge_a": 0.75, "judge_b": 0.82, "mean": 0.735, "spread": 0.185 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.933, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.9111, "spread": 0.033 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.9, "judge_b": 0.92, "mean": 0.9383, "spread": 0.095 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.85, "spread": 0.24 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.1, "judge_b": 0.32, "mean": 0.16, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.18, "spread": 0.23 } } }, { "rank": 22, "id": "st_20261029_6507", "title": "Same-generation CPUs from 4 China clouds are within 8%, storage differs 6x", "url": "https://chinaready.co/research/china-cloud-vm-benchmark-report/", "domain": "chinaready.co", "site": "Chinaready", "categories": [ "devops" ], "category_names": [ "Infrastructure & DevOps" ], "format": "Paper", "depth": "claim_checked", "published_at": "2026-10-28T12:55:28.000Z", "jev": 0.6479, "judge_a": 0.467, "judge_b": 0.7025, "score": 0.6057, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 50, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A cross-cloud benchmark of 35 VMs across Alibaba Cloud, Tencent Cloud, AWS China, and Azure China, run over three time windows in October 2026, finds that same-generation CPUs perform within about 8% of one another, with memory differences of up to roughly 16% and storage differing by as much as…", "page": "https://rubric.hrih.si/editions/2026-10-29/22/", "card": "https://rubric.hrih.si/og/2026-10-29/22.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.573, "judge_a": 0.35, "judge_b": 0.65, "mean": 0.5242, "spread": 0.3 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.6967, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.673, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.5411, "spread": 0.273 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.773, "judge_a": 0.6, "judge_b": 0.75, "mean": 0.7075, "spread": 0.173 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.987, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.8022, "spread": 0.387 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8233, "spread": 0.29 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.95, "mean": 0.9267, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.15, "judge_b": 0.03, "mean": 0.08, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.55, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.36, "spread": 0.37 } } }, { "rank": 23, "id": "st_20261029_6421", "title": "I scanned 3 months of Product Hunt launches for security issues", "url": "https://squirrelscan.com/blog/product-hunt-launches-audit", "domain": "squirrelscan.com", "site": "squirrelscan", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T11:56:10.000Z", "jev": 0.7712, "judge_a": 0.497, "judge_b": 0.778, "score": 0.6821, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 13, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "I scanned three months of Product Hunt launches, 72,356 in total, with a passive surface-level crawl of up to 30 pages per site using the open-source squirrelscan CLI. Each finding was checked by hand.", "page": "https://rubric.hrih.si/editions/2026-10-29/23/", "card": "https://rubric.hrih.si/og/2026-10-29/23.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.655, "judge_a": 0.4, "judge_b": 0.74, "mean": 0.5983, "spread": 0.34 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.81, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8033, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.837, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7189, "spread": 0.237 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.95, "judge_a": 0.75, "judge_b": 0.84, "mean": 0.8467, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.963, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8478, "spread": 0.263 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.8142, "spread": 0.283 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.8233, "spread": 0.38 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2333, "spread": 0.38 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.39, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.2733, "spread": 0.31 } } }, { "rank": 24, "id": "st_20261029_6789", "title": "Unix and Beyond: An Interview with Ken Thompson", "url": "https://www.cs.princeton.edu/courses/archive/spring03/cs333/thompson", "domain": "cs.princeton.edu", "site": "cs.princeton.edu", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Other", "depth": "three_judges", "published_at": "2026-10-28T15:49:00.000Z", "jev": 0.5425, "judge_a": 0.597, "judge_b": 0.7207, "score": 0.6201, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 39, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "This Computer interview features Ken Thompson, co-creator of Unix, Plan 9, and Inferno, discussing his bottom-up design approach, how small, closely interacting groups at Bell Labs resolve technical disputes by prototyping alternatives, and what he sees as Unix's strengths and missed opportunities, notably its underdeveloped treatment of remoteness.", "page": "https://rubric.hrih.si/editions/2026-10-29/24/", "card": "https://rubric.hrih.si/og/2026-10-29/24.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.525, "judge_a": 0.55, "judge_b": 0.8, "mean": 0.625, "spread": 0.275 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.8, "judge_b": 0.85, "mean": 0.7233, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.863, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7544, "spread": 0.263 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.465, "judge_a": 0.5, "judge_b": 0.65, "mean": 0.5383, "spread": 0.185 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.317, "judge_a": 0.45, "judge_b": 0.75, "mean": 0.5056, "spread": 0.433 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.725, "judge_a": 0.55, "judge_b": 0.75, "mean": 0.675, "spread": 0.2 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 1, "mean": 0.9467, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0433, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.15, "judge_b": 0.55, "mean": 0.2967, "spread": 0.4 } } }, { "rank": 25, "id": "st_20261029_6891", "title": "Fast Models, Slow Agents", "url": "https://armincat.com/articles/fast-models-slow-agents", "domain": "armincat.com", "site": "armincat.com", "categories": [ "ai", "data" ], "category_names": [ "AI & machine learning", "Data" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T16:39:01.000Z", "jev": 0.6849, "judge_a": 0.5655, "judge_b": 0.7147, "score": 0.6551, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 19, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "Funnel ran a randomised A/B test of GLM-5.2 against Claude Opus 4.8 in its internal data agent platform, Reef. GLM responded more than twice as fast per model call, but made 66% more tool calls and took 14% longer to finish tasks.", "page": "https://rubric.hrih.si/editions/2026-10-29/25/", "card": "https://rubric.hrih.si/og/2026-10-29/25.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.565, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4983, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.7633, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.56, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.5933, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.895, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8383, "spread": 0.095 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.69, "spread": 0.15 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.755, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6783, "spread": 0.155 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.85, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.8867, "spread": 0.11 }, "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": 0.18, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.1667, "spread": 0.08 } } }, { "rank": 26, "id": "st_20261029_6605", "title": "Search golden sets are anything but", "url": "https://softwaredoug.com/blog/2026/10/26/search-golden-sets-fail", "domain": "softwaredoug.com", "site": "Doug Turnbull's Blog", "categories": [ "data" ], "category_names": [ "Data" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T13:37:11.000Z", "jev": 0.5729, "judge_a": 0.484, "judge_b": 0.6996, "score": 0.5855, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 73, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A critique of standard search evaluation argues that golden sets built from BEIR-style judgment lists can mislead teams about real relevance. The author walks through NDCG using ESCI and WANDS examples, showing how a poor ideal ranking can make weak results look acceptable.", "page": "https://rubric.hrih.si/editions/2026-10-29/26/", "card": "https://rubric.hrih.si/og/2026-10-29/26.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": 0.52, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.6567, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.557, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.5022, "spread": 0.157 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7567, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.497, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6056, "spread": 0.223 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.945, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.8083, "spread": 0.245 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.91, "mean": 0.91, "spread": 0.02 }, "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": 0.26, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2133, "spread": 0.08 } } }, { "rank": 27, "id": "st_20261029_7027", "title": "noalloc: Tooling and libraries to enforce zero heap allocations in Golang", "url": "https://github.com/theconsensuslabs/noalloc", "domain": "github.com", "site": "GitHub", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T18:29:25.000Z", "jev": 0.6192, "judge_a": 0.5865, "judge_b": 0.6543, "score": 0.62, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 40, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "noalloc is an experimental Go toolchain that asserts functions must not heap-allocate and checks the full call tree of any function marked //noalloc:root, reporting each allocating line for fixing or exemption.", "page": "https://rubric.hrih.si/editions/2026-10-29/27/", "card": "https://rubric.hrih.si/og/2026-10-29/27.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4633, "spread": 0.11 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7367, "spread": 0.17 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.433, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.4511, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.768, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.7425, "spread": 0.068 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.7, "judge_b": 0.83, "mean": 0.8289, "spread": 0.257 }, "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": 0.95, "judge_b": 0.98, "mean": 0.9467, "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": 0.2, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1933, "spread": 0.22 } } }, { "rank": 28, "id": "st_20261029_6996", "title": "OpenTaxRate – open-source US sales tax rates by street address", "url": "https://github.com/equated-org/opentaxrate", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "data" ], "category_names": [ "Programming & dev tools", "Data" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-28T17:40:49.000Z", "jev": 0.6269, "judge_a": 0.457, "judge_b": 0.6502, "score": 0.578, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 79, "topic_penalty": 0, "repeat_penalty": 0, "summary": "OpenTaxRate is an MIT-licensed open-source tool that returns the general US sales tax rate for a street address and date, built from state address ranges, tax-area boundaries, and dated rates published by tax agencies.", "page": "https://rubric.hrih.si/editions/2026-10-29/28/", "card": "https://rubric.hrih.si/og/2026-10-29/28.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.495, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.475, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6333, "spread": 0.23 }, "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": 0.865, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7817, "spread": 0.165 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.967, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8289, "spread": 0.267 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.84, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.72, "spread": 0.24 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.95, "judge_b": 0.96, "mean": 0.94, "spread": 0.05 }, "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": 0.12, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1333, "spread": 0.12 } } }, { "rank": 29, "id": "st_20261029_6998", "title": "Orchestrating a One-Person Software Factory", "url": "https://oskargroth.com/blog/orchestrating-agents", "domain": "oskargroth.com", "site": "oskargroth.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T17:38:17.000Z", "jev": 0.7379, "judge_a": 0.4815, "judge_b": 0.7377, "score": 0.6523, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 20, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An indie developer describes running a one-person software company by using an orchestrator agent that spawns and coordinates parallel coding agents, with GitHub issues and pull requests serving as the shared record.", "page": "https://rubric.hrih.si/editions/2026-10-29/29/", "card": "https://rubric.hrih.si/og/2026-10-29/29.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.633, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.5375, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.78, "spread": 0.16 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.723, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6478, "spread": 0.223 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.905, "judge_a": 0.7, "judge_b": 0.84, "mean": 0.815, "spread": 0.205 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.867, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6956, "spread": 0.367 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.895, "judge_a": 0.6, "judge_b": 0.73, "mean": 0.7417, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.6, "judge_b": 0.92, "mean": 0.8067, "spread": 0.32 }, "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": 0.27, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.3, "spread": 0.08 } } }, { "rank": 30, "id": "st_20261029_6573", "title": "Forging a Microsoft Edge Full Chain with Logic Bugs Only", "url": "https://blog.orange.tw/posts/2026-10-the-last-proof-of-human-craft-browser-full-chain/", "domain": "blog.orange.tw", "site": "Orange Tsai", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T13:34:06.000Z", "jev": 0.5559, "judge_a": 0.5847, "judge_b": 0.8104, "score": 0.6503, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 21, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A researcher at DEVCORE, Orange Tsai, published a write-up of a fully logic-based Microsoft Edge exploit chain built for Pwn2Own Berlin 2026, which earned a $175,000 sandbox escape and was nominated for a Pwnie Award. The chain relies on five bugs and two Windows features, with no memory corruption.", "page": "https://rubric.hrih.si/editions/2026-10-29/30/", "card": "https://rubric.hrih.si/og/2026-10-29/30.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.61, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.6667, "spread": 0.29 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.82, "judge_b": 0.96, "mean": 0.7967, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.517, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5456, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.535, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5717, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.783, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8211, "spread": 0.097 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.968, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9325, "spread": 0.13 }, "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": 0.11, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0967, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.42, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2667, "spread": 0.24 } } }, { "rank": 31, "id": "st_20261029_6713", "title": "Accelerating Ubuntu Core Desktop", "url": "https://jnsgr.uk/2026/10/accelerating-ubuntu-core-desktop/", "domain": "jnsgr.uk", "site": "Jon Seager", "categories": [ "programming", "it", "product" ], "category_names": [ "Programming & dev tools", "IT & networking", "Product" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T14:31:40.000Z", "jev": 0.5742, "judge_a": 0.4565, "judge_b": 0.6716, "score": 0.5674, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 90, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Canonical has restarted development of Ubuntu Core Desktop, an immutable, attestable Linux desktop in which every component, including the bootloader, kernel, desktop environment and apps, ships as a snap.", "page": "https://rubric.hrih.si/editions/2026-10-29/31/", "card": "https://rubric.hrih.si/og/2026-10-29/31.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.638, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6358, "spread": 0.17 }, "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": 0.617, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6289, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.653, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5942, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.35, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.4733, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.685, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.605, "spread": 0.235 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.92, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2067, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.25, "judge_b": 0.48, "mean": 0.3433, "spread": 0.23 } } }, { "rank": 32, "id": "st_20261029_6942", "title": "MCP clients now lazy-load tools, and our gateway's token savings mostly vanished", "url": "https://mcprating.io/blog/clients-load-tool-names-now", "domain": "mcprating.io", "site": "mcprating.io", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T17:06:16.000Z", "jev": 0.6422, "judge_a": 0.5515, "judge_b": 0.7359, "score": 0.6432, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 24, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Mcprating.io reports that Claude Code, Cursor and Kiro now load MCP tool definitions lazily by default, so a configured server adds only its tool names to each request rather than its full schemas.", "page": "https://rubric.hrih.si/editions/2026-10-29/32/", "card": "https://rubric.hrih.si/og/2026-10-29/32.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.535, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5017, "spread": 0.27 }, "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": 0.437, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5056, "spread": 0.143 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.758, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.7625, "spread": 0.03 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.85, "judge_b": 0.91, "mean": 0.9178, "spread": 0.143 }, "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": 0.88, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.87, "spread": 0.13 }, "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": 0.2, "judge_a": 0.2, "judge_b": 0.03, "mean": 0.1433, "spread": 0.17 } } }, { "rank": 33, "id": "st_20261029_6700", "title": "Algeon – Compose GPU graph, vector, and ML algorithms in SQL", "url": "https://github.com/algeon-dev/algeon", "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": "2026-10-28T14:47:23.000Z", "jev": 0.5606, "judge_a": 0.448, "judge_b": 0.6794, "score": 0.5626, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 95, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Algeon is an experimental GPU-accelerated query engine built on Apache DataFusion that exposes NVIDIA cuGraph, cuVS, and cuML algorithms as SQL table functions, alongside cuDF relational execution.", "page": "https://rubric.hrih.si/editions/2026-10-29/33/", "card": "https://rubric.hrih.si/og/2026-10-29/33.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.515, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6467, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.387, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.4689, "spread": 0.233 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.66, "spread": 0.12 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.917, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.7289, "spread": 0.467 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.808, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7758, "spread": 0.12 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.95, "judge_b": 0.96, "mean": 0.95, "spread": 0.02 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.35, "judge_b": 0.12, "mean": 0.1933, "spread": 0.24 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.15, "judge_a": 0.35, "judge_b": 0.08, "mean": 0.1933, "spread": 0.27 } } }, { "rank": 34, "id": "st_20261029_6665", "title": "Apple iPhone Duo review: Two screens, two experiences, two thousand dollars", "url": "https://arstechnica.com/gadgets/2026/10/apple-iphone-duo-review-two-screens-two-experiences-two-thousand-dollars/", "domain": "arstechnica.com", "site": "Ars Technica", "categories": [ "hardware", "design" ], "category_names": [ "Hardware & devices", "Design & UX" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T14:02:23.000Z", "jev": 0.4113, "judge_a": 0.5696, "judge_b": 0.705, "score": 0.5619, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 98, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Ars Technica's reviewer tests Apple's first foldable, the $1,999 iPhone Duo, and finds it impressive as a small iPad but frustrating as a phone. The review cites a seam that is nearly invisible when unfolded, an awkward right-side control layout, poor one-handed comfort, and the loss of Face ID and…", "page": "https://rubric.hrih.si/editions/2026-10-29/34/", "card": "https://rubric.hrih.si/og/2026-10-29/34.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.36, "judge_a": 0.6, "judge_b": 0.65, "mean": 0.5367, "spread": 0.29 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.32, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.6333, "spread": 0.56 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.34, "judge_a": 0.67, "judge_b": 0.63, "mean": 0.5467, "spread": 0.33 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.538, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5025, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.617, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.6789, "spread": 0.103 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.738, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5892, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9267, "spread": 0.14 }, "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": 0.22, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1333, "spread": 0.14 } } }, { "rank": 35, "id": "st_20261029_7267", "title": "Training Reve 2.0 at Scale", "url": "https://app.reve.com/training-infra", "domain": "app.reve.com", "site": "Reve", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T21:37:39.000Z", "jev": 0.5967, "judge_a": 0.516, "judge_b": 0.8097, "score": 0.6408, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 25, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Reve Research published a technical report on the training infrastructure behind its Reve 2.0 and 2.1 image models, covering a platform built on Kubernetes, Ray, and Google Cloud.", "page": "https://rubric.hrih.si/editions/2026-10-29/35/", "card": "https://rubric.hrih.si/og/2026-10-29/35.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.66, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6433, "spread": 0.37 }, "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": 0.413, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5444, "spread": 0.307 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.725, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.735, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.62, "judge_a": 0.55, "judge_b": 0.8, "mean": 0.6567, "spread": 0.25 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9333, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.16, "spread": 0.14 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.53, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.3867, "spread": 0.25 } } }, { "rank": 36, "id": "st_20261029_6504", "title": "UEFI pre-driver Nvidia GPU unlocker for Windows", "url": "https://github.com/physicsdolphin/nv-uefi-unlocker", "domain": "github.com", "site": "GitHub", "categories": [ "hardware" ], "category_names": [ "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T12:58:57.000Z", "jev": 0.4735, "judge_a": 0.514, "judge_b": 0.6926, "score": 0.56, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 100, "topic_penalty": 0, "repeat_penalty": 0, "summary": "A GitHub project called nv-uefi-unlocker, shared on Hacker News, offers a UEFI pre-driver tool that runs on a USB boot stick before Windows loads and attempts to unlock NVIDIA GeForce RTX 20, 30 and 40 series GPUs by replaying a firmware sequence that includes GSP-RM boot and a vendor payload…", "page": "https://rubric.hrih.si/editions/2026-10-29/36/", "card": "https://rubric.hrih.si/og/2026-10-29/36.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.498, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5892, "spread": 0.222 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.6967, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.363, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.4311, "spread": 0.117 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.433, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.5442, "spread": 0.267 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.887, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7556, "spread": 0.287 }, "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": 0.95, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.92, "spread": 0.11 }, "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": 0.45, "judge_a": 0.5, "judge_b": 0.42, "mean": 0.4567, "spread": 0.08 } } }, { "rank": 37, "id": "st_20261029_7083", "title": "Are you paying for useless LLM prompts?", "url": "https://cameronmpalmer.com/blog/are-you-paying-for-useless-llm-prompts/", "domain": "cameronmpalmer.com", "site": "cameronmpalmer.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T18:38:23.000Z", "jev": 0.6524, "judge_a": 0.5355, "judge_b": 0.7314, "score": 0.6398, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 26, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An experiment tested whether persona prompts (e.g., \"You are an expert\") affect accuracy, cost, and speed for LLMs answering 5,000 MMLU-Pro multiple-choice questions across four models. Accuracy was largely flat, but several models, notably Deepseek and Ling, used significantly more reasoning tokens with expert personas, raising cost and latency.", "page": "https://rubric.hrih.si/editions/2026-10-29/37/", "card": "https://rubric.hrih.si/og/2026-10-29/37.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4625, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.74, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.73, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.65, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.89, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.79, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.983, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.8011, "spread": 0.383 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.943, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7475, "spread": 0.343 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.92, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.3, "judge_b": 0.2, "mean": 0.32, "spread": 0.26 } } }, { "rank": 38, "id": "st_20261029_6899", "title": "Trapped in the Technologist Factory", "url": "https://ideolalia.com/essays/trapped-in-the-technologist-factory.html", "domain": "ideolalia.com", "site": "ideolalia.com", "categories": [ "programming", "product", "startups" ], "category_names": [ "Programming & dev tools", "Product", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T16:30:38.000Z", "jev": 0.5142, "judge_a": 0.4552, "judge_b": 0.6991, "score": 0.5561, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 105, "topic_penalty": 0, "repeat_penalty": 0, "summary": "This first-person essay recounts a software engineer's career across two startups, Runa and Factual, both of which failed to reach the growth their investors expected. Runa's dynamic-pricing business never won retailer trust, and Factual's data business drifted into location-based advertising, which the author came to find distasteful.", "page": "https://rubric.hrih.si/editions/2026-10-29/38/", "card": "https://rubric.hrih.si/og/2026-10-29/38.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.345, "judge_a": 0.3, "judge_b": 0.68, "mean": 0.4417, "spread": 0.38 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.72, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.94, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7533, "spread": 0.34 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.488, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5025, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.223, "judge_a": 0.35, "judge_b": 0.78, "mean": 0.4511, "spread": 0.557 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.683, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.5208, "spread": 0.383 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.72, "mean": 0.8467, "spread": 0.2 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.03, "spread": 0.05 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.28, "spread": 0.17 } } }, { "rank": 39, "id": "st_20261029_6471", "title": "You Don't Understand Your Code", "url": "https://george.mand.is/2026/10/you-already-dont-understand-your-code/", "domain": "george.mand.is", "site": "George Mandis", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T12:07:11.000Z", "jev": 0.6522, "judge_a": 0.4795, "judge_b": 0.7148, "score": 0.6154, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 42, "topic_penalty": 0.06, "repeat_penalty": 0, "summary": "This Hrih News piece argues that developers often believe they understand their code when they don't, using a series of sorting examples across languages and JavaScript engines. It shows that identical-looking sort calls run different algorithms with different performance and stability characteristics, depending on the language, compiler standard library, or…", "page": "https://rubric.hrih.si/editions/2026-10-29/39/", "card": "https://rubric.hrih.si/og/2026-10-29/39.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.518, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4792, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.78, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.977, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.7256, "spread": 0.477 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.57, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.53, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.87, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8358, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.89, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.0433, "spread": 0.01 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.47, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.3167, "spread": 0.29 } } }, { "rank": 40, "id": "st_20261029_6536", "title": "More Is Less", "url": "https://krmannix.substack.com/p/more-is-less", "domain": "krmannix.substack.com", "site": "krmannix.substack.com", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T13:30:27.000Z", "jev": 0.5967, "judge_a": 0.4792, "judge_b": 0.5886, "score": 0.5548, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 107, "topic_penalty": 0, "repeat_penalty": 0, "summary": "Microsoft has offered voluntary buyouts to about 60,000 employees, roughly 27% of its global workforce, while it spends heavily on AI and continues strong revenue growth.", "page": "https://rubric.hrih.si/editions/2026-10-29/40/", "card": "https://rubric.hrih.si/og/2026-10-29/40.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.725, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.6183, "spread": 0.275 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.7133, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.987, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8689, "spread": 0.187 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.643, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5875, "spread": 0.142 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.313, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.3311, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.283, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1875, "spread": 0.182 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.79, "judge_a": 0.4, "judge_b": 0.95, "mean": 0.7133, "spread": 0.55 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.06, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.58, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.65, "spread": 0.27 } } }, { "rank": 41, "id": "st_20261029_6989", "title": "A RISC-V CPU compiled into the weights of an LLM", "url": "https://github.com/divagr18/qwenputer", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T17:45:29.000Z", "jev": 0.6135, "judge_a": 0.5295, "judge_b": 0.7604, "score": 0.6344, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 28, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Qwenputer is an experimental Qwen3.5-4B model with a 32-bit RISC-V CPU compiled directly into its weights, with no learned parameters in the computer layers. The CPU uses the model's Gated DeltaNet state as RAM and runs C and MicroPython inside forward passes.", "page": "https://rubric.hrih.si/editions/2026-10-29/41/", "card": "https://rubric.hrih.si/og/2026-10-29/41.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.598, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6425, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.8533, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.523, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5811, "spread": 0.097 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.5, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5067, "spread": 0.42 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.8244, "spread": 0.373 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9358, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.95, "judge_b": 0.99, "mean": 0.9533, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1533, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.45, "judge_b": 0.22, "mean": 0.32, "spread": 0.23 } } }, { "rank": 42, "id": "st_20261029_6565", "title": "Anthropic's S-1 explains the unnamed deals in Google, Broadcom, SpaceX filings", "url": "https://equibles.com/research/anthropics-s-1-checked-against-amazon-google-microsoft-and-broadcom-filings", "domain": "equibles.com", "site": "Equibles", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T13:02:37.000Z", "jev": 0.4916, "judge_a": 0.6015, "judge_b": 0.8008, "score": 0.6312, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 31, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The S-1 Anthropic filed on October 26, 2026 describes its largest compute and funding contracts without naming the partners, and Hrih News matched those terms against the 10-Qs and earnings disclosures of Alphabet, Broadcom, SpaceX and Amazon.", "page": "https://rubric.hrih.si/editions/2026-10-29/42/", "card": "https://rubric.hrih.si/og/2026-10-29/42.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.588, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.7158, "spread": 0.272 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.79, "spread": 0.42 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.583, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6344, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.298, "judge_a": 0.35, "judge_b": 0.63, "mean": 0.4258, "spread": 0.333 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.917, "judge_a": 0.85, "judge_b": 0.88, "mean": 0.8822, "spread": 0.067 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.515, "judge_a": 0.3, "judge_b": 0.67, "mean": 0.495, "spread": 0.37 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9167, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0433, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.36, "judge_a": 0.3, "judge_b": 0.12, "mean": 0.26, "spread": 0.24 } } }, { "rank": 43, "id": "st_20261029_6767", "title": "Google Begins Certifying Registered App Stores For Android", "url": "https://tech.slashdot.org/story/26/10/28/0313217/google-begins-certifying-registered-app-stores-for-android", "domain": "tech.slashdot.org", "site": "Slashdot", "categories": [ "policy" ], "category_names": [ "Policy & regulation" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T15:00:00.000Z", "jev": 0.4937, "judge_a": 0.5421, "judge_b": 0.6354, "score": 0.557, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 104, "topic_penalty": 0, "repeat_penalty": 0.0082, "summary": "Google has opened applications for its Registered App Stores program, a voluntary route for alternative Android app stores outside the U.S. and Europe, following its loss in the Epic Games antitrust case.", "page": "https://rubric.hrih.si/editions/2026-10-29/43/", "card": "https://rubric.hrih.si/og/2026-10-29/43.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.583, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5842, "spread": 0.07 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.48, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.62, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.503, "judge_a": 0.67, "judge_b": 0.72, "mean": 0.6311, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.525, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.535, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.44, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5233, "spread": 0.24 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.558, "judge_a": 0.2, "judge_b": 0.22, "mean": 0.3258, "spread": 0.358 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.9, "mean": 0.9033, "spread": 0.01 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.0667, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1733, "spread": 0.14 } } }, { "rank": 44, "id": "st_20261029_6160", "title": "I ran each AI coding-agent sandbox's own tests against the others", "url": "https://github.com/carlostapiacl/sandbox-conformance", "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": "2026-10-28T03:35:56.000Z", "jev": 0.6699, "judge_a": 0.432, "judge_b": 0.7843, "score": 0.6287, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 32, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An open lab called sandbox-conformance turns the security claims of AI coding-agent sandboxes into shared, runnable test contracts, each split into an attack, a control and an on-disk or on-wire effect check.", "page": "https://rubric.hrih.si/editions/2026-10-29/44/", "card": "https://rubric.hrih.si/og/2026-10-29/44.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.593, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5542, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.75, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.563, "judge_a": 0.5, "judge_b": 0.67, "mean": 0.5778, "spread": 0.17 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.733, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7042, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.987, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.8289, "spread": 0.387 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.798, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7258, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.5, "judge_b": 0.98, "mean": 0.79, "spread": 0.48 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.3, "judge_b": 0.04, "mean": 0.1333, "spread": 0.26 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.3, "judge_b": 0.06, "mean": 0.1933, "spread": 0.24 } } }, { "rank": 45, "id": "st_20261029_6585", "title": "Demoted, Still Liable", "url": "https://www.oldschoolburke.com/demoted-still-liable/", "domain": "oldschoolburke.com", "site": "Old School Burke", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T13:46:39.000Z", "jev": 0.6903, "judge_a": 0.5365, "judge_b": 0.6578, "score": 0.6282, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 33, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Hrih News essay argues that when companies promise a \"human in the loop,\" they often demote people into fail-safes who keep the blame but lose the authority to stop or shape the work.", "page": "https://rubric.hrih.si/editions/2026-10-29/45/", "card": "https://rubric.hrih.si/og/2026-10-29/45.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.575, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5317, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7833, "spread": 0.03 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.967, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7556, "spread": 0.367 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.83, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.73, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.413, "judge_a": 0.5, "judge_b": 0.63, "mean": 0.5144, "spread": 0.217 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.298, "judge_a": 0.1, "judge_b": 0.38, "mean": 0.2592, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.84, "spread": 0.22 }, "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": 0.24, "judge_a": 0.3, "judge_b": 0.38, "mean": 0.3067, "spread": 0.14 } } }, { "rank": 46, "id": "st_20261029_6843", "title": "Pragmatic guide to agentic coding – Ilya Razenshteyn", "url": "https://ilyaraz.substack.com/p/pragmatic-guide-to-agentic-coding", "domain": "ilyaraz.substack.com", "site": "ilyaraz.substack.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T16:08:37.000Z", "jev": 0.7042, "judge_a": 0.5425, "judge_b": 0.636, "score": 0.6275, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 34, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Ilya Razenshteyn's long post, \"Pragmatic guide to agentic coding,\" reports on interviews with a couple dozen engineers and researchers about how they use coding agents at work. Its central question is what keeps software engineering from becoming cheap or automated as code generation commoditizes.", "page": "https://rubric.hrih.si/editions/2026-10-29/46/", "card": "https://rubric.hrih.si/og/2026-10-29/46.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.713, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5608, "spread": 0.363 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7333, "spread": 0.14 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.96, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.83, "spread": 0.21 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.903, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7942, "spread": 0.203 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.303, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.3744, "spread": 0.117 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.665, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4983, "spread": 0.315 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7633, "spread": 0.31 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0533, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.4, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.4733, "spread": 0.42 } } }, { "rank": 47, "id": "st_20261029_7048", "title": "Scaling Hierarchical Networks", "url": "https://www.cartesia.ai/blog/from-tokens-to-bytes-part-1", "domain": "cartesia.ai", "site": "Cartesia", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T18:07:41.000Z", "jev": 0.5726, "judge_a": 0.5535, "judge_b": 0.7553, "score": 0.6271, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 35, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Cartesia's Hrih News post introduces hierarchical networks (H-Nets), an end-to-end architecture that replaces hand-built tokenization by learning to chunk raw bytes inside the model.", "page": "https://rubric.hrih.si/editions/2026-10-29/47/", "card": "https://rubric.hrih.si/og/2026-10-29/47.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.67, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.68, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.7, "judge_b": 0.92, "mean": 0.7567, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.677, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6322, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.493, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.5742, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.883, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.8044, "spread": 0.133 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.945, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.925, "spread": 0.045 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.1333, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.63, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.45, "spread": 0.33 } } }, { "rank": 48, "id": "st_20261029_6869", "title": "SnowPR: Benchmarking Coding Agents on Snowflake's 43M-Line Monorepo", "url": "https://www.snowflake.com/en/blog/engineering/Benchmarking-coding-agents/", "domain": "snowflake.com", "site": "snowflake.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T16:55:58.000Z", "jev": 0.6476, "judge_a": 0.5032, "judge_b": 0.7289, "score": 0.6265, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 36, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "SnowPR is a new coding-agent benchmark built from 65 merged pull requests in Snowflake's 43-million-line monorepo, with grading by the hidden tests that shipped alongside each change. Unlike public benchmarks drawn from small Python projects, it reflects large, multi-module Java and C++ work with Bazel builds.", "page": "https://rubric.hrih.si/editions/2026-10-29/48/", "card": "https://rubric.hrih.si/og/2026-10-29/48.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.658, "judge_a": 0.4, "judge_b": 0.76, "mean": 0.6058, "spread": 0.36 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.73, "judge_a": 0.72, "judge_b": 0.9, "mean": 0.7833, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.417, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.4989, "spread": 0.163 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.72, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.82, "judge_b": 0.9, "mean": 0.8922, "spread": 0.137 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.93, "judge_a": 0.78, "judge_b": 0.88, "mean": 0.8633, "spread": 0.15 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.85, "judge_b": 0.99, "mean": 0.9167, "spread": 0.14 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2367, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.2733, "spread": 0.16 } } }, { "rank": 49, "id": "st_20261029_6397", "title": "A framework for capturing webhooks from providers reliably and durably", "url": "https://github.com/jamescarr/ankusa", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "devops" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T11:22:50.000Z", "jev": 0.6748, "judge_a": 0.3825, "judge_b": 0.637, "score": 0.5647, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 94, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Ankusa is an open-source webhook ingestion service that runs as a container in front of your workers and publishes each incoming webhook to your existing broker (Kafka, RabbitMQ, NATS, SQS, Pub/Sub, or Redis) or POSTs it signed to an HTTP endpoint.", "page": "https://rubric.hrih.si/editions/2026-10-29/49/", "card": "https://rubric.hrih.si/og/2026-10-29/49.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.52, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4333, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.65, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.47, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4733, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.913, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7775, "spread": 0.313 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.893, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.7044, "spread": 0.393 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.8725, "spread": 0.288 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.95, "judge_b": 0.96, "mean": 0.95, "spread": 0.02 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.45, "judge_b": 0.12, "mean": 0.2167, "spread": 0.37 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2367, "spread": 0.17 } } }, { "rank": 50, "id": "st_20261029_6268", "title": "The Bitter Lesson for Humans", "url": "https://gfrm.in/posts/bitter-lesson-for-persons/", "domain": "gfrm.in", "site": "Guy Freeman", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T07:42:33.000Z", "jev": 0.7394, "judge_a": 0.355, "judge_b": 0.7723, "score": 0.6222, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 37, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author built a cryptic crossword solver with elaborate Bayesian machinery, then found that a frontier model handed the whole puzzle in one call solved 93 of 116 grids for about 28 cents each, beating the full system.", "page": "https://rubric.hrih.si/editions/2026-10-29/50/", "card": "https://rubric.hrih.si/og/2026-10-29/50.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.628, "judge_a": 0.25, "judge_b": 0.72, "mean": 0.5325, "spread": 0.47 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.74, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.933, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.7044, "spread": 0.533 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.818, "judge_a": 0.5, "judge_b": 0.83, "mean": 0.7158, "spread": 0.33 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.83, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.72, "spread": 0.28 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.783, "judge_a": 0.4, "judge_b": 0.82, "mean": 0.6675, "spread": 0.42 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.83, "spread": 0.38 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.06, "judge_a": 0.2, "judge_b": 0.03, "mean": 0.0967, "spread": 0.17 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.45, "judge_b": 0.38, "mean": 0.36, "spread": 0.2 } } }, { "rank": 51, "id": "st_20261029_6737", "title": "How to Extract Data from InnoDB, MyISAM, and MyRocks Files?", "url": "https://www.percona.com/blog/how-to-extract-data-from-innodb-myisam-and-myrocks-files-without-a-running-server/", "domain": "percona.com", "site": "Percona", "categories": [ "programming", "devops", "data" ], "category_names": [ "Programming & dev tools", "Infrastructure & DevOps", "Data" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T15:23:29.000Z", "jev": 0.5143, "judge_a": 0.5033, "judge_b": 0.662, "score": 0.5598, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 101, "topic_penalty": 0.02, "repeat_penalty": 0, "summary": "Hrih News covers innodump, a new open-source tool that extracts data offline from raw InnoDB, MyISAM, and MyRocks files without a running server, useful for corruption recovery, backup checks, and staging provisioning.", "page": "https://rubric.hrih.si/editions/2026-10-29/51/", "card": "https://rubric.hrih.si/og/2026-10-29/51.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.478, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4525, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.49, "judge_a": 0.68, "judge_b": 0.78, "mean": 0.65, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.44, "judge_a": 0.35, "judge_b": 0.42, "mean": 0.4033, "spread": 0.09 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.613, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7108, "spread": 0.207 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.78, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.72, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.838, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8192, "spread": 0.037 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9333, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.17, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1633, "spread": 0.13 } } }, { "rank": 52, "id": "st_20261029_7079", "title": "Agent Scrum: Running a team of AI coding agents in two-hour sprints", "url": "https://daveremy.github.io/agent-scrum/", "domain": "daveremy.github.io", "site": "daveremy.github.io", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T18:47:09.000Z", "jev": 0.6423, "judge_a": 0.477, "judge_b": 0.7208, "score": 0.6133, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 43, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Agent Scrum is a working paper by Dave Remy describing how a fleet of AI coding agents ran 324 two-hour sprints over 38 days on a real product backlog, adapting Scrum's sprints, points, velocity, Definition of Done and retrospectives to agent teams.", "page": "https://rubric.hrih.si/editions/2026-10-29/52/", "card": "https://rubric.hrih.si/og/2026-10-29/52.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.54, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.54, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.66, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7067, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.61, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5767, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.728, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7025, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.7267, "spread": 0.46 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.55, "judge_b": 0.79, "mean": 0.7742, "spread": 0.433 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.88, "spread": 0.05 }, "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": 0.26, "judge_a": 0.35, "judge_b": 0.3, "mean": 0.3033, "spread": 0.09 } } }, { "rank": 53, "id": "st_20261029_6829", "title": "Nim Version 2.4.0 Released", "url": "https://nim-lang.org/blog/2026/10/28/nim-240.html", "domain": "nim-lang.org", "site": "Nim Programming Language", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T16:12:59.000Z", "jev": 0.5739, "judge_a": 0.618, "judge_b": 0.6464, "score": 0.6127, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 44, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Nim 2.4.0, a new stable release of the Nim programming language, was published on 28 October 2026 and ships several major features: a small-string optimization (--strings:sso) with a new low-level strings API, parallel incremental builds (--ic:on), YRC, a thread-safe memory manager that handles cycles spanning threads, a more concise sum…", "page": "https://rubric.hrih.si/editions/2026-10-29/53/", "card": "https://rubric.hrih.si/og/2026-10-29/53.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.515, "judge_a": 0.55, "judge_b": 0.62, "mean": 0.5617, "spread": 0.105 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.51, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.6433, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.377, "judge_a": 0.6, "judge_b": 0.34, "mean": 0.4389, "spread": 0.26 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.775, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.685, "spread": 0.175 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.78, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.983, "judge_a": 0.8, "judge_b": 0.82, "mean": 0.8675, "spread": 0.183 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9267, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.0567, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1367, "spread": 0.1 } } }, { "rank": 54, "id": "st_20261029_6356", "title": "Does the code compile? Five local models on a DGX Spark, five runs each", "url": "https://borisivanov.com/notes/local-llm-compile-bench/", "domain": "borisivanov.com", "site": "borisivanov.com", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T10:23:42.000Z", "jev": 0.6482, "judge_a": 0.4935, "judge_b": 0.6946, "score": 0.612, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 45, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A benchmark of five local models on a DGX Spark, five runs each, judged by whether the code compiles and passes hidden tests rather than by tokens per second.", "page": "https://rubric.hrih.si/editions/2026-10-29/54/", "card": "https://rubric.hrih.si/og/2026-10-29/54.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.505, "judge_a": 0.2, "judge_b": 0.57, "mean": 0.425, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.71, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.59, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.49, "spread": 0.19 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.793, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7575, "spread": 0.093 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.927, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8156, "spread": 0.227 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.905, "judge_a": 0.7, "judge_b": 0.69, "mean": 0.765, "spread": 0.215 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9133, "spread": 0.08 }, "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": 0.24, "judge_a": 0.2, "judge_b": 0.16, "mean": 0.2, "spread": 0.08 } } }, { "rank": 55, "id": "st_20261029_7049", "title": "Writing GPU kernels in Rust: how I made speakrs 60× faster than pyannote", "url": "https://avencera.com/blog/writing-gpu-kernels-in-rust-speakrs-60x-faster-than-pyannote", "domain": "avencera.com", "site": "Avencera", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T18:07:38.000Z", "jev": 0.6442, "judge_a": 0.431, "judge_b": 0.7594, "score": 0.6115, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 46, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "speakrs, an open-source Rust speaker diarization library, replaced its ONNX Runtime dependency with its own GPU kernels written in Rust via cuda-oxide, and can now build with only the NVIDIA driver.", "page": "https://rubric.hrih.si/editions/2026-10-29/55/", "card": "https://rubric.hrih.si/og/2026-10-29/55.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.553, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5408, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.7933, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.473, "judge_a": 0.3, "judge_b": 0.43, "mean": 0.4011, "spread": 0.173 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.818, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7458, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.7, "judge_b": 0.91, "mean": 0.8567, "spread": 0.26 }, "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": 0.89, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9233, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.4, "judge_b": 0.08, "mean": 0.1833, "spread": 0.33 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.49, "judge_a": 0.5, "judge_b": 0.18, "mean": 0.39, "spread": 0.32 } } }, { "rank": 56, "id": "st_20261029_6982", "title": "Async Commands Fallback Layer", "url": "https://devblogs.microsoft.com/directx/async-commands-fallback-layer/", "domain": "devblogs.microsoft.com", "site": "devblogs.microsoft.com", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T17:49:54.000Z", "jev": 0.5589, "judge_a": 0.5385, "judge_b": 0.7357, "score": 0.611, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 47, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Microsoft's Agility SDK 1.620.1 adds an opt-in fallback layer for Batched Async Commands, letting developers use the new copy, fill, clear, and resolve APIs on GPUs without native support.", "page": "https://rubric.hrih.si/editions/2026-10-29/56/", "card": "https://rubric.hrih.si/og/2026-10-29/56.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.51, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5467, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.44, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6067, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.403, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.4078, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.725, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7283, "spread": 0.26 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.87, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.8733, "spread": 0.15 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.9325, "spread": 0.138 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.95, "judge_b": 1, "mean": 0.96, "spread": 0.07 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1067, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.11, "judge_a": 0.1, "judge_b": 0.04, "mean": 0.0833, "spread": 0.07 } } }, { "rank": 57, "id": "st_20261029_6372", "title": "eMudhra TLS leak: Apple leaves macOS command-line issue unresolved", "url": "https://www.heise.de/en/news/eMudhra-TLS-leak-Apple-leaves-macOS-command-line-issue-unresolved-11502949.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T10:05:00.000Z", "jev": 0.6497, "judge_a": 0.5705, "judge_b": 0.6891, "score": 0.6364, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 27, "topic_penalty": 0.08, "repeat_penalty": 0.0261, "summary": "Apple has not yet fixed a certificate problem in macOS command-line tools that stems from a compromise at certificate authority eMudhra, which could let attackers impersonate servers in some network positions.", "page": "https://rubric.hrih.si/editions/2026-10-29/57/", "card": "https://rubric.hrih.si/og/2026-10-29/57.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.635, "judge_a": 0.5, "judge_b": 0.65, "mean": 0.595, "spread": 0.15 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.69, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.57, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.54, "spread": 0.07 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.863, "judge_a": 0.8, "judge_b": 0.85, "mean": 0.8375, "spread": 0.063 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.58, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.6667, "spread": 0.14 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.94, "judge_a": 0.7, "judge_b": 0.58, "mean": 0.74, "spread": 0.36 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.77, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.5633, "spread": 0.47 }, "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": 0.37, "judge_a": 0.2, "judge_b": 0.28, "mean": 0.2833, "spread": 0.17 } } }, { "rank": 58, "id": "st_20261029_7229", "title": "Oracle celebrates end of Cybersecurity Awareness Month with quadruple trouble", "url": "https://www.theregister.com/security/2026/10/28/oracle-celebrates-end-of-cybersecurity-awareness-month-with-quadruple-trouble/5306121", "domain": "theregister.com", "site": "The Register", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T19:56:56.000Z", "jev": 0.5163, "judge_a": 0.5927, "judge_b": 0.7167, "score": 0.6085, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 48, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Oracle's E-Business Suite faces active exploitation attempts against CVE-2026-101767, an unauthenticated SQL injection flaw that can chain to remote code execution, even though Oracle patched it in October.", "page": "https://rubric.hrih.si/editions/2026-10-29/58/", "card": "https://rubric.hrih.si/og/2026-10-29/58.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.623, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6308, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.73, "spread": 0.15 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.45, "judge_a": 0.6, "judge_b": 0.58, "mean": 0.5433, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.485, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.6617, "spread": 0.295 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.433, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.5778, "spread": 0.267 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.688, "judge_a": 0.45, "judge_b": 0.55, "mean": 0.5625, "spread": 0.238 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.35, "judge_b": 0.94, "mean": 0.7233, "spread": 0.59 }, "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": 0.54, "judge_a": 0.2, "judge_b": 0.2, "mean": 0.3133, "spread": 0.34 } } }, { "rank": 59, "id": "st_20261029_6701", "title": "A safety checklist in the prompt didn't measurably help frontier models", "url": "https://theagentforge.io/lab/4/results", "domain": "theagentforge.io", "site": "The Agent Forge", "categories": [ "ai", "security" ], "category_names": [ "AI & machine learning", "Security" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T14:49:55.000Z", "jev": 0.644, "judge_a": 0.447, "judge_b": 0.729, "score": 0.6066, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 49, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A pre-registered test found that adding an eight-item safety checklist to frontier AI agents' instructions did not measurably reduce how often they fell for planted traps: 24.8% with the card versus 29.5% without, not statistically significant. It also did not harm legitimate work, which stayed within pre-set margins.", "page": "https://rubric.hrih.si/editions/2026-10-29/59/", "card": "https://rubric.hrih.si/og/2026-10-29/59.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.593, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5208, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.55, "judge_b": 0.83, "mean": 0.6667, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.73, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.6033, "spread": 0.33 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.598, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.6225, "spread": 0.073 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.8, "judge_b": 0.92, "mean": 0.9044, "spread": 0.193 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.95, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.79, "spread": 0.35 }, "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": 0.3, "judge_b": 0.01, "mean": 0.12, "spread": 0.29 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.16, "judge_a": 0.2, "judge_b": 0.05, "mean": 0.1367, "spread": 0.15 } } }, { "rank": 60, "id": "st_20261029_6128", "title": "Confident Science Papers Sway AI Judges – Regardless of Merit", "url": "https://www.unite.ai/confident-science-papers-sway-ai-judges-regardless-of-merit/", "domain": "unite.ai", "site": "Unite.AI", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T05:21:29.000Z", "jev": 0.5553, "judge_a": 0.551, "judge_b": 0.7088, "score": 0.605, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 51, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A new study from Germany finds that six AI reviewers, including GPT-5.5 and Claude Sonnet 4.6, score scientific papers higher when they are written in confident language, even when the research is identical to a more cautious version.", "page": "https://rubric.hrih.si/editions/2026-10-29/60/", "card": "https://rubric.hrih.si/og/2026-10-29/60.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5767, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.7, "judge_b": 0.87, "mean": 0.72, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.753, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6744, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.51, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5933, "spread": 0.21 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.73, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.71, "spread": 0.03 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.708, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.6125, "spread": 0.258 }, "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": 0.1, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0567, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.46, "judge_a": 0.2, "judge_b": 0.38, "mean": 0.3467, "spread": 0.26 } } }, { "rank": 61, "id": "st_20261029_6249", "title": "A Second Look at Apple's Secure Kernel", "url": "https://www.antid0te.com/blog/secure-kernel-first-look-erratum.html", "domain": "antid0te.com", "site": "antid0te.com", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T07:08:42.000Z", "jev": 0.5319, "judge_a": 0.544, "judge_b": 0.7381, "score": 0.6047, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 52, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This correction retracts a February article claiming that an ordinary Exclave could make Apple's Secure Kernel dispatch through a fake capability in writable memory.", "page": "https://rubric.hrih.si/editions/2026-10-29/61/", "card": "https://rubric.hrih.si/og/2026-10-29/61.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.49, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.5867, "spread": 0.23 }, "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": 0.33, "judge_a": 0.45, "judge_b": 0.35, "mean": 0.3767, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.45, "judge_a": 0.3, "judge_b": 0.7, "mean": 0.4833, "spread": 0.4 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9244, "spread": 0.123 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9625, "spread": 0.098 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.85, "judge_b": 0.98, "mean": 0.9, "spread": 0.13 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.04, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1033, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.1267, "spread": 0.16 } } }, { "rank": 62, "id": "st_20261029_6190", "title": "Dueling proofs from OpenAI and Anthropic astound scientists", "url": "https://www.scientificamerican.com/article/dueling-proofs-from-openai-and-anthropic-astound-scientists/", "domain": "scientificamerican.com", "site": "Scientific American", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T01:47:28.000Z", "jev": 0.4954, "judge_a": 0.588, "judge_b": 0.7269, "score": 0.6034, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 53, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "OpenAI released three AI-generated results touching on Kakeya sets in all dimensions, the Yang-Mills existence problem, and the exponential time hypothesis, the first two of which are Millennium-adjacent or Millennium-related claims.", "page": "https://rubric.hrih.si/editions/2026-10-29/62/", "card": "https://rubric.hrih.si/og/2026-10-29/62.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.845, "judge_a": 0.8, "judge_b": 0.98, "mean": 0.875, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.7867, "spread": 0.41 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.693, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7244, "spread": 0.28 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.343, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4242, "spread": 0.237 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.287, "judge_a": 0.4, "judge_b": 0.32, "mean": 0.3356, "spread": 0.113 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.535, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5517, "spread": 0.12 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.9, "judge_b": 0.93, "mean": 0.9033, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.08, "spread": 0.11 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.66, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6633, "spread": 0.23 } } }, { "rank": 63, "id": "st_20261029_7130", "title": "Evaluating Kubernetes Pod Checkpoint/Restore on AWS (CRIU, PLEG, eBPF)", "url": "https://github.com/musaxasmammedov7/final-research", "domain": "github.com", "site": "GitHub", "categories": [ "devops" ], "category_names": [ "Infrastructure & DevOps" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-28T19:58:28.000Z", "jev": 0.5614, "judge_a": 0.4205, "judge_b": 0.7053, "score": 0.5624, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 96, "topic_penalty": 0.04, "repeat_penalty": 0, "summary": "This repository holds the testbed, operator, benchmarks, and LaTeX source for a study of Kubernetes pod checkpoint/restore migration using CRIU, Kubelet's checkpoint API, Buildah, CRI-O, and Cilium eBPF on a three-node AWS cluster.", "page": "https://rubric.hrih.si/editions/2026-10-29/63/", "card": "https://rubric.hrih.si/og/2026-10-29/63.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4758, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.45, "judge_b": 0.84, "mean": 0.6033, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.513, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4711, "spread": 0.2 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.66, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6467, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.99, "judge_a": 0.7, "judge_b": 0.76, "mean": 0.8167, "spread": 0.29 }, "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": 0.96, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.94, "spread": 0.06 }, "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": 0.36, "judge_a": 0.25, "judge_b": 0.28, "mean": 0.2967, "spread": 0.11 } } }, { "rank": 64, "id": "st_20261029_5914", "title": "Tailscale SSH: Findings and fixes", "url": "https://tailscale.com/blog/tailscale-ssh-findings-fixes", "domain": "tailscale.com", "site": "Tailscale Blog", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T01:15:00.000Z", "jev": 0.5985, "judge_a": 0.504, "judge_b": 0.703, "score": 0.6018, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 54, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Tailscale disclosed fixes for vulnerabilities in Tailscale SSH and related features, reported in a researcher's \"Tailworm\" report, and shipped them in v1.104.4. The flaws let an already-authorized peer exceed its assigned privileges after connecting, including via SSH forwarding, but could not be used by unauthenticated attackers.", "page": "https://rubric.hrih.si/editions/2026-10-29/64/", "card": "https://rubric.hrih.si/og/2026-10-29/64.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.598, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5592, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.7667, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.253, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.4111, "spread": 0.327 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.718, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7325, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.637, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6856, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.62, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5633, "spread": 0.17 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.74, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.67, "spread": 0.19 }, "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": 0.16, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.16, "spread": 0.08 } } }, { "rank": 65, "id": "st_20261029_6553", "title": "JSPI Speedup", "url": "https://www.pcbjam.com/blog/moving-from-asyncify-to-jspi/", "domain": "pcbjam.com", "site": "PCBJam", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T13:13:25.000Z", "jev": 0.5377, "judge_a": 0.585, "judge_b": 0.6827, "score": 0.6017, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 55, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A blog post from pcbjam reports that switching the KiCad web port from Asyncify to JavaScript Promise Integration (JSPI), a native browser API for suspending and resuming WebAssembly, roughly halved bundle size, load times, and board-pan latency on test boards.", "page": "https://rubric.hrih.si/editions/2026-10-29/65/", "card": "https://rubric.hrih.si/og/2026-10-29/65.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.55, "judge_a": 0.45, "judge_b": 0.58, "mean": 0.5267, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.69, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.5, "judge_a": 0.5, "judge_b": 0.52, "mean": 0.5067, "spread": 0.02 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.743, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7408, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.767, "judge_a": 0.65, "judge_b": 0.78, "mean": 0.7322, "spread": 0.13 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.795, "judge_a": 0.8, "judge_b": 0.68, "mean": 0.7583, "spread": 0.12 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.7, "judge_b": 0.96, "mean": 0.8567, "spread": 0.26 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.06, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.64, "judge_a": 0.2, "judge_b": 0.22, "mean": 0.3533, "spread": 0.44 } } }, { "rank": 66, "id": "st_20261029_7207", "title": "Octoverse: Commits, pushes, reach new highs in 2026", "url": "https://github.blog/news-insights/octoverse/octoverse-commits-pushes-reach-new-highs-in-2026-as-developers-build-with-agents/", "domain": "github.blog", "site": "The GitHub Blog", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T20:55:08.000Z", "jev": 0.6802, "judge_a": 0.5, "judge_b": 0.625, "score": 0.6017, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 56, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "GitHub's Octoverse 2026 report says developers pushed code 17.9 billion times in the past year, more than 2024 and 2025 combined, with commits across public and private repos reaching 34.3 billion.", "page": "https://rubric.hrih.si/editions/2026-10-29/66/", "card": "https://rubric.hrih.si/og/2026-10-29/66.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.733, "judge_a": 0.55, "judge_b": 0.75, "mean": 0.6775, "spread": 0.2 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.71, "judge_a": 0.8, "judge_b": 0.8, "mean": 0.77, "spread": 0.09 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.94, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.88, "spread": 0.14 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.638, "judge_a": 0.55, "judge_b": 0.65, "mean": 0.6125, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.8, "mean": 0.7767, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.61, "judge_a": 0.3, "judge_b": 0.65, "mean": 0.52, "spread": 0.35 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9133, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.19, "judge_a": 0.6, "judge_b": 0.55, "mean": 0.4467, "spread": 0.41 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.28, "judge_a": 0.35, "judge_b": 0.35, "mean": 0.3267, "spread": 0.07 } } }, { "rank": 67, "id": "st_20261029_6879", "title": "Prioritize Dependabot alerts with reachability analysis", "url": "https://github.blog/changelog/2026-10-28-prioritize-dependabot-alerts-with-reachability-analysis/", "domain": "github.blog", "site": "The GitHub Blog", "categories": [ "programming", "security", "devops" ], "category_names": [ "Programming & dev tools", "Security", "Infrastructure & DevOps" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T16:40:58.000Z", "jev": 0.6696, "judge_a": 0.454, "judge_b": 0.6728, "score": 0.5987, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 57, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "GitHub has launched a public preview of reachability analysis for Dependabot alerts, currently covering JavaScript and TypeScript repositories on Enterprise Cloud. It checks whether your code has a call path to the vulnerable functions in a flagged dependency, labeling each alert as reachable, potentially reachable, unreachable, or unavailable.", "page": "https://rubric.hrih.si/editions/2026-10-29/67/", "card": "https://rubric.hrih.si/og/2026-10-29/67.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.628, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5325, "spread": 0.277 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.55, "judge_b": 0.76, "mean": 0.6467, "spread": 0.21 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.537, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5289, "spread": 0.05 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.93, "judge_a": 0.75, "judge_b": 0.8, "mean": 0.8267, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.667, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.6622, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.75, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.61, "spread": 0.25 }, "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": 0.13, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.2367, "spread": 0.27 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.1133, "spread": 0.16 } } }, { "rank": 68, "id": "st_20261029_6309", "title": "Attackers target Brotli, Adobe Commerce and Magento vulnerabilities", "url": "https://www.heise.de/en/news/Attackers-target-Brotli-Adobe-Commerce-and-Magento-vulnerabilities-11502688.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T08:26:00.000Z", "jev": 0.7362, "judge_a": 0.6374, "judge_b": 0.7213, "score": 0.5982, "contested": true, "flag_penalty": 0.1, "freshness_penalty": 0, "score_rank": 58, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Attackers are actively exploiting two flaws: \"Broccoli2Shell,\" a Brotli decoder vulnerability (CVE-2026-111497, affecting versions 1.0.3 through 1.2.0) that lets unauthenticated users inject malicious code, and an Adobe Commerce/Magento unrestricted file upload bug (CVE-2026-48356). CISA has added both to its Known Exploited Vulnerabilities catalog.", "page": "https://rubric.hrih.si/editions/2026-10-29/68/", "card": "https://rubric.hrih.si/og/2026-10-29/68.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.708, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6292, "spread": 0.208 }, "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": 0.723, "judge_a": 0.67, "judge_b": 0.62, "mean": 0.6711, "spread": 0.103 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.908, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.8592, "spread": 0.088 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.593, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6378, "spread": 0.127 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.683, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.5208, "spread": 0.283 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.77, "judge_a": 0.15, "judge_b": 0.78, "mean": 0.5667, "spread": 0.63 }, "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": 0.21, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.16, "spread": 0.09 } } }, { "rank": 69, "id": "st_20261029_6373", "title": "Veeam: Backup servers can be tampered with, MFA disabled", "url": "https://www.heise.de/en/news/Veeam-Backup-servers-can-be-tampered-with-MFA-disabled-11502838.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "devops", "it" ], "category_names": [ "Security", "Infrastructure & DevOps", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T09:55:00.000Z", "jev": 0.5522, "judge_a": 0.5215, "judge_b": 0.7159, "score": 0.5965, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 59, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Security researchers disclosed several vulnerabilities in Veeam Backup & Replication and related products, including a critical flaw (CVE-2026-108142) that lets unauthenticated attackers reach the Continuous Data Protection service on backup servers. Veeam Agent for Linux and Veeam ONE are also affected, with patches available.", "page": "https://rubric.hrih.si/editions/2026-10-29/69/", "card": "https://rubric.hrih.si/og/2026-10-29/69.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.618, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5658, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.273, "judge_a": 0.4, "judge_b": 0.42, "mean": 0.3644, "spread": 0.147 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.608, "judge_a": 0.7, "judge_b": 0.8, "mean": 0.7025, "spread": 0.193 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.55, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.6567, "spread": 0.27 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.72, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.5667, "spread": 0.32 }, "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": 0.05, "judge_b": 0.03, "mean": 0.0567, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.31, "judge_a": 0.2, "judge_b": 0.04, "mean": 0.1833, "spread": 0.27 } } }, { "rank": 70, "id": "st_20261029_6353", "title": "Getting good at AI takes time and practice", "url": "https://seangoedecke.com/getting-good-at-ai-takes-time-and-practice/", "domain": "seangoedecke.com", "site": "Sean Goedecke", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T00:00:00.000Z", "jev": 0.6982, "judge_a": 0.5025, "judge_b": 0.5888, "score": 0.5964, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 60, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This essay argues against the view that getting good at AI requires no learning, claiming instead that durable skills take time and practice to build. It covers supplying the right context to agents, knowing what models can and cannot do, recognizing the characteristic mistakes of AI-generated code, and skimming verbose…", "page": "https://rubric.hrih.si/editions/2026-10-29/70/", "card": "https://rubric.hrih.si/og/2026-10-29/70.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.648, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.4925, "spread": 0.298 }, "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": 0.927, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.7622, "spread": 0.327 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.863, "judge_a": 0.7, "judge_b": 0.7, "mean": 0.7542, "spread": 0.163 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.2, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.31, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.633, "judge_a": 0.4, "judge_b": 0.43, "mean": 0.4875, "spread": 0.232 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.82, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7133, "spread": 0.22 }, "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": 0.16, "judge_a": 0.25, "judge_b": 0.48, "mean": 0.2967, "spread": 0.32 } } }, { "rank": 71, "id": "st_20261029_6988", "title": "Particle-1", "url": "https://particle.ai/blog/introducing-particle-1", "domain": "particle.ai", "site": "particle.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T17:45:39.000Z", "jev": 0.6358, "judge_a": 0.3905, "judge_b": 0.7598, "score": 0.5953, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 61, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Particle has released Particle-1, a version of DeepSeek V4.1 Flash post-trained with reinforcement learning to cut token use, averaging about 45% fewer tokens per task.", "page": "https://rubric.hrih.si/editions/2026-10-29/71/", "card": "https://rubric.hrih.si/og/2026-10-29/71.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.595, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.555, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.7, "spread": 0.35 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.64, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.6033, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.823, "judge_a": 0.6, "judge_b": 0.84, "mean": 0.7542, "spread": 0.24 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.89, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.7367, "spread": 0.39 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.873, "judge_a": 0.65, "judge_b": 0.86, "mean": 0.7942, "spread": 0.223 }, "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": 0.14, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2567, "spread": 0.31 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.43, "judge_a": 0.4, "judge_b": 0.22, "mean": 0.35, "spread": 0.21 } } }, { "rank": 72, "id": "st_20261029_7026", "title": "Agent Memory Is in the Wrong Place", "url": "https://www.omnara.com/blog/agent-memory-is-in-the-wrong-place", "domain": "omnara.com", "site": "Omnara", "categories": [ "programming", "ai", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T18:29:49.000Z", "jev": 0.6562, "judge_a": 0.4235, "judge_b": 0.7062, "score": 0.5952, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 62, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Omnara describes putting agent memory in its control plane as a filesystem that any worker can mount, rather than on the agent's sandbox, so memory survives machines going offline and multiple agents can share stores.", "page": "https://rubric.hrih.si/editions/2026-10-29/72/", "card": "https://rubric.hrih.si/og/2026-10-29/72.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.613, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.5642, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.75, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7767, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.51, "judge_a": 0.5, "judge_b": 0.62, "mean": 0.5433, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.79, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7233, "spread": 0.19 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.577, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6156, "spread": 0.37 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.94, "judge_a": 0.8, "judge_b": 0.91, "mean": 0.8833, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.88, "judge_a": 0.8, "judge_b": 0.97, "mean": 0.8833, "spread": 0.17 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.55, "judge_b": 0.28, "mean": 0.3167, "spread": 0.43 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.21, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.28, "spread": 0.27 } } }, { "rank": 73, "id": "st_20261029_6608", "title": "Gemini API CLI", "url": "https://ai.google.dev/gemini-api/docs/cli", "domain": "ai.google.dev", "site": "Google AI for Developers", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T13:34:17.000Z", "jev": 0.6139, "judge_a": 0.4725, "judge_b": 0.6976, "score": 0.5946, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 63, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Google's Gemini API CLI (gemini-api) is a stateless, zero-dependency command-line client designed for AI agents with shell access and for scripts or CI pipelines. It lets agents generate text, images, speech, music, and video, analyze files, and launch background Gemini managed agents through standard shell commands on the Interactions API…", "page": "https://rubric.hrih.si/editions/2026-10-29/73/", "card": "https://rubric.hrih.si/og/2026-10-29/73.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.53, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4867, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.53, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.65, "spread": 0.29 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.473, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.5911, "spread": 0.227 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.908, "judge_a": 0.75, "judge_b": 0.88, "mean": 0.8458, "spread": 0.157 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.71, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.66, "spread": 0.17 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.93, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7433, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.93, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.14, "judge_a": 0.45, "judge_b": 0.18, "mean": 0.2567, "spread": 0.31 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.13, "judge_a": 0.25, "judge_b": 0.22, "mean": 0.2, "spread": 0.12 } } }, { "rank": 74, "id": "st_20261029_6612", "title": "Microsoft Authenticator flaw let malicious apps steal access tokens", "url": "https://www.heise.de/en/news/Microsoft-Authenticator-flaw-let-malicious-apps-steal-access-tokens-11503446.html", "domain": "heise.de", "site": "heise online English", "categories": [ "security", "it" ], "category_names": [ "Security", "IT & networking" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T13:50:00.000Z", "jev": 0.5797, "judge_a": 0.6023, "judge_b": 0.7485, "score": 0.6434, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 23, "topic_penalty": 0.08, "repeat_penalty": 0.05, "summary": "Researchers at Enclave found that Microsoft Authenticator's check on calling apps only compared a self-declared redirect URI against the package signature, letting even permission-free malicious apps impersonate Teams and obtain plaintext access tokens, which could expose mailboxes, files, and calendars.", "page": "https://rubric.hrih.si/editions/2026-10-29/74/", "card": "https://rubric.hrih.si/og/2026-10-29/74.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.65, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.66, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.7, "judge_b": 0.93, "mean": 0.7667, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.377, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.5589, "spread": 0.323 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.678, "judge_a": 0.65, "judge_b": 0.8, "mean": 0.7092, "spread": 0.15 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.53, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.6833, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.763, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6942, "spread": 0.162 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.65, "judge_a": 0.6, "judge_b": 0.02, "mean": 0.4233, "spread": 0.63 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.03, "judge_b": 0.03, "mean": 0.0367, "spread": 0.02 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.33, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.2367, "spread": 0.15 } } }, { "rank": 75, "id": "st_20261029_6589", "title": "How you could have invented muon from memory", "url": "https://lucastononro.github.io/how-you-could-have-invented-muon-from-memory/", "domain": "lucastononro.github.io", "site": "Tonon Journey", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-28T13:48:39.000Z", "jev": 0.5422, "judge_a": 0.5015, "judge_b": 0.7341, "score": 0.5925, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 64, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "This Hrih News entry is a long, interactive research essay titled \"How you could have invented muon from memory,\" by Lucas Tononro, that walks readers through deriving the Muon optimizer step by step from basic gradient descent.", "page": "https://rubric.hrih.si/editions/2026-10-29/75/", "card": "https://rubric.hrih.si/og/2026-10-29/75.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.538, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5225, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.57, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.6733, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.56, "judge_a": 0.5, "judge_b": 0.55, "mean": 0.5367, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.513, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.5642, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.633, "judge_a": 0.5, "judge_b": 0.86, "mean": 0.6644, "spread": 0.36 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.99, "judge_a": 0.85, "judge_b": 0.97, "mean": 0.9367, "spread": 0.14 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.92, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.05, "judge_b": 0.01, "mean": 0.0367, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.34, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.19, "spread": 0.26 } } }, { "rank": 76, "id": "st_20261029_7103", "title": "The employee without the employment", "url": "https://hrih.si/articles/the-employee-without-the-employment/", "domain": "hrih.si", "site": "hrih.si", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T19:24:34.343Z", "jev": 0.6234, "judge_a": 0.4535, "judge_b": 0.6989, "score": 0.5918, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 65, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "An essay by Hatim Rih describes how he used Claude to submit his site to several blog directories: Claude did the research, wrote a tested code change, and filled in forms, but waited for his approval before acting under his name or passing a captcha.", "page": "https://rubric.hrih.si/editions/2026-10-29/76/", "card": "https://rubric.hrih.si/og/2026-10-29/76.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.553, "judge_a": 0.35, "judge_b": 0.66, "mean": 0.5208, "spread": 0.31 }, "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": 0.847, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.6989, "spread": 0.297 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.618, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5792, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.637, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6722, "spread": 0.18 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.495, "judge_a": 0.2, "judge_b": 0.42, "mean": 0.3717, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.8, "judge_b": 0.95, "mean": 0.88, "spread": 0.15 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0833, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.3, "judge_b": 0.32, "mean": 0.3067, "spread": 0.02 } } }, { "rank": 77, "id": "st_20261029_6129", "title": "Artificial Analysis Intelligence Index v5.0", "url": "https://artificialanalysis.ai/methodology/intelligence-benchmarking", "domain": "artificialanalysis.ai", "site": "artificialanalysis.ai", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T05:29:11.000Z", "jev": 0.5962, "judge_a": 0.525, "judge_b": 0.6536, "score": 0.5916, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 66, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The Artificial Analysis Intelligence Index v5.0 is a composite benchmark that scores language models through a weighted average of nine evaluations across four equally weighted categories: knowledge work, coding, science, and general knowledge. It is primarily text-based and English-language, with visual and multilingual performance reported separately.", "page": "https://rubric.hrih.si/editions/2026-10-29/77/", "card": "https://rubric.hrih.si/og/2026-10-29/77.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.66, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6267, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.68, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7267, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.585, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6017, "spread": 0.035 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.907, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8356, "spread": 0.207 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.82, "spread": 0.38 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.95, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9433, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.16, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.67, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5633, "spread": 0.42 } } }, { "rank": 78, "id": "st_20261029_7112", "title": "Anthropic's Efficiency: More Bang for the Buck, or More Bucks for the Bang?", "url": "https://llukyanov.substack.com/p/on-anthropics-compute-efficiency", "domain": "llukyanov.substack.com", "site": "llukyanov.substack.com", "categories": [ "ai", "startups" ], "category_names": [ "AI & machine learning", "Startups & founders" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T19:28:28.000Z", "jev": 0.5087, "judge_a": 0.5175, "judge_b": 0.7464, "score": 0.5909, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 67, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Anthropic's S-1 shows revenue per dollar of compute rising from 0.40x to 2.08x between Q1 2025 and Q2 2026, but the author argues this does not prove a matching gain in technical efficiency.", "page": "https://rubric.hrih.si/editions/2026-10-29/78/", "card": "https://rubric.hrih.si/og/2026-10-29/78.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.6, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.61, "spread": 0.33 }, "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": 0.49, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6033, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.38, "judge_a": 0.4, "judge_b": 0.68, "mean": 0.4867, "spread": 0.3 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.787, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6689, "spread": 0.187 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.453, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4342, "spread": 0.25 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.55, "judge_b": 0.98, "mean": 0.8167, "spread": 0.43 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0667, "spread": 0.09 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.24, "judge_a": 0.25, "judge_b": 0.18, "mean": 0.2233, "spread": 0.07 } } }, { "rank": 79, "id": "st_20261029_7263", "title": "Zero Breach Toolkit, open source tool capable of detecting Israeli spyware", "url": "https://github.com/interplains/zero-breach", "domain": "github.com", "site": "GitHub", "categories": [ "security" ], "category_names": [ "Security" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T21:41:23.000Z", "jev": 0.5396, "judge_a": 0.484, "judge_b": 0.7493, "score": 0.5909, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 68, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Zero Breach v0.1.8 is an open-source, read-only scanner that checks a computer's browser records, file hashes, Windows persistence entries, and DNS cache for published indicators tied to the AdFection/Candiru spyware campaign, which used real-time ad bidding to deliver zero-click infections.", "page": "https://rubric.hrih.si/editions/2026-10-29/79/", "card": "https://rubric.hrih.si/og/2026-10-29/79.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.488, "judge_a": 0.5, "judge_b": 0.66, "mean": 0.5492, "spread": 0.173 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.61, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7033, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.453, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4444, "spread": 0.08 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.55, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6433, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.8456, "spread": 0.257 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.993, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.8042, "spread": 0.393 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.6, "judge_b": 0.96, "mean": 0.8233, "spread": 0.36 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.05, "judge_a": 0.2, "judge_b": 0.03, "mean": 0.0933, "spread": 0.17 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.45, "judge_a": 0.3, "judge_b": 0.04, "mean": 0.2633, "spread": 0.41 } } }, { "rank": 80, "id": "st_20261029_6939", "title": "Homi, open decision model for audio, near Flash on MMSU and 50x cheaper", "url": "https://github.com/egma-ai/homi/", "domain": "github.com", "site": "GitHub", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-28T17:09:08.000Z", "jev": 0.602, "judge_a": 0.3925, "judge_b": 0.7738, "score": 0.5894, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 69, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Homi is an open-weights audio decision model built on frozen Qwen3-Omni-30B with a 17M-parameter LoRA and a decision head that outputs calibrated probabilities over options rather than generated text.", "page": "https://rubric.hrih.si/editions/2026-10-29/80/", "card": "https://rubric.hrih.si/og/2026-10-29/80.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.543, "judge_a": 0.35, "judge_b": 0.72, "mean": 0.5375, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.65, "judge_a": 0.5, "judge_b": 0.9, "mean": 0.6833, "spread": 0.4 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5, "spread": 0.22 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.773, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.6975, "spread": 0.32 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.993, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8644, "spread": 0.293 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.7, "judge_b": 0.94, "mean": 0.8783, "spread": 0.295 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.95, "judge_b": 0.98, "mean": 0.9533, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1533, "spread": 0.22 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.55, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.3767, "spread": 0.37 } } }, { "rank": 81, "id": "st_20261029_7291", "title": "Local Code – Run DeepSeek 4.1 Flash 1017 Locally on Your Mac", "url": "https://github.com/drawthingsai/local-code-community", "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": "2026-10-28T22:05:50.000Z", "jev": 0.6528, "judge_a": 0.3895, "judge_b": 0.7238, "score": 0.5887, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 70, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Local Code is a Mac, iPad and iPhone app that bundles an on-device LLM engine for Apple Silicon, a per-model coding harness, and a sandboxed agent runtime with Python, Node.js and common CLI tools.", "page": "https://rubric.hrih.si/editions/2026-10-29/81/", "card": "https://rubric.hrih.si/og/2026-10-29/81.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.575, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5317, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.86, "mean": 0.7167, "spread": 0.26 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.44, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4567, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.805, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.7117, "spread": 0.255 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.973, "judge_a": 0.6, "judge_b": 0.91, "mean": 0.8278, "spread": 0.373 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.995, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.9117, "spread": 0.195 }, "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": 0.08, "judge_a": 0.4, "judge_b": 0.22, "mean": 0.2333, "spread": 0.32 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.26, "judge_a": 0.4, "judge_b": 0.18, "mean": 0.28, "spread": 0.22 } } }, { "rank": 82, "id": "st_20261029_7125", "title": "AMD to invest $1bn in Meta as its Helios racks start running there", "url": "https://thenextweb.com/news/amd-1bn-meta-equity-helios-running", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T19:19:04.000Z", "jev": 0.5271, "judge_a": 0.5255, "judge_b": 0.7085, "score": 0.587, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 71, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "AMD said it will invest $1bn in Meta's equity offering and that its first Helios rack systems, each holding 72 GPUs, are now being tested at Meta with broader deployment expected soon.", "page": "https://rubric.hrih.si/editions/2026-10-29/82/", "card": "https://rubric.hrih.si/og/2026-10-29/82.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.663, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.6442, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.8, "judge_b": 0.9, "mean": 0.7667, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.71, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6967, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.373, "judge_a": 0.4, "judge_b": 0.63, "mean": 0.4675, "spread": 0.258 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.76, "mean": 0.68, "spread": 0.16 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.473, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.3975, "spread": 0.173 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.99, "mean": 0.9, "spread": 0.19 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.2, "judge_a": 0.2, "judge_b": 0.2, "mean": 0.2, "spread": 0 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.25, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1933, "spread": 0.17 } } }, { "rank": 83, "id": "st_20261029_7349", "title": "Starboard, Rules of the Road for Satellites", "url": "https://tokeloshe.github.io/starboard-protocol/", "domain": "tokeloshe.github.io", "site": "tokeloshe.github.io", "categories": [ "programming", "hardware" ], "category_names": [ "Programming & dev tools", "Hardware & devices" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T23:34:10.000Z", "jev": 0.6064, "judge_a": 0.482, "judge_b": 0.6649, "score": 0.5844, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 74, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Starboard is an open-source protocol that gives two satellites facing a close approach the same deterministic rule set, adapted from maritime COLREGS, so both independently agree on which craft must maneuver without any negotiation.", "page": "https://rubric.hrih.si/editions/2026-10-29/83/", "card": "https://rubric.hrih.si/og/2026-10-29/83.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.513, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5608, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.74, "judge_a": 0.7, "judge_b": 0.88, "mean": 0.7733, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.493, "judge_a": 0.4, "judge_b": 0.28, "mean": 0.3911, "spread": 0.213 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.608, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5592, "spread": 0.17 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.94, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.7733, "spread": 0.34 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.98, "judge_a": 0.85, "judge_b": 0.96, "mean": 0.93, "spread": 0.13 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.99, "mean": 0.9367, "spread": 0.09 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.1233, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.3067, "spread": 0.07 } } }, { "rank": 84, "id": "st_20261029_6542", "title": "The Wall from Inside: Running coding agents in Apple containers", "url": "https://fluxum.net/blog/the-wall-from-inside", "domain": "fluxum.net", "site": "fluxum engineering", "categories": [ "programming", "ai", "security", "devops" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security", "Infrastructure & DevOps" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T13:23:20.000Z", "jev": 0.6138, "judge_a": 0.4135, "judge_b": 0.7225, "score": 0.5832, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 75, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The author describes how the outermost \"wall\" layer of his coding-agent setup works: an Apple container that runs Claude Code and Codex without access to his Mac. Unlike Docker Desktop, Apple's container tool gives each container its own lightweight VM and mounts no Mac folders by default.", "page": "https://rubric.hrih.si/editions/2026-10-29/84/", "card": "https://rubric.hrih.si/og/2026-10-29/84.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.478, "judge_a": 0.2, "judge_b": 0.62, "mean": 0.4325, "spread": 0.42 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6333, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.42, "judge_a": 0.25, "judge_b": 0.55, "mean": 0.4067, "spread": 0.3 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.76, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7267, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.897, "judge_a": 0.6, "judge_b": 0.79, "mean": 0.7622, "spread": 0.297 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.998, "judge_a": 0.8, "judge_b": 0.83, "mean": 0.8758, "spread": 0.198 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.8767, "spread": 0.13 }, "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": 0.17, "judge_a": 0.15, "judge_b": 0.12, "mean": 0.1467, "spread": 0.05 } } }, { "rank": 85, "id": "st_20261029_6722", "title": "CISA confirms attackers are exploiting both PHP2Shell flaws", "url": "https://thenextweb.com/news/php2shell-flaws-exploited-cisa-kev", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "claim_checked", "published_at": "2026-10-28T14:23:18.000Z", "jev": 0.6864, "judge_a": 0.7436, "judge_b": 0.7677, "score": 0.6326, "contested": true, "flag_penalty": 0.1, "freshness_penalty": 0, "score_rank": 29, "topic_penalty": 0.08, "repeat_penalty": 0.05, "summary": "CISA has added both PHP2Shell flaws (CVE-2026-109618 and CVE-2026-109619) to its Known Exploited Vulnerabilities catalogue, requiring federal civilian agencies to patch by 29 October, though it has not named victims or detailed its evidence.", "page": "https://rubric.hrih.si/editions/2026-10-29/85/", "card": "https://rubric.hrih.si/og/2026-10-29/85.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.74, "judge_a": 0.72, "judge_b": 0.82, "mean": 0.76, "spread": 0.1 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.82, "judge_a": 0.85, "judge_b": 0.94, "mean": 0.87, "spread": 0.12 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.8, "judge_b": 0.78, "mean": 0.7478, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.81, "judge_a": 0.9, "judge_b": 0.86, "mean": 0.8567, "spread": 0.09 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.697, "judge_a": 0.68, "judge_b": 0.78, "mean": 0.7189, "spread": 0.1 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.683, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6542, "spread": 0.083 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.77, "judge_a": 0.45, "judge_b": 0.88, "mean": 0.7, "spread": 0.43 }, "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": 0.59, "judge_a": 0.3, "judge_b": 0.72, "mean": 0.5367, "spread": 0.42 } } }, { "rank": 86, "id": "st_20261029_6464", "title": "AI Can Draw Blueprints Now", "url": "https://stephenjgallagher.substack.com/p/ai-can-draw-blueprints-now", "domain": "stephenjgallagher.substack.com", "site": "stephenjgallagher.substack.com", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T12:27:45.000Z", "jev": 0.5493, "judge_a": 0.442, "judge_b": 0.7467, "score": 0.5793, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 76, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News reports that a former architect tested 13 AI models on technical drawing and found that Anthropic's Claude Opus 5.5, released in September 2026, can produce permit-quality construction drawings for a simple house when guided by a human reviewer. Earlier models mostly produced crude or inaccurate drawings.", "page": "https://rubric.hrih.si/editions/2026-10-29/86/", "card": "https://rubric.hrih.si/og/2026-10-29/86.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.623, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6308, "spread": 0.37 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.71, "spread": 0.33 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.553, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4844, "spread": 0.203 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.428, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.5358, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.84, "judge_a": 0.55, "judge_b": 0.72, "mean": 0.7033, "spread": 0.29 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.785, "judge_a": 0.5, "judge_b": 0.79, "mean": 0.6917, "spread": 0.29 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.89, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0567, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.35, "judge_b": 0.48, "mean": 0.3733, "spread": 0.19 } } }, { "rank": 87, "id": "st_20261029_6615", "title": "Android 18: Google further restricts access to contacts", "url": "https://www.heise.de/en/news/Android-18-Google-further-restricts-access-to-contacts-11503313.html", "domain": "heise.de", "site": "heise online English", "categories": [ "programming", "security" ], "category_names": [ "Programming & dev tools", "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T13:47:00.000Z", "jev": 0.5606, "judge_a": 0.517, "judge_b": 0.658, "score": 0.5785, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 77, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "With Android 18, Google will block most apps from creating or editing contacts in the background, arguing that few apps need that access. Apps targeting Android 18 must instead use contact intents such as ACTION_INSERT and ACTION_EDIT, which require no permissions, and direct writes will be rejected.", "page": "https://rubric.hrih.si/editions/2026-10-29/87/", "card": "https://rubric.hrih.si/og/2026-10-29/87.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.59, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5367, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.42, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.5667, "spread": 0.36 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.54, "judge_a": 0.5, "judge_b": 0.48, "mean": 0.5067, "spread": 0.06 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.78, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.7333, "spread": 0.08 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.583, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6544, "spread": 0.197 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.735, "judge_a": 0.45, "judge_b": 0.52, "mean": 0.5683, "spread": 0.285 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8867, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.05, "judge_b": 0.04, "mean": 0.0667, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.2, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.16, "spread": 0.1 } } }, { "rank": 88, "id": "st_20261029_6267", "title": "Rocky Linux Creator Built a New \"Not Linux\" Kernel in 11 Days Using AI", "url": "https://itsfoss.com/news/ginux/", "domain": "itsfoss.com", "site": "It's FOSS", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T07:46:50.000Z", "jev": 0.6232, "judge_a": 0.47, "judge_b": 0.6418, "score": 0.5783, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 78, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Gregory Kurtzer, creator of CentOS and Rocky Linux, has released Ginux, an independent Rust-based x86-64 kernel that is not a Linux fork but implements the Linux ABI so unmodified Linux binaries can run. He reports building it in 11 days with an AI coding agent under human direction.", "page": "https://rubric.hrih.si/editions/2026-10-29/88/", "card": "https://rubric.hrih.si/og/2026-10-29/88.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.593, "judge_a": 0.45, "judge_b": 0.62, "mean": 0.5542, "spread": 0.17 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.77, "judge_a": 0.85, "judge_b": 0.9, "mean": 0.84, "spread": 0.13 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.717, "judge_a": 0.6, "judge_b": 0.55, "mean": 0.6222, "spread": 0.167 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.58, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5167, "spread": 0.27 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.653, "judge_a": 0.4, "judge_b": 0.7, "mean": 0.5844, "spread": 0.3 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.72, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.6, "spread": 0.22 }, "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": 0.11, "judge_a": 0.2, "judge_b": 0.08, "mean": 0.13, "spread": 0.12 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.29, "judge_a": 0.6, "judge_b": 0.42, "mean": 0.4367, "spread": 0.31 } } }, { "rank": 89, "id": "st_20261029_6273", "title": "Commentary: AI Recreates Photoshop — Copying Is Cheap, Decisions Aren’t", "url": "https://www.heise.de/en/blog/Commentary-AI-Recreates-Photoshop-Copying-Is-Cheap-Decisions-Aren-t-11502570.html", "domain": "heise.de", "site": "heise online English", "categories": [ "programming", "ai", "product" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Product" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T07:49:00.000Z", "jev": 0.6286, "judge_a": 0.409, "judge_b": 0.6836, "score": 0.5737, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 80, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Brandon Thomas's ArtCraft project released 13 Rust applications, including PhotoCraft, a Photoshop clone largely generated by Anthropic's Claude Opus 5.5, and claimed \"software is over.\" This commentary argues the claim is wrong: PhotoCraft succeeds because it copies a fixed, observable target, while most real software requires decisions no template can…", "page": "https://rubric.hrih.si/editions/2026-10-29/89/", "card": "https://rubric.hrih.si/og/2026-10-29/89.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": 0.72, "judge_a": 0.55, "judge_b": 0.82, "mean": 0.6967, "spread": 0.27 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.893, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.7311, "spread": 0.293 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.563, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5642, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.633, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6178, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.6, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.46, "spread": 0.3 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.87, "judge_a": 0.45, "judge_b": 0.93, "mean": 0.75, "spread": 0.48 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.1, "judge_a": 0.1, "judge_b": 0.03, "mean": 0.0767, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.35, "judge_b": 0.18, "mean": 0.2367, "spread": 0.17 } } }, { "rank": 90, "id": "st_20261029_6541", "title": "Foundational Cryptography in SPARK", "url": "https://www.adacore.com/blog/foundational-cryptography-in-spark", "domain": "adacore.com", "site": "AdaCore", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T13:22:52.000Z", "jev": 0.5471, "judge_a": 0.4695, "judge_b": 0.7046, "score": 0.5737, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 81, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A team used AI to generate formal SPARK specifications and fully proven implementations of AES (all three key sizes), SHA-256, and HMAC-SHA-256, starting from the NSA's Cryptol specifications in its public Foundation repository.", "page": "https://rubric.hrih.si/editions/2026-10-29/90/", "card": "https://rubric.hrih.si/og/2026-10-29/90.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.55, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.59, "spread": 0.22 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.74, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.447, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.4756, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.63, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6033, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.96, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.92, "spread": 0.11 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.988, "judge_a": 0.9, "judge_b": 0.97, "mean": 0.9525, "spread": 0.088 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.93, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.15, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.2933, "spread": 0.23 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.52, "judge_a": 0.35, "judge_b": 0.2, "mean": 0.3567, "spread": 0.32 } } }, { "rank": 91, "id": "st_20261029_6550", "title": "Anthropic Files Its S-1", "url": "https://thezvi.wordpress.com/2026/10/28/anthropic-files-its-s-1/", "domain": "thezvi.wordpress.com", "site": "Don't Worry About the Vase", "categories": [ "ai", "startups", "policy" ], "category_names": [ "AI & machine learning", "Startups & founders", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T13:19:24.000Z", "jev": 0.4545, "judge_a": 0.553, "judge_b": 0.7137, "score": 0.5737, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 82, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Anthropic's S-1 filing, now public, shows $16.8 billion in Q3 revenue, up 45% over three months, with an 11% adjusted operating margin and roughly $131 billion in cash on hand after raising $141 billion.", "page": "https://rubric.hrih.si/editions/2026-10-29/91/", "card": "https://rubric.hrih.si/og/2026-10-29/91.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.653, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6775, "spread": 0.18 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.59, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.7833, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.49, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6033, "spread": 0.23 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.328, "judge_a": 0.4, "judge_b": 0.63, "mean": 0.4525, "spread": 0.303 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.547, "judge_a": 0.75, "judge_b": 0.78, "mean": 0.6922, "spread": 0.233 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.42, "judge_a": 0.2, "judge_b": 0.42, "mean": 0.3467, "spread": 0.22 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.6, "judge_b": 0.98, "mean": 0.8267, "spread": 0.38 }, "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": 0.46, "judge_a": 0.3, "judge_b": 0.55, "mean": 0.4367, "spread": 0.25 } } }, { "rank": 92, "id": "st_20261029_6872", "title": "Claude Refuted ETH Too: Anthropic's 3-SAT Paper Explained", "url": "https://cellcog.ai/blog/claude-refutes-eth-3-sat/", "domain": "cellcog.ai", "site": "CellCog", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T16:54:51.000Z", "jev": 0.4832, "judge_a": 0.5585, "judge_b": 0.6795, "score": 0.5736, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 83, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Anthropic published an October 27, 2026 paper credited to Claude, claiming a deterministic 2^o(n) algorithm for 3-SAT that refutes the Exponential Time Hypothesis and, with it, the stronger SETH.", "page": "https://rubric.hrih.si/editions/2026-10-29/92/", "card": "https://rubric.hrih.si/og/2026-10-29/92.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.638, "judge_a": 0.8, "judge_b": 0.93, "mean": 0.7892, "spread": 0.293 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.55, "judge_a": 0.8, "judge_b": 0.94, "mean": 0.7633, "spread": 0.39 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.55, "judge_a": 0.45, "judge_b": 0.72, "mean": 0.5733, "spread": 0.27 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.273, "judge_a": 0.2, "judge_b": 0.43, "mean": 0.3008, "spread": 0.23 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.643, "judge_a": 0.7, "judge_b": 0.68, "mean": 0.6744, "spread": 0.057 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.673, "judge_a": 0.7, "judge_b": 0.79, "mean": 0.7208, "spread": 0.118 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.95, "judge_b": 0.92, "mean": 0.9267, "spread": 0.04 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.12, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.15, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.3, "judge_b": 0.56, "mean": 0.3867, "spread": 0.26 } } }, { "rank": 93, "id": "st_20261029_6299", "title": "Luanti (formerly Minetest) reveal a major ContentDB security incident", "url": "https://blog.luanti.org/2026/10/27/contentdb-security-incident/", "domain": "blog.luanti.org", "site": "Luanti Blog", "categories": [ "security" ], "category_names": [ "Security" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T08:45:20.000Z", "jev": 0.5799, "judge_a": 0.4846, "judge_b": 0.656, "score": 0.5735, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 84, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Luanti, the open-source voxel game engine formerly known as Minetest, disclosed that an attacker breached ContentDB, its package repository, through two vulnerabilities, gaining access to user emails, hashed passwords, IP addresses, and uploads.", "page": "https://rubric.hrih.si/editions/2026-10-29/93/", "card": "https://rubric.hrih.si/og/2026-10-29/93.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.507, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.4925, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.72, "judge_a": 0.55, "judge_b": 0.85, "mean": 0.7067, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.387, "judge_a": 0.33, "judge_b": 0.28, "mean": 0.3322, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.663, "judge_a": 0.6, "judge_b": 0.7, "mean": 0.6542, "spread": 0.1 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.653, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7111, "spread": 0.127 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.638, "judge_a": 0.35, "judge_b": 0.38, "mean": 0.4558, "spread": 0.288 }, "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": 0.07, "judge_a": 0.02, "judge_b": 0.01, "mean": 0.0333, "spread": 0.06 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.1233, "spread": 0.11 } } }, { "rank": 94, "id": "st_20261029_6179", "title": "PVisor, VM-isolated containers for agents", "url": "https://github.com/DeepLink-org/pvisor", "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": "2026-10-28T02:17:04.000Z", "jev": 0.5984, "judge_a": 0.3925, "judge_b": 0.7259, "score": 0.5723, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 85, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "PVisor is a developer-preview CLI and runtime that runs Agent CLIs or scripts in isolated jobs, with copy-on-write workspace staging so changed files can be reviewed and selectively applied or discarded.", "page": "https://rubric.hrih.si/editions/2026-10-29/94/", "card": "https://rubric.hrih.si/og/2026-10-29/94.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.502, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4742, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.63, "judge_a": 0.45, "judge_b": 0.82, "mean": 0.6333, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.453, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.4678, "spread": 0.15 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.748, "judge_a": 0.6, "judge_b": 0.85, "mean": 0.7325, "spread": 0.25 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.697, "judge_a": 0.4, "judge_b": 0.86, "mean": 0.6522, "spread": 0.46 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.913, "judge_a": 0.7, "judge_b": 0.9, "mean": 0.8375, "spread": 0.213 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9333, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.25, "judge_b": 0.12, "mean": 0.1533, "spread": 0.16 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.3, "judge_b": 0.08, "mean": 0.1833, "spread": 0.22 } } }, { "rank": 95, "id": "st_20261029_7226", "title": "Stop broken SSH sessions: Ghostty updates its modern terminal", "url": "https://www.heise.de/en/news/Stop-broken-SSH-sessions-Ghostty-updates-its-modern-terminal-11503590.html", "domain": "heise.de", "site": "heise online English", "categories": [ "programming", "it" ], "category_names": [ "Programming & dev tools", "IT & networking" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T20:29:00.000Z", "jev": 0.5808, "judge_a": 0.4815, "judge_b": 0.6526, "score": 0.5715, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 86, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Ghostty, the free terminal emulator for macOS, Linux and FreeBSD, has reached version 1.4.0 after seven months of development. The release compresses idle scrollback to cut memory use by 70% to 90% on text-heavy output, raises the default buffer limit to 50 MB per terminal, and speeds up I/O and…", "page": "https://rubric.hrih.si/editions/2026-10-29/95/", "card": "https://rubric.hrih.si/og/2026-10-29/95.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.598, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4925, "spread": 0.298 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.66, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.48, "judge_a": 0.4, "judge_b": 0.52, "mean": 0.4667, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.683, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.6542, "spread": 0.083 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.723, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7144, "spread": 0.22 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.813, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7108, "spread": 0.213 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.91, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.9233, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.08, "judge_a": 0.1, "judge_b": 0.12, "mean": 0.1, "spread": 0.04 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.35, "judge_a": 0.15, "judge_b": 0.08, "mean": 0.1933, "spread": 0.27 } } }, { "rank": 96, "id": "st_20261029_6747", "title": "Brazilian skimmer gang hijacks Plausible analytics script on Convertr checkouts", "url": "https://www.bleepingcomputer.com/news/security/hackers-hijack-convertr-analytics-script-to-steal-payment-card-data/", "domain": "bleepingcomputer.com", "site": "BleepingComputer", "categories": [ "security", "fintech" ], "category_names": [ "Security", "Fintech" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T15:11:48.000Z", "jev": 0.5416, "judge_a": 0.456, "judge_b": 0.7129, "score": 0.5701, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 87, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Brazilian card-skimming gang reportedly hijacked a shared analytics script (a Plausible tracker) hosted by e-commerce platform Convertr, injecting code that stole card numbers, CVVs, names, and CPF tax IDs from checkouts on merchant sites from October 24.", "page": "https://rubric.hrih.si/editions/2026-10-29/96/", "card": "https://rubric.hrih.si/og/2026-10-29/96.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.538, "judge_a": 0.4, "judge_b": 0.72, "mean": 0.5525, "spread": 0.32 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.68, "judge_a": 0.6, "judge_b": 0.9, "mean": 0.7267, "spread": 0.3 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.287, "judge_a": 0.4, "judge_b": 0.58, "mean": 0.4222, "spread": 0.293 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.675, "judge_a": 0.5, "judge_b": 0.78, "mean": 0.6517, "spread": 0.28 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.45, "judge_a": 0.55, "judge_b": 0.7, "mean": 0.5667, "spread": 0.25 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.725, "judge_a": 0.45, "judge_b": 0.48, "mean": 0.5517, "spread": 0.275 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.9, "judge_a": 0.85, "judge_b": 0.95, "mean": 0.9, "spread": 0.1 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.15, "judge_b": 0.18, "mean": 0.1467, "spread": 0.07 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.3, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.2067, "spread": 0.18 } } }, { "rank": 97, "id": "st_20261029_7104", "title": "Kavach – Replay production crashes locally without a DB or network", "url": "https://github.com/kavachlabs/kavach", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "New project", "depth": "three_judges", "published_at": "2026-10-28T19:28:18.000Z", "jev": 0.6583, "judge_a": 0.382, "judge_b": 0.6635, "score": 0.5679, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 88, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Kavach is a pre-alpha open-source tool that records a service's inputs, clock reads, and random reads, then flushes a fixture when the service crashes or breaks an invariant. The fixture replays locally without any broker, database, or network.", "page": "https://rubric.hrih.si/editions/2026-10-29/97/", "card": "https://rubric.hrih.si/og/2026-10-29/97.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.538, "judge_a": 0.3, "judge_b": 0.52, "mean": 0.4525, "spread": 0.238 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.69, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7033, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.533, "judge_a": 0.4, "judge_b": 0.48, "mean": 0.4711, "spread": 0.133 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.81, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.7433, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.967, "judge_a": 0.5, "judge_b": 0.82, "mean": 0.7622, "spread": 0.467 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.965, "judge_a": 0.7, "judge_b": 0.86, "mean": 0.8417, "spread": 0.265 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.94, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.94, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.09, "judge_a": 0.45, "judge_b": 0.12, "mean": 0.22, "spread": 0.36 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.22, "judge_a": 0.45, "judge_b": 0.25, "mean": 0.3067, "spread": 0.23 } } }, { "rank": 98, "id": "st_20261029_6827", "title": "Mistral Responses API", "url": "https://docs.mistral.ai/studio/conversations/responses", "domain": "docs.mistral.ai", "site": "docs.mistral.ai", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Tutorial", "depth": "three_judges", "published_at": "2026-10-28T16:15:38.000Z", "jev": 0.5823, "judge_a": 0.456, "judge_b": 0.6649, "score": 0.5677, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 89, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Mistral has released the Responses API in public preview, letting developers use Mistral models through the OpenAI Responses request format. It supports text, image and document input, function calling, streaming, structured output, and input token counting.", "page": "https://rubric.hrih.si/editions/2026-10-29/98/", "card": "https://rubric.hrih.si/og/2026-10-29/98.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.585, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5183, "spread": 0.27 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.41, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.53, "spread": 0.38 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.42, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.5467, "spread": 0.3 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.918, "judge_a": 0.8, "judge_b": 0.85, "mean": 0.8558, "spread": 0.117 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.663, "judge_a": 0.4, "judge_b": 0.78, "mean": 0.6144, "spread": 0.38 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.783, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.6675, "spread": 0.183 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.98, "mean": 0.9367, "spread": 0.08 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.13, "judge_a": 0.3, "judge_b": 0.35, "mean": 0.26, "spread": 0.22 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.14, "judge_a": 0.1, "judge_b": 0.18, "mean": 0.14, "spread": 0.08 } } }, { "rank": 99, "id": "st_20261029_7087", "title": "A proof-of-concept formalization of the IRS federal tax forms", "url": "https://github.com/emmilco/us-tax-model", "domain": "github.com", "site": "GitHub", "categories": [ "programming", "ai" ], "category_names": [ "Programming & dev tools", "AI & machine learning" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T18:30:53.000Z", "jev": 0.6228, "judge_a": 0.4175, "judge_b": 0.66, "score": 0.5667, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 91, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "A Hacker News \"Show HN\" project formalizes the IRS federal individual income tax forms and instructions as cited, machine-checkable YAML data, with a guided self-preparation interview as its first client. The software computes values and explains them but never files or transmits returns.", "page": "https://rubric.hrih.si/editions/2026-10-29/99/", "card": "https://rubric.hrih.si/og/2026-10-29/99.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.553, "judge_a": 0.35, "judge_b": 0.56, "mean": 0.4875, "spread": 0.21 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.67, "judge_a": 0.55, "judge_b": 0.78, "mean": 0.6667, "spread": 0.23 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.467, "judge_a": 0.25, "judge_b": 0.45, "mean": 0.3889, "spread": 0.217 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.653, "judge_a": 0.35, "judge_b": 0.62, "mean": 0.5408, "spread": 0.303 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.957, "judge_a": 0.8, "judge_b": 0.88, "mean": 0.8789, "spread": 0.157 }, "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": 0.93, "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": 0.1, "judge_b": 0.02, "mean": 0.0633, "spread": 0.08 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.17, "judge_a": 0.25, "judge_b": 0.08, "mean": 0.1667, "spread": 0.17 } } }, { "rank": 100, "id": "st_20261029_7088", "title": "Node 26 Released (LTS)", "url": "https://nodejs.org/en/blog/release/v26.13.0", "domain": "nodejs.org", "site": "nodejs.org", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T18:30:47.000Z", "jev": 0.5526, "judge_a": 0.642, "judge_b": 0.5024, "score": 0.5656, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 92, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Node.js 26.13.0, codenamed 'Lithium', marks the promotion of the 26.x line to Long Term Support, with updates scheduled through April 2029. Beyond metadata reflecting the LTS status, it contains no code changes from 26.12.1.", "page": "https://rubric.hrih.si/editions/2026-10-29/100/", "card": "https://rubric.hrih.si/og/2026-10-29/100.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.533, "judge_a": 0.55, "judge_b": 0.42, "mean": 0.5008, "spread": 0.13 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.36, "judge_a": 0.7, "judge_b": 0.38, "mean": 0.48, "spread": 0.34 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.663, "judge_a": 0.8, "judge_b": 0.68, "mean": 0.7144, "spread": 0.137 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.735, "judge_a": 0.7, "judge_b": 0.55, "mean": 0.6617, "spread": 0.185 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.897, "judge_a": 0.6, "judge_b": 0.93, "mean": 0.8089, "spread": 0.33 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.55, "judge_a": 0.2, "judge_b": 0.12, "mean": 0.29, "spread": 0.43 }, "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": 0.12, "judge_a": 0.05, "judge_b": 0.02, "mean": 0.0633, "spread": 0.1 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.09, "judge_a": 0.05, "judge_b": 0.03, "mean": 0.0567, "spread": 0.06 } } }, { "rank": 101, "id": "st_20261029_6132", "title": "Letting Agents Loose on the Network", "url": "https://cloudywithachanceoflatency.net/blog_04-investigative-tools.html", "domain": "cloudywithachanceoflatency.net", "site": "Cloudy, with a chance of latency", "categories": [ "programming", "ai", "devops", "it" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Infrastructure & DevOps", "IT & networking" ], "format": "Write-up", "depth": "three_judges", "published_at": "2026-10-28T05:09:02.000Z", "jev": 0.6005, "judge_a": 0.387, "judge_b": 0.7092, "score": 0.5655, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 93, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article argues that network incident response lags far behind software observability, and that AI agents investigating live networks need more than standard tools like ping and mtr.", "page": "https://rubric.hrih.si/editions/2026-10-29/101/", "card": "https://rubric.hrih.si/og/2026-10-29/101.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.523, "judge_a": 0.35, "judge_b": 0.68, "mean": 0.5175, "spread": 0.33 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.6, "judge_b": 0.88, "mean": 0.7267, "spread": 0.28 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.573, "judge_a": 0.3, "judge_b": 0.62, "mean": 0.4978, "spread": 0.32 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.683, "judge_a": 0.45, "judge_b": 0.83, "mean": 0.6542, "spread": 0.38 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.883, "judge_a": 0.5, "judge_b": 0.7, "mean": 0.6944, "spread": 0.383 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.978, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.9125, "spread": 0.178 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.89, "judge_a": 0.9, "judge_b": 0.94, "mean": 0.91, "spread": 0.05 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.11, "judge_a": 0.3, "judge_b": 0.18, "mean": 0.1967, "spread": 0.19 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.44, "judge_a": 0.4, "judge_b": 0.38, "mean": 0.4067, "spread": 0.06 } } }, { "rank": 102, "id": "st_20261029_7134", "title": "Local-First Means Three Different Things", "url": "https://hologram.page/blog/local-first-means-three-different-things", "domain": "hologram.page", "site": "Hologram", "categories": [ "programming" ], "category_names": [ "Programming & dev tools" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T19:54:56.000Z", "jev": 0.6052, "judge_a": 0.454, "judge_b": 0.6275, "score": 0.5622, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 97, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Hrih News covers a blog post by Bart Blast, creator of Hologram, arguing that \"local-first\" is used to mean three different things: ownership of data (the Ink & Switch essay's original vision), offline operation, and instant UI speed.", "page": "https://rubric.hrih.si/editions/2026-10-29/102/", "card": "https://rubric.hrih.si/og/2026-10-29/102.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.558, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.4958, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6733, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.687, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6689, "spread": 0.12 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.748, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.6892, "spread": 0.148 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.573, "judge_a": 0.5, "judge_b": 0.58, "mean": 0.5511, "spread": 0.08 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.76, "judge_a": 0.65, "judge_b": 0.62, "mean": 0.6767, "spread": 0.14 }, "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": 0.22, "judge_a": 0.35, "judge_b": 0.28, "mean": 0.2833, "spread": 0.13 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.18, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.1867, "spread": 0.02 } } }, { "rank": 103, "id": "st_20261029_6955", "title": "Gemini 4 Carbon: Our next frontier model", "url": "https://deepmind.google/blog/gemini-4-carbon-our-next-frontier-model/", "domain": "deepmind.google", "site": "Google DeepMind Blog", "categories": [ "programming", "ai", "security" ], "category_names": [ "Programming & dev tools", "AI & machine learning", "Security" ], "format": "Release", "depth": "three_judges", "published_at": "2026-10-28T17:00:58.000Z", "jev": 0.5787, "judge_a": 0.5338, "judge_b": 0.571, "score": 0.5611, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 99, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Google announced Gemini 4 Carbon, its new frontier model, with state-of-the-art results on cybersecurity benchmarks for finding, exploiting, and patching vulnerabilities, and a #1 ranking on the Artificial Analysis Intelligence Index v5.0.", "page": "https://rubric.hrih.si/editions/2026-10-29/103/", "card": "https://rubric.hrih.si/og/2026-10-29/103.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.73, "judge_a": 0.72, "judge_b": 0.78, "mean": 0.7433, "spread": 0.06 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.62, "judge_a": 0.85, "judge_b": 0.86, "mean": 0.7767, "spread": 0.24 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.743, "judge_a": 0.85, "judge_b": 0.82, "mean": 0.8044, "spread": 0.107 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.658, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.6792, "spread": 0.18 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.623, "judge_a": 0.35, "judge_b": 0.58, "mean": 0.5178, "spread": 0.273 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.558, "judge_a": 0.3, "judge_b": 0.58, "mean": 0.4792, "spread": 0.28 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.96, "mean": 0.93, "spread": 0.06 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.29, "judge_a": 0.6, "judge_b": 0.82, "mean": 0.57, "spread": 0.53 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.45, "judge_a": 0.6, "judge_b": 0.62, "mean": 0.5567, "spread": 0.17 } } }, { "rank": 104, "id": "st_20261029_7068", "title": "Can LLMs Supercharge Geolocations? Big Improvements but Humans Remain Key", "url": "https://www.bellingcat.com/resources/2026/10/22/can-llms-supercharge-geolocations-our-tests-show-big-improvements-but-humans-remain-key/", "domain": "bellingcat.com", "site": "bellingcat", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T18:57:43.000Z", "jev": 0.5666, "judge_a": 0.4435, "judge_b": 0.6645, "score": 0.5582, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 102, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Bellingcat ran a benchmark of 52 unpublished, hard-to-geolocate photos across closed and open AI models, scoring them on regional accuracy and fine-grained distance. Anthropic's Claude Opus and Fable and Google's Gemini 4 Argon performed best, often narrowing locations faster than humans could.", "page": "https://rubric.hrih.si/editions/2026-10-29/104/", "card": "https://rubric.hrih.si/og/2026-10-29/104.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.605, "judge_a": 0.45, "judge_b": 0.68, "mean": 0.5783, "spread": 0.23 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.52, "judge_a": 0.55, "judge_b": 0.84, "mean": 0.6367, "spread": 0.32 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.657, "judge_a": 0.35, "judge_b": 0.45, "mean": 0.4856, "spread": 0.307 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.48, "judge_a": 0.4, "judge_b": 0.62, "mean": 0.5, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.907, "judge_a": 0.7, "judge_b": 0.82, "mean": 0.8089, "spread": 0.207 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.76, "judge_a": 0.5, "judge_b": 0.68, "mean": 0.6467, "spread": 0.26 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.8, "judge_b": 0.96, "mean": 0.8933, "spread": 0.16 }, "promotional": { "name": "Promotional", "weight": -0.19, "jev": 0.07, "judge_a": 0.1, "judge_b": 0.08, "mean": 0.0833, "spread": 0.03 }, "overclaim": { "name": "Overclaim", "weight": -0.11, "jev": 0.19, "judge_a": 0.2, "judge_b": 0.18, "mean": 0.19, "spread": 0.02 } } }, { "rank": 105, "id": "st_20261029_6724", "title": "Canada and Europe Need a Way to See the AI They Cannot Access", "url": "https://techpolicy.press/canada-and-europe-need-a-way-to-see-the-ai-they-cannot-access", "domain": "techpolicy.press", "site": "Tech Policy Press", "categories": [ "ai", "policy" ], "category_names": [ "AI & machine learning", "Policy & regulation" ], "format": "Analysis", "depth": "three_judges", "published_at": "2026-10-28T14:55:01.000Z", "jev": 0.5567, "judge_a": 0.481, "judge_b": 0.6337, "score": 0.5571, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 103, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The article argues that Washington's push to rename AI as \"super intelligence\" blurs a meaningful capability threshold, since the term now covers all AI systems. It shows that companies and governments keep moving their own capability lines, that current tests are unreliable at high capability levels, and that internal models…", "page": "https://rubric.hrih.si/editions/2026-10-29/105/", "card": "https://rubric.hrih.si/og/2026-10-29/105.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.623, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.6142, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.6, "judge_a": 0.6, "judge_b": 0.78, "mean": 0.66, "spread": 0.18 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.883, "judge_a": 0.6, "judge_b": 0.67, "mean": 0.7178, "spread": 0.283 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.373, "judge_a": 0.35, "judge_b": 0.55, "mean": 0.4242, "spread": 0.2 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.6, "judge_a": 0.7, "judge_b": 0.72, "mean": 0.6733, "spread": 0.12 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.415, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.3783, "spread": 0.12 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.92, "judge_a": 0.5, "judge_b": 0.88, "mean": 0.7667, "spread": 0.42 }, "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": 0.23, "judge_a": 0.25, "judge_b": 0.32, "mean": 0.2667, "spread": 0.09 } } }, { "rank": 106, "id": "st_20261029_6721", "title": "Satya Nadella says he was wrong to think AI alignment would come with scale", "url": "https://thenextweb.com/news/satya-nadella-ai-alignment-20vc", "domain": "thenextweb.com", "site": "The Next Web", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T14:38:38.000Z", "jev": 0.6638, "judge_a": 0.3975, "judge_b": 0.605, "score": 0.5554, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 106, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "Microsoft CEO Satya Nadella said on the 20VC podcast that he no longer believes AI alignment will arise simply from scaling models, calling it a harder, separate problem.", "page": "https://rubric.hrih.si/editions/2026-10-29/106/", "card": "https://rubric.hrih.si/og/2026-10-29/106.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.695, "judge_a": 0.35, "judge_b": 0.56, "mean": 0.535, "spread": 0.345 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.7, "judge_a": 0.55, "judge_b": 0.75, "mean": 0.6667, "spread": 0.2 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.88, "judge_a": 0.6, "judge_b": 0.72, "mean": 0.7333, "spread": 0.28 }, "practical_relevance": { "name": "Practical relevance", 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"thenextweb.com", "site": "The Next Web", "categories": [ "ai", "hardware" ], "category_names": [ "AI & machine learning", "Hardware & devices" ], "format": "News", "depth": "three_judges", "published_at": "2026-10-28T09:02:29.000Z", "jev": 0.455, "judge_a": 0.522, "judge_b": 0.6793, "score": 0.5521, "contested": true, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 108, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "The Information reported that OpenAI, one of Nvidia's largest chip customers, plans to buy Google's TPU AI processors, though the report is unverified and OpenAI declined to comment.", "page": "https://rubric.hrih.si/editions/2026-10-29/107/", "card": "https://rubric.hrih.si/og/2026-10-29/107.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.625, "judge_a": 0.55, "judge_b": 0.67, "mean": 0.615, "spread": 0.12 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.56, "judge_a": 0.72, "judge_b": 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0.08, "mean": 0.29, "spread": 0.56 } } }, { "rank": 108, "id": "st_20261029_6407", "title": "MotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model", "url": "https://nvlabs.github.io/motionbricks/", "domain": "nvlabs.github.io", "site": "nvlabs.github.io", "categories": [ "ai" ], "category_names": [ "AI & machine learning" ], "format": "Paper", "depth": "three_judges", "published_at": "2026-10-28T11:03:55.000Z", "jev": 0.5481, "judge_a": 0.407, "judge_b": 0.6985, "score": 0.5512, "contested": false, "flag_penalty": 0, "freshness_penalty": 0, "score_rank": 109, "topic_penalty": 0.08, "repeat_penalty": 0, "summary": "NVIDIA's MotionBricks is a real-time neural motion generation framework that uses a single modular latent model trained on over 350,000 motion clips to produce navigation and object-interaction animation without per-task fine-tuning.", "page": "https://rubric.hrih.si/editions/2026-10-29/108/", "card": "https://rubric.hrih.si/og/2026-10-29/108.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.785, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6717, "spread": 0.335 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.53, "judge_a": 0.55, "judge_b": 0.9, "mean": 0.66, "spread": 0.37 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.58, "judge_a": 0.4, "judge_b": 0.55, "mean": 0.51, "spread": 0.18 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.678, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.6325, "spread": 0.22 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.87, "judge_a": 0.55, "judge_b": 0.86, "mean": 0.76, "spread": 0.32 }, "technical_depth": { "name": "Technical depth", "weight": 0.05, "jev": 0.57, "judge_a": 0.45, "judge_b": 0.78, "mean": 0.6, "spread": 0.33 }, "new_information": { "name": "New information", "weight": 0.05, "jev": 0.93, "judge_a": 0.9, "judge_b": 0.98, "mean": 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which argues that providing precise, text-based context (especially Markdown) is what separates productive AI coding from costly rework.", "page": "https://rubric.hrih.si/editions/2026-10-29/109/", "card": "https://rubric.hrih.si/og/2026-10-29/109.png", "breakdown": { "significance": { "name": "Significance", "weight": 0.24, "jev": 0.58, "judge_a": 0.3, "judge_b": 0.48, "mean": 0.4533, "spread": 0.28 }, "reader_interest": { "name": "Reader interest", "weight": 0.26, "jev": 0.64, "judge_a": 0.5, "judge_b": 0.72, "mean": 0.62, "spread": 0.22 }, "audience_breadth": { "name": "Audience breadth", "weight": 0.13, "jev": 0.893, "judge_a": 0.6, "judge_b": 0.68, "mean": 0.7244, "spread": 0.293 }, "practical_relevance": { "name": "Practical relevance", "weight": 0.22, "jev": 0.903, "judge_a": 0.7, "judge_b": 0.78, "mean": 0.7942, "spread": 0.203 }, "substantiated": { "name": "Substantiated", "weight": 0.05, "jev": 0.423, "judge_a": 0.3, "judge_b": 0.42, "mean": 0.3811, "spread": 0.123 }, 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