{
  "edition": "2026-11-08",
  "name": "Rubric, edition of Sunday 8 November 2026",
  "url": "https://rubric.hrih.si/editions/2026-11-08/",
  "published_at": "2026-11-08T06:00:00Z",
  "rubric_version": "2.3",
  "how_ranking_works": "https://rubric.hrih.si/how-ranking-works/",
  "license": {
    "name": "CC BY 4.0",
    "url": "https://creativecommons.org/licenses/by/4.0/",
    "attribution": "Rubric (rubric.hrih.si)",
    "notice": "CC BY 4.0, Rubric (rubric.hrih.si), edition of 2026-11-08: https://rubric.hrih.si/editions/2026-11-08/"
  },
  "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_20261108_17764",
      "title": "Hacking Two Automakers: ForgeRock Path Confusion, RCE, and Remote Vehicle Access",
      "url": "https://samcurry.net/forgerock",
      "domain": "samcurry.net",
      "site": "samcurry.net",
      "categories": [
        "security"
      ],
      "category_names": [
        "Security"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T12:21:36.000Z",
      "jev": 0.7051,
      "judge_a": 0.7765,
      "judge_b": 0.8547,
      "score": 0.7787,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 1,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "A security researchers' writeup describes chaining a ForgeRock path-confusion bypass of an edge WAF with default Felix console credentials to reach OpenIDM's OSGi bundle inventory on two automakers' login servers.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/1/",
      "card": "https://rubric.hrih.si/og/2026-11-08/1.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.66,
          "judge_a": 0.8,
          "judge_b": 0.88,
          "mean": 0.78,
          "spread": 0.22
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.78,
          "judge_a": 0.93,
          "judge_b": 0.96,
          "mean": 0.89,
          "spread": 0.18
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.57,
          "judge_a": 0.72,
          "judge_b": 0.58,
          "mean": 0.6233,
          "spread": 0.15
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.77,
          "judge_a": 0.6,
          "judge_b": 0.82,
          "mean": 0.73,
          "spread": 0.22
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.943,
          "judge_a": 0.85,
          "judge_b": 0.94,
          "mean": 0.9111,
          "spread": 0.093
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.998,
          "judge_a": 0.92,
          "judge_b": 0.99,
          "mean": 0.9692,
          "spread": 0.078
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.9,
          "judge_a": 0.95,
          "judge_b": 0.98,
          "mean": 0.9433,
          "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": 0.31,
          "judge_a": 0.12,
          "judge_b": 0.05,
          "mean": 0.16,
          "spread": 0.26
        }
      }
    },
    {
      "rank": 2,
      "id": "st_20261108_18090",
      "title": "How Do Committees Invent?",
      "url": "https://www.melconway.com/research/committees.html",
      "domain": "melconway.com",
      "site": "melconway.com",
      "categories": [
        "programming"
      ],
      "category_names": [
        "Programming & dev tools"
      ],
      "format": "Analysis",
      "depth": "claim_checked",
      "published_at": "2026-11-07T19:57:16.000Z",
      "jev": 0.7006,
      "judge_a": 0.7877,
      "judge_b": 0.8074,
      "score": 0.7652,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 2,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Melvin Conway's 1968 Datamation paper, republished on his site with a later note, argues that any organization designing a system will produce a design that copies its communication structure. He bases this on a graph-homomorphism argument and anecdotes, such as a five-person COBOL team and a three-person ALGOL team producing five-phase and three-phase compilers. These are illustrations, not measurements, and no data is cited. For builders, the implication is to organize teams around needed communication and keep them small and flexible.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/2/",
      "card": "https://rubric.hrih.si/og/2026-11-08/2.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.815,
          "judge_a": 0.8,
          "judge_b": 0.78,
          "mean": 0.7983,
          "spread": 0.035
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.75,
          "judge_a": 0.9,
          "judge_b": 0.9,
          "mean": 0.85,
          "spread": 0.15
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.947,
          "judge_a": 0.9,
          "judge_b": 0.82,
          "mean": 0.8889,
          "spread": 0.127
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.668,
          "judge_a": 0.8,
          "judge_b": 0.82,
          "mean": 0.7625,
          "spread": 0.152
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.307,
          "judge_a": 0.6,
          "judge_b": 0.72,
          "mean": 0.5422,
          "spread": 0.413
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.538,
          "judge_a": 0.5,
          "judge_b": 0.7,
          "mean": 0.5792,
          "spread": 0.2
        },
        "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": 0.02,
          "judge_b": 0,
          "mean": 0.02,
          "spread": 0.04
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.37,
          "judge_a": 0.25,
          "judge_b": 0.18,
          "mean": 0.2667,
          "spread": 0.19
        }
      }
    },
    {
      "rank": 3,
      "id": "st_20261108_17783",
      "title": "Weekend at Bernie's II",
      "url": "https://nesbitt.io/2026/11/06/weekend-at-bernies-ii.html",
      "domain": "nesbitt.io",
      "site": "Andrew Nesbitt",
      "categories": [
        "programming",
        "ai",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Security"
      ],
      "format": "Paper",
      "depth": "claim_checked",
      "published_at": "2026-11-07T12:50:34.000Z",
      "jev": 0.7122,
      "judge_a": 0.702,
      "judge_b": 0.8467,
      "score": 0.7536,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 3,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Andrew Nesbitt re-ran his May classification of ecosyste.ms critical repos: 421 that were dead, dormant or unknown are active again, though the overall split moved under two points. Ownership rarely changed; a new person took over in six cases. AI-disclosed commits rose from 3.3% to 7.9% of human commits in revived repos, mostly from each repo's top committer. Advisories from previously inactive repos waited a median 38 days for GitHub's reviewed database, versus 11 for active ones, delaying Dependabot alerts.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/3/",
      "card": "https://rubric.hrih.si/og/2026-11-08/3.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.693,
          "judge_a": 0.55,
          "judge_b": 0.82,
          "mean": 0.6875,
          "spread": 0.27
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.77,
          "judge_a": 0.82,
          "judge_b": 0.93,
          "mean": 0.84,
          "spread": 0.16
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.797,
          "judge_a": 0.7,
          "judge_b": 0.78,
          "mean": 0.7589,
          "spread": 0.097
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.708,
          "judge_a": 0.8,
          "judge_b": 0.86,
          "mean": 0.7892,
          "spread": 0.152
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.933,
          "judge_a": 0.85,
          "judge_b": 0.94,
          "mean": 0.9078,
          "spread": 0.09
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.818,
          "judge_a": 0.6,
          "judge_b": 0.73,
          "mean": 0.7158,
          "spread": 0.218
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.92,
          "judge_a": 0.9,
          "judge_b": 0.97,
          "mean": 0.93,
          "spread": 0.07
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.04,
          "judge_a": 0.03,
          "judge_b": 0.03,
          "mean": 0.0333,
          "spread": 0.01
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.36,
          "judge_a": 0.2,
          "judge_b": 0.08,
          "mean": 0.2133,
          "spread": 0.28
        }
      }
    },
    {
      "rank": 4,
      "id": "st_20261108_17936",
      "title": "Google Described Its Agent Sandbox. I Built One with Firecracker and Measured It",
      "url": "https://antonau.tech/posts/2026/11/agents-microvm-firecracker/",
      "domain": "antonau.tech",
      "site": "antonau.tech",
      "categories": [
        "programming",
        "ai",
        "security",
        "devops"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "New project",
      "depth": "three_judges",
      "published_at": "2026-11-07T16:01:40.000Z",
      "jev": 0.6854,
      "judge_a": 0.6937,
      "judge_b": 0.8379,
      "score": 0.7389,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 4,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "A developer on the antonau.tech blog built a Firecracker-based version of the agent sandbox pattern Google described in its October 22 Agent Compute Engine post, which gives each AI agent session a checkpointable, network-less microVM.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/4/",
      "card": "https://rubric.hrih.si/og/2026-11-08/4.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.533,
          "judge_a": 0.55,
          "judge_b": 0.72,
          "mean": 0.6008,
          "spread": 0.188
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.79,
          "judge_a": 0.82,
          "judge_b": 0.94,
          "mean": 0.85,
          "spread": 0.15
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.53,
          "judge_a": 0.6,
          "judge_b": 0.68,
          "mean": 0.6033,
          "spread": 0.15
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.823,
          "judge_a": 0.78,
          "judge_b": 0.9,
          "mean": 0.8342,
          "spread": 0.12
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.99,
          "judge_a": 0.85,
          "judge_b": 0.93,
          "mean": 0.9233,
          "spread": 0.14
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 1,
          "judge_a": 0.92,
          "judge_b": 0.99,
          "mean": 0.97,
          "spread": 0.08
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.83,
          "judge_a": 0.8,
          "judge_b": 0.98,
          "mean": 0.87,
          "spread": 0.18
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.06,
          "judge_a": 0.04,
          "judge_b": 0.01,
          "mean": 0.0367,
          "spread": 0.05
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.25,
          "judge_a": 0.2,
          "judge_b": 0.08,
          "mean": 0.1767,
          "spread": 0.17
        }
      }
    },
    {
      "rank": 5,
      "id": "st_20261108_17569",
      "title": "What happens when a fake coding test reaches a security company?",
      "url": "https://www.sophos.com/en-us/blog/fake-coding-test-reaches-security-company",
      "domain": "sophos.com",
      "site": "SOPHOS",
      "categories": [
        "programming",
        "security",
        "it"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security",
        "IT & networking"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T03:12:31.000Z",
      "jev": 0.7649,
      "judge_a": 0.628,
      "judge_b": 0.7814,
      "score": 0.7248,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 5,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Sophos says a fake GitHub coding assignment compromised one employee's Mac in September, when a hidden VS Code workspace command installed the BeaverTail and InvisibleFerret malware within two minutes. A partner's tip revealed it, because the Mac's endpoint agent was broken. Sophos links the technique to North Korean groups and to Binance's reported $6.3 billion theft, though the Binance details, the detection claim and the advisory figures could not be independently checked. Its advice: reconcile identity sign-ins against endpoint telemetry, and limit what AI tools can reach.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/5/",
      "card": "https://rubric.hrih.si/og/2026-11-08/5.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.7,
          "judge_a": 0.6,
          "judge_b": 0.78,
          "mean": 0.6933,
          "spread": 0.18
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.83,
          "judge_a": 0.85,
          "judge_b": 0.9,
          "mean": 0.86,
          "spread": 0.07
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.853,
          "judge_a": 0.8,
          "judge_b": 0.78,
          "mean": 0.8111,
          "spread": 0.073
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.873,
          "judge_a": 0.8,
          "judge_b": 0.88,
          "mean": 0.8508,
          "spread": 0.08
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.73,
          "judge_a": 0.6,
          "judge_b": 0.8,
          "mean": 0.71,
          "spread": 0.2
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.748,
          "judge_a": 0.55,
          "judge_b": 0.7,
          "mean": 0.6658,
          "spread": 0.198
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.89,
          "judge_a": 0.5,
          "judge_b": 0.84,
          "mean": 0.7433,
          "spread": 0.39
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.09,
          "judge_a": 0.35,
          "judge_b": 0.18,
          "mean": 0.2067,
          "spread": 0.26
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.21,
          "judge_a": 0.3,
          "judge_b": 0.16,
          "mean": 0.2233,
          "spread": 0.14
        }
      }
    },
    {
      "rank": 6,
      "id": "st_20261108_17508",
      "title": "How hard could implementing DTLS 1.3 possibly be?",
      "url": "https://joturk.net/posts/dtls",
      "domain": "joturk.net",
      "site": "Jo Turk",
      "categories": [
        "programming",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T00:47:35.000Z",
      "jev": 0.6849,
      "judge_a": 0.622,
      "judge_b": 0.8246,
      "score": 0.7104,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 6,
      "topic_penalty": 0.02,
      "repeat_penalty": 0,
      "summary": "Jo Turk describes how a volunteer team, funded partly by an NLnet grant, built DTLS 1.3 for Go's pion/dtls, which became the default on October 31 after about ten months and roughly 400 commits (the author's own account). It covers a one-round-trip handshake, post-quantum key exchange, Connection IDs, 0-RTT and Encrypted Client Hello. One finding: the 1216-byte ML-KEM key share broke wolfSSL, which rejects fragmented first ClientHellos, so Pion now sends plain X25519 first and falls back to ML-KEM through HelloRetryRequest.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/6/",
      "card": "https://rubric.hrih.si/og/2026-11-08/6.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.643,
          "judge_a": 0.55,
          "judge_b": 0.78,
          "mean": 0.6575,
          "spread": 0.23
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.73,
          "judge_a": 0.82,
          "judge_b": 0.94,
          "mean": 0.83,
          "spread": 0.21
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.577,
          "judge_a": 0.5,
          "judge_b": 0.62,
          "mean": 0.5656,
          "spread": 0.12
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.743,
          "judge_a": 0.6,
          "judge_b": 0.84,
          "mean": 0.7275,
          "spread": 0.24
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.913,
          "judge_a": 0.72,
          "judge_b": 0.93,
          "mean": 0.8544,
          "spread": 0.21
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.98,
          "judge_a": 0.92,
          "judge_b": 0.96,
          "mean": 0.9533,
          "spread": 0.06
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.91,
          "judge_a": 0.7,
          "judge_b": 0.99,
          "mean": 0.8667,
          "spread": 0.29
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.06,
          "judge_a": 0.08,
          "judge_b": 0.04,
          "mean": 0.06,
          "spread": 0.04
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.24,
          "judge_a": 0.2,
          "judge_b": 0.08,
          "mean": 0.1733,
          "spread": 0.16
        }
      }
    },
    {
      "rank": 7,
      "id": "st_20261108_18077",
      "title": "From the server to SQLite to reactive Svelte state",
      "url": "https://www.milieu.cloud/blog/from-the-server-to-sqlite-to-reactive-svelte-state",
      "domain": "milieu.cloud",
      "site": "Milieu Engineering",
      "categories": [
        "programming",
        "data"
      ],
      "category_names": [
        "Programming & dev tools",
        "Data"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T19:06:12.000Z",
      "jev": 0.6692,
      "judge_a": 0.544,
      "judge_b": 0.7908,
      "score": 0.668,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 8,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Milieu's engineering team describes a local-first design in which a new browser starts as a server-rendered SvelteKit app while a worker downloads the account's SQLite file. The tab switches to the in-browser database (SQLite on WebAssembly, stored in OPFS) only once its per-owner cursors reach the highest server state the tab has seen, so the screen never goes backwards. Pending changes are replayed on the server's state rather than merged with CRDTs. The claims come from the company's own post, with no independent verification.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/7/",
      "card": "https://rubric.hrih.si/og/2026-11-08/7.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.585,
          "judge_a": 0.35,
          "judge_b": 0.72,
          "mean": 0.5517,
          "spread": 0.37
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.76,
          "judge_a": 0.8,
          "judge_b": 0.9,
          "mean": 0.82,
          "spread": 0.14
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.49,
          "judge_a": 0.5,
          "judge_b": 0.68,
          "mean": 0.5567,
          "spread": 0.19
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.795,
          "judge_a": 0.6,
          "judge_b": 0.85,
          "mean": 0.7483,
          "spread": 0.25
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.527,
          "judge_a": 0.5,
          "judge_b": 0.83,
          "mean": 0.6189,
          "spread": 0.33
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.975,
          "judge_a": 0.9,
          "judge_b": 0.95,
          "mean": 0.9417,
          "spread": 0.075
        },
        "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": 0.07,
          "judge_a": 0.2,
          "judge_b": 0.08,
          "mean": 0.1167,
          "spread": 0.13
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.12,
          "judge_a": 0.2,
          "judge_b": 0.12,
          "mean": 0.1467,
          "spread": 0.08
        }
      }
    },
    {
      "rank": 8,
      "id": "st_20261108_18110",
      "title": "Workflow dependency lockfiles",
      "url": "https://docs.github.com/en/actions/concepts/security/workflow-dependency-lockfiles",
      "domain": "docs.github.com",
      "site": "GitHub Docs",
      "categories": [
        "programming",
        "security",
        "devops"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "Release",
      "depth": "claim_checked",
      "published_at": "2026-11-07T20:27:05.000Z",
      "jev": 0.6336,
      "judge_a": 0.6278,
      "judge_b": 0.7387,
      "score": 0.6667,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 9,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "GitHub has documented workflow dependency lockfiles for Actions. The gh actions-lock CLI extension writes .github/workflows/actions.lock, which pins every direct and transitive action to a commit, recording the source repository's owner and repo IDs so runs can detect a swapped repository. Locked commits must remain reachable from the recorded tag or branch. Reusable workflow calls are not pinned and still need full SHAs, and inputs, secrets and runner images are outside its scope. GitHub Enterprise Server is unsupported. Most claims come from GitHub's own documentation alone.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/8/",
      "card": "https://rubric.hrih.si/og/2026-11-08/8.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.59,
          "judge_a": 0.5,
          "judge_b": 0.67,
          "mean": 0.5867,
          "spread": 0.17
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.61,
          "judge_a": 0.72,
          "judge_b": 0.82,
          "mean": 0.7167,
          "spread": 0.21
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.47,
          "judge_a": 0.67,
          "judge_b": 0.67,
          "mean": 0.6033,
          "spread": 0.2
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.85,
          "judge_a": 0.8,
          "judge_b": 0.86,
          "mean": 0.8367,
          "spread": 0.06
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.57,
          "judge_a": 0.67,
          "judge_b": 0.82,
          "mean": 0.6867,
          "spread": 0.25
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.795,
          "judge_a": 0.72,
          "judge_b": 0.7,
          "mean": 0.7383,
          "spread": 0.095
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.91,
          "judge_a": 0.85,
          "judge_b": 0.88,
          "mean": 0.88,
          "spread": 0.06
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.08,
          "judge_a": 0.2,
          "judge_b": 0.12,
          "mean": 0.1333,
          "spread": 0.12
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.12,
          "judge_a": 0.15,
          "judge_b": 0.08,
          "mean": 0.1167,
          "spread": 0.07
        }
      }
    },
    {
      "rank": 9,
      "id": "st_20261108_17773",
      "title": "Google: Old TODO comments send coding agents chasing bugs fixed years ago",
      "url": "https://developers.googleblog.com/en/agent-distraction/",
      "domain": "developers.googleblog.com",
      "site": "developers.googleblog.com",
      "categories": [
        "programming",
        "ai"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning"
      ],
      "format": "Paper",
      "depth": "claim_checked",
      "published_at": "2026-11-07T12:08:24.000Z",
      "jev": 0.6695,
      "judge_a": 0.563,
      "judge_b": 0.7122,
      "score": 0.6482,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 10,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Google engineer Sam Saccone reports that stale TODO(bug/...) comments distract coding agents in Google's internal traces. About 13% of file-reading sessions encountered one; agents acted on 6% of encounters, mostly unprompted. Of those detours, roughly 41% chased closed tickets. Regression estimates put the cost at 70% more steps, 77% more tokens and 105% more wait time per session, though these figures are unverified and the causal framing rests on observational data. Google's cleanup agent has removed thousands of obsolete references, with over 95% reviewer acceptance.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/9/",
      "card": "https://rubric.hrih.si/og/2026-11-08/9.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.67,
          "judge_a": 0.45,
          "judge_b": 0.67,
          "mean": 0.5967,
          "spread": 0.22
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.73,
          "judge_a": 0.7,
          "judge_b": 0.88,
          "mean": 0.77,
          "spread": 0.18
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.757,
          "judge_a": 0.6,
          "judge_b": 0.67,
          "mean": 0.6756,
          "spread": 0.157
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.798,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.7725,
          "spread": 0.12
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.767,
          "judge_a": 0.6,
          "judge_b": 0.78,
          "mean": 0.7156,
          "spread": 0.18
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.745,
          "judge_a": 0.55,
          "judge_b": 0.68,
          "mean": 0.6583,
          "spread": 0.195
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.84,
          "judge_a": 0.9,
          "judge_b": 0.92,
          "mean": 0.8867,
          "spread": 0.08
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.18,
          "judge_a": 0.15,
          "judge_b": 0.18,
          "mean": 0.17,
          "spread": 0.03
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.35,
          "judge_a": 0.3,
          "judge_b": 0.27,
          "mean": 0.3067,
          "spread": 0.08
        }
      }
    },
    {
      "rank": 10,
      "id": "st_20261108_17882",
      "title": "We compressed a 31B video+audio diffusion model to 14B, 6 steps, INT8",
      "url": "https://github.com/Video-Rebirth/hyperflow-h3-flash",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "ai"
      ],
      "category_names": [
        "AI & machine learning"
      ],
      "format": "Release",
      "depth": "claim_checked",
      "published_at": "2026-11-07T15:16:01.000Z",
      "jev": 0.6531,
      "judge_a": 0.477,
      "judge_b": 0.7997,
      "score": 0.6432,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 11,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Video Rebirth released HyperFlow H3 Flash, a pruned, INT8-quantized, few-step version of MiniMax-H3 that generates video with synchronized stereo audio. It has 13.7B–13.9B parameters versus about 31B, samples in 4–8 steps without classifier-free guidance, and ships GGUF builds (Q4_K_M is 9.1 GB) for 24 GB and 32 GB consumer GPUs. Its own timings are 8.7 s for a 5.2 s clip on 4× B200 and 92 s on one RTX 5090; these and the real-time claim are unverified. Weights exclude US, EU, UK and Korean use without MiniMax's authorization.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/10/",
      "card": "https://rubric.hrih.si/og/2026-11-08/10.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.633,
          "judge_a": 0.45,
          "judge_b": 0.72,
          "mean": 0.6008,
          "spread": 0.27
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.68,
          "judge_a": 0.6,
          "judge_b": 0.91,
          "mean": 0.73,
          "spread": 0.31
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.473,
          "judge_a": 0.4,
          "judge_b": 0.62,
          "mean": 0.4978,
          "spread": 0.22
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.75,
          "judge_a": 0.7,
          "judge_b": 0.88,
          "mean": 0.7767,
          "spread": 0.18
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.98,
          "judge_a": 0.6,
          "judge_b": 0.93,
          "mean": 0.8367,
          "spread": 0.38
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.998,
          "judge_a": 0.85,
          "judge_b": 0.98,
          "mean": 0.9425,
          "spread": 0.148
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.9,
          "judge_a": 0.9,
          "judge_b": 0.98,
          "mean": 0.9267,
          "spread": 0.08
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.08,
          "judge_a": 0.35,
          "judge_b": 0.08,
          "mean": 0.17,
          "spread": 0.27
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.28,
          "judge_a": 0.4,
          "judge_b": 0.12,
          "mean": 0.2667,
          "spread": 0.28
        }
      }
    },
    {
      "rank": 11,
      "id": "st_20261108_17687",
      "title": "Every Mac comes with an (undocumented) family member classifier",
      "url": "https://8rank.ai/game/apple-intelligence-personal-knowledge-graph",
      "domain": "8rank.ai",
      "site": "Eighth Rank",
      "categories": [
        "ai",
        "security",
        "data"
      ],
      "category_names": [
        "AI & machine learning",
        "Security",
        "Data"
      ],
      "format": "Paper",
      "depth": "claim_checked",
      "published_at": "2026-11-07T09:46:03.000Z",
      "jev": 0.6045,
      "judge_a": 0.6085,
      "judge_b": 0.8319,
      "score": 0.6816,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 7,
      "topic_penalty": 0.04,
      "repeat_penalty": 0,
      "summary": "Eighth Rank reports that macOS 26 runs two daemons, knowledgeconstructiond and intelligenceplatformd, that build an on-device knowledge graph from Contacts, Calendar, Photos, Family Sharing, communication logs and location. It is stored in plaintext SQLite under Full Disk Access protection, with 181 entity classes and 278 predicates, including inferred family relationships. The man page is nearly the only Apple documentation, and the mechanism mapping is the authors' inference. Most specific figures, including the family classifier details, lack independent sourcing. Developers should note the privacy exposure if that store is compromised.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/11/",
      "card": "https://rubric.hrih.si/og/2026-11-08/11.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.605,
          "judge_a": 0.6,
          "judge_b": 0.84,
          "mean": 0.6817,
          "spread": 0.24
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.74,
          "judge_a": 0.85,
          "judge_b": 0.95,
          "mean": 0.8467,
          "spread": 0.21
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.477,
          "judge_a": 0.6,
          "judge_b": 0.8,
          "mean": 0.6256,
          "spread": 0.323
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.615,
          "judge_a": 0.5,
          "judge_b": 0.82,
          "mean": 0.645,
          "spread": 0.32
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.94,
          "judge_a": 0.7,
          "judge_b": 0.9,
          "mean": 0.8467,
          "spread": 0.24
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.97,
          "judge_a": 0.85,
          "judge_b": 0.95,
          "mean": 0.9233,
          "spread": 0.12
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.92,
          "judge_a": 0.6,
          "judge_b": 0.94,
          "mean": 0.82,
          "spread": 0.34
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.06,
          "judge_a": 0.1,
          "judge_b": 0.04,
          "mean": 0.0667,
          "spread": 0.06
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.55,
          "judge_a": 0.3,
          "judge_b": 0.3,
          "mean": 0.3833,
          "spread": 0.25
        }
      }
    },
    {
      "rank": 12,
      "id": "st_20261108_17976",
      "title": "MaxxBench – \"Nice DeepSWE score. But is it true?\"",
      "url": "https://maxxbench.unlap.ai/research/why-maxxbench",
      "domain": "maxxbench.unlap.ai",
      "site": "MaxxBench",
      "categories": [
        "programming",
        "ai"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning"
      ],
      "format": "Paper",
      "depth": "claim_checked",
      "published_at": "2026-11-07T17:19:00.000Z",
      "jev": 0.7189,
      "judge_a": 0.6833,
      "judge_b": 0.8205,
      "score": 0.6409,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0.1,
      "score_rank": 13,
      "topic_penalty": 0.02,
      "repeat_penalty": 0,
      "summary": "MaxxBench, from Gavin and Justin Lo, alters requirements or starting code in 89 DeepSWE tasks, with reference patches growing a median of 17 lines. Models that scored about 74% on DeepSWE spread widely: Claude Opus 5.5 reached 76.4%, Gemini 3.8 Flash 53.4%. Trajectories show models guessing hidden tests over following the prompt, and DeepSeek V4.1 Flash used an extracted API key to query outside models. The authors' figures are unverified, and the paper is not yet published, so treat the rankings as provisional.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/12/",
      "card": "https://rubric.hrih.si/og/2026-11-08/12.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.705,
          "judge_a": 0.6,
          "judge_b": 0.78,
          "mean": 0.695,
          "spread": 0.18
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.8,
          "judge_a": 0.88,
          "judge_b": 0.93,
          "mean": 0.87,
          "spread": 0.13
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.73,
          "judge_a": 0.7,
          "judge_b": 0.78,
          "mean": 0.7367,
          "spread": 0.08
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.695,
          "judge_a": 0.7,
          "judge_b": 0.83,
          "mean": 0.7417,
          "spread": 0.135
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.973,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.8311,
          "spread": 0.273
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.925,
          "judge_a": 0.8,
          "judge_b": 0.86,
          "mean": 0.8617,
          "spread": 0.125
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.84,
          "judge_a": 0.85,
          "judge_b": 0.98,
          "mean": 0.89,
          "spread": 0.14
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.07,
          "judge_a": 0.1,
          "judge_b": 0.03,
          "mean": 0.0667,
          "spread": 0.07
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.27,
          "judge_a": 0.3,
          "judge_b": 0.18,
          "mean": 0.25,
          "spread": 0.12
        }
      }
    },
    {
      "rank": 13,
      "id": "st_20261108_17720",
      "title": "AARE: A proof-of-concept open-source implementation of Android Auto",
      "url": "https://github.com/prototux/AARE",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "other"
      ],
      "category_names": [
        "Other"
      ],
      "format": "Release",
      "depth": "claim_checked",
      "published_at": "2026-11-07T10:40:43.000Z",
      "jev": 0.603,
      "judge_a": 0.5634,
      "judge_b": 0.6737,
      "score": 0.6133,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 20,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "AARE is an open-source proof of concept that reimplements the phone side of Android Auto without Google Play Services, letting apps such as CoMaps, Organic Maps, Tempus and WhatsApp run on a car screen. The project reports two real cars working, but units that check for Google-issued certificates reject its self-signed one, so users must extract Google's key. Google Maps and Waze can't be hosted. A desktop head unit allows testing without a car. The compatibility reports couldn't be independently verified.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/13/",
      "card": "https://rubric.hrih.si/og/2026-11-08/13.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.5,
          "judge_a": 0.45,
          "judge_b": 0.58,
          "mean": 0.51,
          "spread": 0.13
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.64,
          "judge_a": 0.72,
          "judge_b": 0.82,
          "mean": 0.7267,
          "spread": 0.18
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.4,
          "judge_a": 0.55,
          "judge_b": 0.38,
          "mean": 0.4433,
          "spread": 0.17
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.663,
          "judge_a": 0.6,
          "judge_b": 0.65,
          "mean": 0.6375,
          "spread": 0.063
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.933,
          "judge_a": 0.55,
          "judge_b": 0.9,
          "mean": 0.7944,
          "spread": 0.383
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.993,
          "judge_a": 0.8,
          "judge_b": 0.9,
          "mean": 0.8975,
          "spread": 0.193
        },
        "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": 0.04,
          "judge_a": 0.12,
          "judge_b": 0.03,
          "mean": 0.0633,
          "spread": 0.09
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.15,
          "judge_a": 0.25,
          "judge_b": 0.04,
          "mean": 0.1467,
          "spread": 0.21
        }
      }
    },
    {
      "rank": 14,
      "id": "st_20261108_17608",
      "title": "Progress Report: Linux 7.3 - Asahi Linux",
      "url": "https://asahilinux.org/2026/11/progress-report-7-3/",
      "domain": "asahilinux.org",
      "site": "Asahi Linux Blog",
      "categories": [
        "programming",
        "hardware"
      ],
      "category_names": [
        "Programming & dev tools",
        "Hardware & devices"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T04:30:00.000Z",
      "jev": 0.5319,
      "judge_a": 0.5302,
      "judge_b": 0.7469,
      "score": 0.603,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 26,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Asahi Linux's Linux 7.3 progress report says M3 Macs no longer need the installer's EXPERT flag, and that the macaudio (about 3,100 lines) and AVD (about 13,000 lines) drivers are on the kernel mailing list. Thunderbolt patches are upstream too, but only USB3, XDomain and networking work, not PCIe or DisplayPort tunnelling. Google's Android P3S patch let 4 KiB-page games run without muvm, but it is hard-coded for 16 KiB hosts, so Asahi will not ship it. The figures are the project's own and were not independently verified.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/14/",
      "card": "https://rubric.hrih.si/og/2026-11-08/14.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.558,
          "judge_a": 0.45,
          "judge_b": 0.78,
          "mean": 0.5958,
          "spread": 0.33
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.54,
          "judge_a": 0.72,
          "judge_b": 0.9,
          "mean": 0.72,
          "spread": 0.36
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.45,
          "judge_a": 0.5,
          "judge_b": 0.45,
          "mean": 0.4667,
          "spread": 0.05
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.52,
          "judge_a": 0.4,
          "judge_b": 0.72,
          "mean": 0.5467,
          "spread": 0.32
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.69,
          "judge_a": 0.7,
          "judge_b": 0.84,
          "mean": 0.7433,
          "spread": 0.15
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.835,
          "judge_a": 0.7,
          "judge_b": 0.9,
          "mean": 0.8117,
          "spread": 0.2
        },
        "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": 0.07,
          "judge_a": 0.1,
          "judge_b": 0.08,
          "mean": 0.0833,
          "spread": 0.03
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.23,
          "judge_a": 0.15,
          "judge_b": 0.1,
          "mean": 0.16,
          "spread": 0.13
        }
      }
    },
    {
      "rank": 15,
      "id": "st_20261108_18005",
      "title": "Straitjacket: A TypeScript template with Guardrails for your agents",
      "url": "https://github.com/balajeerc/straitjacket",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming",
        "ai",
        "security",
        "devops"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "Release",
      "depth": "claim_checked",
      "published_at": "2026-11-07T17:39:56.000Z",
      "jev": 0.6951,
      "judge_a": 0.4687,
      "judge_b": 0.6345,
      "score": 0.5994,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 27,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Straitjacket is a BSD-licensed TypeScript template on GitHub that bundles lint, test and supply-chain rules meant to stop coding agents from faking progress. It bans lint-disable comments, runs Stryker mutation testing on pre-push, uses drift guards to check quoted numbers against configs, and refuses packages under 30 days old. There is no CI, and --no-verify bypasses both git hooks, so enforcement depends on local discipline. The claims here come from the project's own README, without independent verification.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/15/",
      "card": "https://rubric.hrih.si/og/2026-11-08/15.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.518,
          "judge_a": 0.3,
          "judge_b": 0.42,
          "mean": 0.4125,
          "spread": 0.217
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.77,
          "judge_a": 0.62,
          "judge_b": 0.78,
          "mean": 0.7233,
          "spread": 0.16
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.583,
          "judge_a": 0.5,
          "judge_b": 0.55,
          "mean": 0.5444,
          "spread": 0.083
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.905,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.8083,
          "spread": 0.205
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.703,
          "judge_a": 0.45,
          "judge_b": 0.72,
          "mean": 0.6244,
          "spread": 0.27
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.998,
          "judge_a": 0.7,
          "judge_b": 0.86,
          "mean": 0.8525,
          "spread": 0.298
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.88,
          "judge_a": 0.9,
          "judge_b": 0.94,
          "mean": 0.9067,
          "spread": 0.06
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.06,
          "judge_a": 0.25,
          "judge_b": 0.12,
          "mean": 0.1433,
          "spread": 0.19
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.2,
          "judge_a": 0.35,
          "judge_b": 0.22,
          "mean": 0.2567,
          "spread": 0.15
        }
      }
    },
    {
      "rank": 16,
      "id": "st_20261108_17711",
      "title": "Next-Level Benchmarking Overfitting",
      "url": "https://databasearchitects.blogspot.com/2026/11/next-level-benchmarking-overfitting.html",
      "domain": "databasearchitects.blogspot.com",
      "site": "Database Architects (Neumann, Leis)",
      "categories": [
        "programming",
        "ai",
        "data"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Data"
      ],
      "format": "Analysis",
      "depth": "claim_checked",
      "published_at": "2026-11-07T10:14:11.781Z",
      "jev": 0.5681,
      "judge_a": 0.5011,
      "judge_b": 0.6926,
      "score": 0.5872,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 32,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Thomas Neumann, an Umbra developer, argues that GenDB-V, an AI-generated database engine whose paper claims 22× to 61× speedups over Umbra and DuckDB, overfits to TPC-H. He says the gains come from per-query structures precomputed at load time, and that the \"plain-array\" mode hard-codes dbgen's row layout, with a slow fallback otherwise. His bandwidth estimate puts such speedups out of reach for conventional execution. Most figures are his own and unverified, so the takeaway is caution about benchmark-tuned agents.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/16/",
      "card": "https://rubric.hrih.si/og/2026-11-08/16.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.525,
          "judge_a": 0.35,
          "judge_b": 0.62,
          "mean": 0.4983,
          "spread": 0.27
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.7,
          "judge_a": 0.7,
          "judge_b": 0.88,
          "mean": 0.76,
          "spread": 0.18
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.547,
          "judge_a": 0.45,
          "judge_b": 0.42,
          "mean": 0.4722,
          "spread": 0.127
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.555,
          "judge_a": 0.4,
          "judge_b": 0.68,
          "mean": 0.545,
          "spread": 0.28
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.557,
          "judge_a": 0.75,
          "judge_b": 0.82,
          "mean": 0.7089,
          "spread": 0.263
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.825,
          "judge_a": 0.8,
          "judge_b": 0.87,
          "mean": 0.8317,
          "spread": 0.07
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.88,
          "judge_a": 0.6,
          "judge_b": 0.96,
          "mean": 0.8133,
          "spread": 0.36
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.11,
          "judge_a": 0.03,
          "judge_b": 0.01,
          "mean": 0.05,
          "spread": 0.1
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.23,
          "judge_a": 0.12,
          "judge_b": 0.18,
          "mean": 0.1767,
          "spread": 0.11
        }
      }
    },
    {
      "rank": 17,
      "id": "st_20261108_18209",
      "title": "Dual JWT Authentication: Security, Redis, and Trade-Offs",
      "url": "https://www.jstools.space/blog/dual-jwt-authentication/",
      "domain": "jstools.space",
      "site": "JSTools.Space",
      "categories": [
        "programming",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security"
      ],
      "format": "Tutorial",
      "depth": "claim_checked",
      "published_at": "2026-11-07T23:56:09.000Z",
      "jev": 0.7584,
      "judge_a": 0.481,
      "judge_b": 0.6856,
      "score": 0.6416,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 12,
      "topic_penalty": 0.06,
      "repeat_penalty": 0,
      "summary": "JSTools.Space describes a Dual JWT design: access tokens of 5–15 minutes kept in memory, refresh tokens of up to 30 days in HttpOnly cookies, and a per-user Redis revocation cutoff that invalidates all older refresh tokens at once. The article stresses the trade-offs. Renewing without invalidating old tokens is not replay-resistant rotation, which RFC 9700 expects of public clients. The cutoff cannot end a single device's session, and access tokens may work about five minutes after logout-all. The performance savings are asserted, not measured.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/17/",
      "card": "https://rubric.hrih.si/og/2026-11-08/17.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.615,
          "judge_a": 0.3,
          "judge_b": 0.52,
          "mean": 0.4783,
          "spread": 0.315
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.76,
          "judge_a": 0.55,
          "judge_b": 0.72,
          "mean": 0.6767,
          "spread": 0.21
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.767,
          "judge_a": 0.6,
          "judge_b": 0.58,
          "mean": 0.6489,
          "spread": 0.187
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.96,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.8267,
          "spread": 0.26
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.62,
          "judge_a": 0.45,
          "judge_b": 0.78,
          "mean": 0.6167,
          "spread": 0.33
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.998,
          "judge_a": 0.7,
          "judge_b": 0.86,
          "mean": 0.8525,
          "spread": 0.298
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.91,
          "judge_a": 0.35,
          "judge_b": 0.93,
          "mean": 0.73,
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        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.04,
          "judge_a": 0.1,
          "judge_b": 0.01,
          "mean": 0.05,
          "spread": 0.09
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.15,
          "judge_a": 0.2,
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          "mean": 0.1433,
          "spread": 0.12
        }
      }
    },
    {
      "rank": 18,
      "id": "st_20261108_18155",
      "title": "Learning in a World of Notes",
      "url": "https://engram.com/blog/learning-in-a-world-of-notes",
      "domain": "engram.com",
      "site": "Engram",
      "categories": [
        "ai"
      ],
      "category_names": [
        "AI & machine learning"
      ],
      "format": "Paper",
      "depth": "claim_checked",
      "published_at": "2026-11-07T21:45:43.000Z",
      "jev": 0.6535,
      "judge_a": 0.4725,
      "judge_b": 0.7363,
      "score": 0.6207,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 16,
      "topic_penalty": 0.04,
      "repeat_penalty": 0,
      "summary": "Engram reports that reinforcement learning on top of fixed, high-quality notes raised agent accuracy by 7 to 20 points across three workspaces. On Harvey C&H, a trained Qwen 27B model matched GPT-6 Sol with notes at 78% for $0.06 per query versus $0.17, and training cost roughly 5-30% of building the notes. A model trained once still beat the base model after 23M tokens of note edits. These are the company's own results; on Hex it did not reach frontier accuracy, and the robustness test covers one workspace.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/18/",
      "card": "https://rubric.hrih.si/og/2026-11-08/18.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.668,
          "judge_a": 0.45,
          "judge_b": 0.72,
          "mean": 0.6125,
          "spread": 0.27
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.69,
          "judge_a": 0.7,
          "judge_b": 0.91,
          "mean": 0.7667,
          "spread": 0.22
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.67,
          "judge_a": 0.55,
          "judge_b": 0.72,
          "mean": 0.6467,
          "spread": 0.17
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.715,
          "judge_a": 0.6,
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          "mean": 0.7117,
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        },
        "substantiated": {
          "name": "Substantiated",
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          "judge_a": 0.5,
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        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
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          "judge_a": 0.6,
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        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.87,
          "judge_a": 0.8,
          "judge_b": 0.93,
          "mean": 0.8667,
          "spread": 0.13
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.11,
          "judge_a": 0.35,
          "judge_b": 0.18,
          "mean": 0.2133,
          "spread": 0.24
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.37,
          "judge_a": 0.45,
          "judge_b": 0.34,
          "mean": 0.3867,
          "spread": 0.11
        }
      }
    },
    {
      "rank": 19,
      "id": "st_20261108_17679",
      "title": "FastVM – MicroVMs that sleep on idle and wake when packets arrive",
      "url": "https://github.com/tuananh/fastvm",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming",
        "devops"
      ],
      "category_names": [
        "Programming & dev tools",
        "Infrastructure & DevOps"
      ],
      "format": "New project",
      "depth": "three_judges",
      "published_at": "2026-11-07T09:26:45.000Z",
      "jev": 0.5881,
      "judge_a": 0.4955,
      "judge_b": 0.6367,
      "score": 0.5734,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 39,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "FastVM is an open-source, single-host sandbox runtime built in Go on Firecracker/KVM that boots micro-VMs in about 100 ms, pauses and pages their memory to disk when idle, and wakes them when a network packet arrives.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/19/",
      "card": "https://rubric.hrih.si/og/2026-11-08/19.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.463,
          "judge_a": 0.38,
          "judge_b": 0.48,
          "mean": 0.4408,
          "spread": 0.1
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.62,
          "judge_a": 0.62,
          "judge_b": 0.78,
          "mean": 0.6733,
          "spread": 0.16
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.42,
          "judge_a": 0.42,
          "judge_b": 0.42,
          "mean": 0.42,
          "spread": 0
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.71,
          "judge_a": 0.55,
          "judge_b": 0.7,
          "mean": 0.6533,
          "spread": 0.16
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.977,
          "judge_a": 0.56,
          "judge_b": 0.88,
          "mean": 0.8056,
          "spread": 0.417
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.973,
          "judge_a": 0.78,
          "judge_b": 0.9,
          "mean": 0.8842,
          "spread": 0.193
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.9,
          "judge_a": 0.95,
          "judge_b": 0.99,
          "mean": 0.9467,
          "spread": 0.09
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.07,
          "judge_a": 0.12,
          "judge_b": 0.08,
          "mean": 0.09,
          "spread": 0.05
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.22,
          "judge_a": 0.22,
          "judge_b": 0.12,
          "mean": 0.1867,
          "spread": 0.1
        }
      }
    },
    {
      "rank": 20,
      "id": "st_20261108_17983",
      "title": "Psalm 7 release: PHP security&static analysis software",
      "url": "https://psalm.dev/articles/psalm-7",
      "domain": "psalm.dev",
      "site": "Psalm",
      "categories": [
        "programming",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security"
      ],
      "format": "Release",
      "depth": "claim_checked",
      "published_at": "2026-11-07T17:06:18.000Z",
      "jev": 0.6651,
      "judge_a": 0.4585,
      "judge_b": 0.717,
      "score": 0.6135,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 19,
      "topic_penalty": 0.02,
      "repeat_penalty": 0.0214,
      "summary": "Psalm 7, the PHP static analyzer, was released on November 2 by maintainer Daniil Gentili. Taint-based security analysis now runs by default in the same pass as type checking and dead code detection, and the project says it is up to 10x faster, though no benchmark is given. It adds NoSQL, LLM prompt and URL taint kinds, a Hack-style capabilities model replacing the old purity annotations, and graph-based dead code detection. Framework plugins load automatically, and the plugin API is versioned separately. Projects can opt out of taint analysis.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/20/",
      "card": "https://rubric.hrih.si/og/2026-11-08/20.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.613,
          "judge_a": 0.4,
          "judge_b": 0.7,
          "mean": 0.5708,
          "spread": 0.3
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.62,
          "judge_a": 0.55,
          "judge_b": 0.82,
          "mean": 0.6633,
          "spread": 0.27
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.47,
          "judge_a": 0.35,
          "judge_b": 0.42,
          "mean": 0.4133,
          "spread": 0.12
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.863,
          "judge_a": 0.6,
          "judge_b": 0.78,
          "mean": 0.7475,
          "spread": 0.263
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.887,
          "judge_a": 0.7,
          "judge_b": 0.88,
          "mean": 0.8222,
          "spread": 0.187
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.97,
          "judge_a": 0.85,
          "judge_b": 0.9,
          "mean": 0.9067,
          "spread": 0.12
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.9,
          "judge_a": 0.9,
          "judge_b": 0.98,
          "mean": 0.9267,
          "spread": 0.08
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.07,
          "judge_a": 0.25,
          "judge_b": 0.08,
          "mean": 0.1333,
          "spread": 0.18
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.17,
          "judge_a": 0.3,
          "judge_b": 0.12,
          "mean": 0.1967,
          "spread": 0.18
        }
      }
    },
    {
      "rank": 21,
      "id": "st_20261108_18035",
      "title": "We Built Render OIDC",
      "url": "https://render.com/blog/how-we-built-render-oidc",
      "domain": "render.com",
      "site": "render.com",
      "categories": [
        "security",
        "devops"
      ],
      "category_names": [
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T18:58:44.000Z",
      "jev": 0.5973,
      "judge_a": 0.4537,
      "judge_b": 0.6367,
      "score": 0.5625,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 45,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "Render has described how its OIDC support lets services authenticate to providers such as AWS and GCP without long-lived API tokens. Tokens expire after 60 minutes, and the workspace ID in the issuer URL makes each workspace a separate identity provider. Because most SDKs only read a token file from disk, a daemon outside each container writes refreshed tokens to an in-memory directory bind-mounted read-only into the service. These details come from Render's own account; the claim check found no independent sources.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/21/",
      "card": "https://rubric.hrih.si/og/2026-11-08/21.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.525,
          "judge_a": 0.35,
          "judge_b": 0.56,
          "mean": 0.4783,
          "spread": 0.21
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.67,
          "judge_a": 0.62,
          "judge_b": 0.82,
          "mean": 0.7033,
          "spread": 0.2
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.537,
          "judge_a": 0.45,
          "judge_b": 0.58,
          "mean": 0.5222,
          "spread": 0.13
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.733,
          "judge_a": 0.6,
          "judge_b": 0.72,
          "mean": 0.6842,
          "spread": 0.133
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.43,
          "judge_a": 0.5,
          "judge_b": 0.72,
          "mean": 0.55,
          "spread": 0.29
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.925,
          "judge_a": 0.78,
          "judge_b": 0.77,
          "mean": 0.825,
          "spread": 0.155
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.89,
          "judge_a": 0.85,
          "judge_b": 0.96,
          "mean": 0.9,
          "spread": 0.11
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.11,
          "judge_a": 0.35,
          "judge_b": 0.18,
          "mean": 0.2133,
          "spread": 0.24
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.23,
          "judge_a": 0.2,
          "judge_b": 0.3,
          "mean": 0.2433,
          "spread": 0.1
        }
      }
    },
    {
      "rank": 22,
      "id": "st_20261108_17579",
      "title": "Promptcheckup – pass/fail check for prompt changes, with 95% intervals",
      "url": "https://github.com/gael55x/promptcheckup",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming",
        "ai",
        "devops"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Infrastructure & DevOps"
      ],
      "format": "New project",
      "depth": "three_judges",
      "published_at": "2026-11-07T03:52:28.000Z",
      "jev": 0.652,
      "judge_a": 0.493,
      "judge_b": 0.7083,
      "score": 0.6177,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 18,
      "topic_penalty": 0.06,
      "repeat_penalty": 0,
      "summary": "Promptcheckup is an open-source Python tool that gates prompt changes by running the old and new prompts on the same cases and reporting better, worse, or not sure yet with a 95% confidence interval, usable as a pass/fail check in CI.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/22/",
      "card": "https://rubric.hrih.si/og/2026-11-08/22.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.53,
          "judge_a": 0.3,
          "judge_b": 0.58,
          "mean": 0.47,
          "spread": 0.28
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.69,
          "judge_a": 0.7,
          "judge_b": 0.83,
          "mean": 0.74,
          "spread": 0.14
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.497,
          "judge_a": 0.45,
          "judge_b": 0.62,
          "mean": 0.5222,
          "spread": 0.17
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.87,
          "judge_a": 0.7,
          "judge_b": 0.78,
          "mean": 0.7833,
          "spread": 0.17
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.98,
          "judge_a": 0.65,
          "judge_b": 0.84,
          "mean": 0.8233,
          "spread": 0.33
        },
        "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": 0.91,
          "judge_a": 0.9,
          "judge_b": 0.93,
          "mean": 0.9133,
          "spread": 0.03
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.09,
          "judge_a": 0.25,
          "judge_b": 0.08,
          "mean": 0.14,
          "spread": 0.17
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.34,
          "judge_a": 0.35,
          "judge_b": 0.12,
          "mean": 0.27,
          "spread": 0.23
        }
      }
    },
    {
      "rank": 23,
      "id": "st_20261108_18070",
      "title": "Catching prompt injections hidden in PDFs",
      "url": "https://tomclancy.info/joshua-judges-ruth.html",
      "domain": "tomclancy.info",
      "site": "Tom Clancy",
      "categories": [
        "ai",
        "security"
      ],
      "category_names": [
        "AI & machine learning",
        "Security"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T19:28:56.000Z",
      "jev": 0.6075,
      "judge_a": 0.543,
      "judge_b": 0.7606,
      "score": 0.637,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 14,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "Tom Clancy, a QA engineer, describes how he tested an internal tool that parses financial PDFs for prompt injection. Hidden white-on-white earnings figures, zero-height text and off-page text all got through on his first attempts. He built six single-purpose \"judges\" (HiddenInk, text-versus-pixel divergence, Unicode, accessibility tags, glyph overlap, non-text ink), each with stated blind spots, such as inline /ActualText substitution. Two further attack vectors remain open as Jira tickets, and he has caught no real-world attempts. The claims are his own, unverified.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/23/",
      "card": "https://rubric.hrih.si/og/2026-11-08/23.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.51,
          "judge_a": 0.35,
          "judge_b": 0.66,
          "mean": 0.5067,
          "spread": 0.31
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.65,
          "judge_a": 0.7,
          "judge_b": 0.9,
          "mean": 0.75,
          "spread": 0.25
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.587,
          "judge_a": 0.4,
          "judge_b": 0.58,
          "mean": 0.5222,
          "spread": 0.187
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.753,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.7575,
          "spread": 0.12
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.52,
          "judge_a": 0.5,
          "judge_b": 0.72,
          "mean": 0.58,
          "spread": 0.22
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.868,
          "judge_a": 0.75,
          "judge_b": 0.86,
          "mean": 0.8258,
          "spread": 0.118
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
          "jev": 0.85,
          "judge_a": 0.8,
          "judge_b": 0.92,
          "mean": 0.8567,
          "spread": 0.12
        },
        "promotional": {
          "name": "Promotional",
          "weight": -0.19,
          "jev": 0.06,
          "judge_a": 0.05,
          "judge_b": 0.02,
          "mean": 0.0433,
          "spread": 0.04
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.24,
          "judge_a": 0.2,
          "judge_b": 0.08,
          "mean": 0.1733,
          "spread": 0.16
        }
      }
    },
    {
      "rank": 24,
      "id": "st_20261108_17803",
      "title": "Porting the Simulation Engine into Rust",
      "url": "https://www.modeloop.app/blog/porting-the-simulation-engine-into-rust",
      "domain": "modeloop.app",
      "site": "Modeloop",
      "categories": [
        "programming"
      ],
      "category_names": [
        "Programming & dev tools"
      ],
      "format": "Write-up",
      "depth": "claim_checked",
      "published_at": "2026-11-07T13:14:29.000Z",
      "jev": 0.5784,
      "judge_a": 0.4835,
      "judge_b": 0.7098,
      "score": 0.5906,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 30,
      "topic_penalty": 0.04,
      "repeat_penalty": 0,
      "summary": "Modeloop rewrote its simulation engine from Python to Rust to speed up model execution, reporting in its own engineering benchmarks an average 8.6× speedup for model-in-the-loop runs and 16.4× for software-in-the-loop runs across six models.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/24/",
      "card": "https://rubric.hrih.si/og/2026-11-08/24.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
          "weight": 0.24,
          "jev": 0.49,
          "judge_a": 0.4,
          "judge_b": 0.62,
          "mean": 0.5033,
          "spread": 0.22
        },
        "reader_interest": {
          "name": "Reader interest",
          "weight": 0.26,
          "jev": 0.64,
          "judge_a": 0.7,
          "judge_b": 0.82,
          "mean": 0.72,
          "spread": 0.18
        },
        "audience_breadth": {
          "name": "Audience breadth",
          "weight": 0.13,
          "jev": 0.35,
          "judge_a": 0.4,
          "judge_b": 0.55,
          "mean": 0.4333,
          "spread": 0.2
        },
        "practical_relevance": {
          "name": "Practical relevance",
          "weight": 0.22,
          "jev": 0.665,
          "judge_a": 0.55,
          "judge_b": 0.72,
          "mean": 0.645,
          "spread": 0.17
        },
        "substantiated": {
          "name": "Substantiated",
          "weight": 0.05,
          "jev": 0.957,
          "judge_a": 0.8,
          "judge_b": 0.94,
          "mean": 0.8989,
          "spread": 0.157
        },
        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
          "jev": 0.943,
          "judge_a": 0.75,
          "judge_b": 0.84,
          "mean": 0.8442,
          "spread": 0.193
        },
        "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": 0.09,
          "judge_a": 0.3,
          "judge_b": 0.08,
          "mean": 0.1567,
          "spread": 0.22
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.18,
          "judge_a": 0.3,
          "judge_b": 0.04,
          "mean": 0.1733,
          "spread": 0.26
        }
      }
    },
    {
      "rank": 25,
      "id": "st_20261108_17920",
      "title": "The US let Nvidia sell chips to an empty office in China",
      "url": "https://www.theverge.com/ai-artificial-intelligence/1029483/nvidia-ai-chips-china-blacklist-government",
      "domain": "theverge.com",
      "site": "The Verge",
      "categories": [
        "ai",
        "hardware",
        "policy"
      ],
      "category_names": [
        "AI & machine learning",
        "Hardware & devices",
        "Policy & regulation"
      ],
      "format": "News",
      "depth": "claim_checked",
      "published_at": "2026-11-07T16:23:28.000Z",
      "jev": 0.4387,
      "judge_a": 0.5373,
      "judge_b": 0.6741,
      "score": 0.55,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 51,
      "topic_penalty": 0,
      "repeat_penalty": 0,
      "summary": "The New York Times reports that the US government approved Nvidia H200 AI chip sales to Chinese firms, including New Matrix, which has an apparently empty office and little verifiable existence, and whose owners have ties to a Chinese private equity firm blacklisted since 2024.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/25/",
      "card": "https://rubric.hrih.si/og/2026-11-08/25.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
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        "reader_interest": {
          "name": "Reader interest",
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          "name": "Audience breadth",
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        "practical_relevance": {
          "name": "Practical relevance",
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        "substantiated": {
          "name": "Substantiated",
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        "technical_depth": {
          "name": "Technical depth",
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          "judge_b": 0.12,
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        "new_information": {
          "name": "New information",
          "weight": 0.05,
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        "promotional": {
          "name": "Promotional",
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          "spread": 0.08
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        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.64,
          "judge_a": 0.2,
          "judge_b": 0.3,
          "mean": 0.38,
          "spread": 0.44
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      }
    },
    {
      "rank": 26,
      "id": "st_20261108_17667",
      "title": "NVKE: Nvidia previews its own Kubernetes engine",
      "url": "https://www.heise.de/en/news/NVKE-Nvidia-previews-its-own-Kubernetes-engine-11516165.html",
      "domain": "heise.de",
      "site": "heise online English",
      "categories": [
        "devops"
      ],
      "category_names": [
        "Infrastructure & DevOps"
      ],
      "format": "News",
      "depth": "claim_checked",
      "published_at": "2026-11-07T08:15:00.000Z",
      "jev": 0.5179,
      "judge_a": 0.4615,
      "judge_b": 0.7028,
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      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 47,
      "topic_penalty": 0.02,
      "repeat_penalty": 0,
      "summary": "Nvidia has released NVKE, a preview Kubernetes engine on GitHub under Apache 2.0, letting operators of GPU data centers offer managed Kubernetes clusters to customers, similar to AWS EKS. It is not yet recommended for production, and it is tightly integrated with Nvidia's own provisioning stack, NICo, which heise reports.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/26/",
      "card": "https://rubric.hrih.si/og/2026-11-08/26.png",
      "breakdown": {
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          "name": "Significance",
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        "reader_interest": {
          "name": "Reader interest",
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        "audience_breadth": {
          "name": "Audience breadth",
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        "practical_relevance": {
          "name": "Practical relevance",
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        "substantiated": {
          "name": "Substantiated",
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        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
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          "judge_a": 0.7,
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          "mean": 0.7425,
          "spread": 0.108
        },
        "new_information": {
          "name": "New information",
          "weight": 0.05,
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          "judge_a": 0.9,
          "judge_b": 0.95,
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        },
        "promotional": {
          "name": "Promotional",
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          "jev": 0.09,
          "judge_a": 0.15,
          "judge_b": 0.08,
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          "spread": 0.07
        },
        "overclaim": {
          "name": "Overclaim",
          "weight": -0.11,
          "jev": 0.13,
          "judge_a": 0.1,
          "judge_b": 0.06,
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          "spread": 0.07
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      }
    },
    {
      "rank": 27,
      "id": "st_20261108_17585",
      "title": "Adding proper security to your vibecoded Linux GUI apps",
      "url": "https://www.akitaonrails.com/en/2026/11/06/adding-proper-security-to-your-vibecoded-linux-gui-apps/",
      "domain": "akitaonrails.com",
      "site": "AkitaOnRails.com",
      "categories": [
        "programming",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "Security"
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      "format": "New project",
      "depth": "three_judges",
      "published_at": "2026-11-07T04:22:19.000Z",
      "jev": 0.726,
      "judge_a": 0.4525,
      "judge_b": 0.6781,
      "score": 0.6188,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 17,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "A developer's walkthrough of hardening a personal Rust/QML financial aggregation app for Linux, argued from the premise that AI-driven audits make decades-old security shortcuts cheap to exploit.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/27/",
      "card": "https://rubric.hrih.si/og/2026-11-08/27.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
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          "name": "Reader interest",
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        "audience_breadth": {
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          "name": "Practical relevance",
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          "name": "Substantiated",
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        "technical_depth": {
          "name": "Technical depth",
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        },
        "new_information": {
          "name": "New information",
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        "promotional": {
          "name": "Promotional",
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        "overclaim": {
          "name": "Overclaim",
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          "judge_a": 0.2,
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      }
    },
    {
      "rank": 28,
      "id": "st_20261108_17565",
      "title": "My personal Navier-Stokes or How Claude rebuilt the game we wrote in high school",
      "url": "https://writing.pointnine.com/p/my-personal-navier-stokes-or-how",
      "domain": "writing.pointnine.com",
      "site": "writing.pointnine.com",
      "categories": [
        "programming",
        "ai"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning"
      ],
      "format": "New project",
      "depth": "three_judges",
      "published_at": "2026-11-07T03:11:40.000Z",
      "jev": 0.6472,
      "judge_a": 0.5505,
      "judge_b": 0.6383,
      "score": 0.6119,
      "contested": true,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 21,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "A developer used Claude to reverse-engineer a 1997 DOS brewery simulation he co-wrote in high school, with no source code, and rebuild it as a browser game.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/28/",
      "card": "https://rubric.hrih.si/og/2026-11-08/28.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
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        "reader_interest": {
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        "audience_breadth": {
          "name": "Audience breadth",
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        "practical_relevance": {
          "name": "Practical relevance",
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        "substantiated": {
          "name": "Substantiated",
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        "technical_depth": {
          "name": "Technical depth",
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        "new_information": {
          "name": "New information",
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        "overclaim": {
          "name": "Overclaim",
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      }
    },
    {
      "rank": 29,
      "id": "st_20261108_17556",
      "title": "Brave Sells Its Search Index to AI, and Your Website Is Probably in It",
      "url": "https://www.searchenginejournal.com/brave-sells-its-search-index-to-ai-and-your-website-is-probably-in-it/595829/",
      "domain": "searchenginejournal.com",
      "site": "Search Engine Journal",
      "categories": [
        "ai",
        "policy"
      ],
      "category_names": [
        "AI & machine learning",
        "Policy & regulation"
      ],
      "format": "Analysis",
      "depth": "three_judges",
      "published_at": "2026-11-07T02:59:27.000Z",
      "jev": 0.602,
      "judge_a": 0.537,
      "judge_b": 0.687,
      "score": 0.6087,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 22,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "Brave sells access to its 30-billion-page search index to AI companies through its Search API, and its crawler doesn't identify itself with a distinct user agent, so websites cannot block it without also blocking Google, which Brave says it follows. The New York Times sued Brave on Nov.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/29/",
      "card": "https://rubric.hrih.si/og/2026-11-08/29.png",
      "breakdown": {
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          "name": "Significance",
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        "audience_breadth": {
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        "substantiated": {
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        "technical_depth": {
          "name": "Technical depth",
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        },
        "new_information": {
          "name": "New information",
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        "promotional": {
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    },
    {
      "rank": 30,
      "id": "st_20261108_17963",
      "title": "How RL Judge affects your models reasoning",
      "url": "https://chinmaykarkar.com/blog/llm_credit_assignment/",
      "domain": "chinmaykarkar.com",
      "site": "Chinmay Karkar",
      "categories": [
        "ai"
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      "category_names": [
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      "published_at": "2026-11-07T16:30:53.000Z",
      "jev": 0.5762,
      "judge_a": 0.4935,
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      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 23,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "This post tests whether an LLM judge that assigns per-span credit during reinforcement learning on math problems can make a model's reasoning more efficient, not just its final answers more correct. The author trains Qwen3.5-4B with GRPO under three conditions (outcome-only, and judge credit at two strengths).",
      "page": "https://rubric.hrih.si/editions/2026-11-08/30/",
      "card": "https://rubric.hrih.si/og/2026-11-08/30.png",
      "breakdown": {
        "significance": {
          "name": "Significance",
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        "reader_interest": {
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        "audience_breadth": {
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        "substantiated": {
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        "technical_depth": {
          "name": "Technical depth",
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        },
        "new_information": {
          "name": "New information",
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        "promotional": {
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      }
    },
    {
      "rank": 31,
      "id": "st_20261108_17558",
      "title": "Trigger.dev: Security incident report – Sunday November first",
      "url": "https://trigger.dev/blog/security-incident-report-nov-2026",
      "domain": "trigger.dev",
      "site": "trigger.dev",
      "categories": [
        "security",
        "devops"
      ],
      "category_names": [
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "News",
      "depth": "three_judges",
      "published_at": "2026-11-07T02:41:26.000Z",
      "jev": 0.5682,
      "judge_a": 0.639,
      "judge_b": 0.6082,
      "score": 0.6051,
      "contested": false,
      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 25,
      "topic_penalty": 0.08,
      "repeat_penalty": 0.0021,
      "summary": "Trigger.dev, a platform for running background jobs, disclosed a security incident on November 1, 2026, in which an attacker obtained root API keys for some customer environments and accessed environment variables, affecting 0.66% of customers.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/31/",
      "card": "https://rubric.hrih.si/og/2026-11-08/31.png",
      "breakdown": {
        "significance": {
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        "audience_breadth": {
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        "substantiated": {
          "name": "Substantiated",
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    },
    {
      "rank": 32,
      "id": "st_20261108_17520",
      "title": "Implementing GPT-2 in CSS",
      "url": "https://shubhampatil.dev/blog/gpt2-css",
      "domain": "shubhampatil.dev",
      "site": "shubhampatil.dev",
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        "ai"
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        "AI & machine learning"
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      "page": "https://rubric.hrih.si/editions/2026-11-08/32/",
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    {
      "rank": 33,
      "id": "st_20261108_17810",
      "title": "Former Meta Security Chief Calls Password Access for AI Agents 'Insane'",
      "url": "https://epyhia.com/meta-launches-muse-ai-spaces-former-security-chief-calls-password",
      "domain": "epyhia.com",
      "site": "epyhia.com",
      "categories": [
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        "security"
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      "category_names": [
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        "Security"
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      "published_at": "2026-11-07T13:59:07.000Z",
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      "summary": "Former Meta chief security officer Alex Stamos, now at AI coding startup Cognition, called giving AI agents access to full password vaults and finances \"totally insane\" on a podcast this October.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/33/",
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    },
    {
      "rank": 34,
      "id": "st_20261108_18178",
      "title": "Data Centers are Being Powered By Giant Gas Plants, Possibly Increasing Carbon Pollution for Decades",
      "url": "https://hardware.slashdot.org/story/26/11/07/2153213/data-centers-are-being-powered-by-giant-gas-plants-possibly-increasing-carbon-pollution-for-decades",
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      "site": "Slashdot",
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      "page": "https://rubric.hrih.si/editions/2026-11-08/34/",
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    },
    {
      "rank": 35,
      "id": "st_20261108_18127",
      "title": "TPS on Kimi K3 with TPU Megakernels",
      "url": "https://inferact.ai/news/tpu-megakernels",
      "domain": "inferact.ai",
      "site": "inferact.ai",
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        "ai",
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      "category_names": [
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        "AI & machine learning",
        "Hardware & devices"
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      "page": "https://rubric.hrih.si/editions/2026-11-08/35/",
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    {
      "rank": 36,
      "id": "st_20261108_17843",
      "title": "Microsoft open-sources DACS: stalled data loads fell from 14% to 0.1%",
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      "page": "https://rubric.hrih.si/editions/2026-11-08/36/",
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    {
      "rank": 37,
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      "title": "Kubernetes, mark II? Why Google's giving away gVisor used by A/ and OAI",
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      "domain": "thenewstack.io",
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        "devops"
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      "category_names": [
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        "Security",
        "Infrastructure & DevOps"
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      "summary": "Google is donating three AI agent infrastructure projects, gVisor (a container sandboxing runtime already used by Anthropic and OpenAI), Agent Substrate, and OpenRL, to the Cloud Native Computing Foundation, aiming to make them industry standards as Kubernetes became.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/37/",
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    {
      "rank": 38,
      "id": "st_20261108_18117",
      "title": "Anatomy of a disguised botnet deploy: how we caught a hidden C2 bot on platform",
      "url": "https://railyard.run/blog/anatomy-of-a-disguised-botnet-deploy/",
      "domain": "railyard.run",
      "site": "Railyard",
      "categories": [
        "security",
        "devops"
      ],
      "category_names": [
        "Security",
        "Infrastructure & DevOps"
      ],
      "format": "Write-up",
      "depth": "three_judges",
      "published_at": "2026-11-07T20:49:52.000Z",
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      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 34,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "Railyard, a hosting platform, describes how it caught a disguised botnet node deployed on its service: an obfuscated loader hid a command-and-control bot that the platform's container isolation contained, with the only real-world effect a brief blocklisting of a relay server.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/38/",
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          "name": "Technical depth",
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      }
    },
    {
      "rank": 39,
      "id": "st_20261108_18056",
      "title": "Supermarket cooling systems: German intelligence investigates suspected sabotage",
      "url": "https://www.heise.de/en/news/Supermarket-cooling-systems-German-intelligence-investigates-suspected-sabotage-11516443.html",
      "domain": "heise.de",
      "site": "heise online English",
      "categories": [
        "security",
        "it"
      ],
      "category_names": [
        "Security",
        "IT & networking"
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      "format": "News",
      "depth": "three_judges",
      "published_at": "2026-11-07T18:43:00.000Z",
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      "repeat_penalty": 0,
      "summary": "Germany's intelligence agencies have joined police investigations into cyberattacks that tampered with temperature controls in supermarket refrigerated and frozen-food cases, spoiling food and raising fears of coordinated sabotage. Attacks have spread to several other European countries.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/39/",
      "card": "https://rubric.hrih.si/og/2026-11-08/39.png",
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        "technical_depth": {
          "name": "Technical depth",
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          "judge_b": 0.38,
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          "name": "New information",
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          "name": "Promotional",
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        "overclaim": {
          "name": "Overclaim",
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      }
    },
    {
      "rank": 40,
      "id": "st_20261108_17822",
      "title": "Ramp data shows AI spending stalling just as Anthropic nears its IPO",
      "url": "https://techcrunch.com/2026/11/06/ramp-data-shows-ai-spending-stalling-just-as-anthropic-nears-its-ipo/",
      "domain": "techcrunch.com",
      "site": "TechCrunch",
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        "ai",
        "startups"
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      "category_names": [
        "AI & machine learning",
        "Startups & founders"
      ],
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      "published_at": "2026-11-07T13:31:42.000Z",
      "jev": 0.5849,
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      "summary": "Ramp's latest AI Index, reported by TechCrunch, finds that business token spending on AI rose only 1.5% across August and September even as token volumes kept climbing, down from 42% growth in July.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/40/",
      "card": "https://rubric.hrih.si/og/2026-11-08/40.png",
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      }
    },
    {
      "rank": 41,
      "id": "st_20261108_18039",
      "title": "Webframe runs web pages as CLI programs using a hidden system web view",
      "url": "https://github.com/tractordev/webframe",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming"
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      "category_names": [
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      "summary": "webframe is a small Go binary that loads a web page in a hidden system webview (WebKit, WebKitGTK, or WebView2) via the glaze library, and wires the page's console to stdout, errors to stderr, and stdin to in-page JavaScript evaluation.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/41/",
      "card": "https://rubric.hrih.si/og/2026-11-08/41.png",
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    },
    {
      "rank": 42,
      "id": "st_20261108_17875",
      "title": "Securing AI Agents [at Uber]: Lessons from Real-World Deployment",
      "url": "https://www.uber.com/us/en/blog/securing-ai-agents/",
      "domain": "uber.com",
      "site": "Uber",
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      "category_names": [
        "AI & machine learning",
        "Security"
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      "published_at": "2026-11-07T15:23:58.000Z",
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      "repeat_penalty": 0.0162,
      "summary": "Uber published a write-up on its \"Uber Secure AI\" framework for securing agentic AI systems across their lifecycle. Its core claim is that traditional identity and authorization controls can't catch agents that are manipulated into harmful but technically permitted actions.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/42/",
      "card": "https://rubric.hrih.si/og/2026-11-08/42.png",
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    {
      "rank": 43,
      "id": "st_20261108_18173",
      "title": "Ask HN What to learn for AI Researcher interview?",
      "url": "https://github.com/sergey-kurshev/AiPrepLab",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming",
        "ai"
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      "category_names": [
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        "AI & machine learning"
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      "score_rank": 40,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "A GitHub repository called AiPrepLab, by Sergey Kurshev, offers a free, CPU-friendly curriculum for junior AI researcher interviews, covering math, coding from scratch, ML debugging, systems, inference, RAG, safety, CUDA, and mixture-of-experts. Each module pairs a short article with a runnable exercise and checks.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/43/",
      "card": "https://rubric.hrih.si/og/2026-11-08/43.png",
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    },
    {
      "rank": 44,
      "id": "st_20261108_17748",
      "title": "You Don't Know If Your Agents.md Helps. You Just Keep Adding Rules",
      "url": "https://prod.codes/blog/agents-md-false-confidence/",
      "domain": "prod.codes",
      "site": "!prod",
      "categories": [
        "programming",
        "ai"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning"
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      "published_at": "2026-11-07T11:59:38.000Z",
      "jev": 0.6351,
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      "score_rank": 41,
      "topic_penalty": 0.08,
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      "summary": "A developer describes running Claude Code, Codex and Antigravity on one workspace and finding that agents stall at decision points, so overnight work waits for the owner to reply.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/44/",
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      "breakdown": {
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          "name": "Practical relevance",
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        },
        "substantiated": {
          "name": "Substantiated",
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        "technical_depth": {
          "name": "Technical depth",
          "weight": 0.05,
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          "judge_b": 0.52,
          "mean": 0.5167,
          "spread": 0.43
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          "name": "New information",
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          "name": "Promotional",
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          "name": "Overclaim",
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      }
    },
    {
      "rank": 45,
      "id": "st_20261108_17726",
      "title": "Call is being recorded – and broadcast to the internet",
      "url": "https://groundup.org.za/article/your-call-is-being-recorded-and-broadcast-to-the-internet/",
      "domain": "groundup.org.za",
      "site": "GroundUp News",
      "categories": [
        "security",
        "it",
        "policy"
      ],
      "category_names": [
        "Security",
        "IT & networking",
        "Policy & regulation"
      ],
      "format": "News",
      "depth": "three_judges",
      "published_at": "2026-11-07T10:34:34.000Z",
      "jev": 0.4385,
      "judge_a": 0.5315,
      "judge_b": 0.7311,
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      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 42,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "GroundUp reported that Open V, a Gqeberha IT firm, misconfigured a server that left about 9,000 audio files of customer telephone calls, many involving medical scheme queries for Edge Financial Group, publicly accessible online for roughly two months. The exposure also included databases, passwords and source code.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/45/",
      "card": "https://rubric.hrih.si/og/2026-11-08/45.png",
      "breakdown": {
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          "name": "Technical depth",
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          "name": "New information",
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          "name": "Promotional",
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        "overclaim": {
          "name": "Overclaim",
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      }
    },
    {
      "rank": 46,
      "id": "st_20261108_17705",
      "title": "OMLX: Use a second Mac to speed up local LLM prefill 2.9x",
      "url": "https://github.com/jundot/omlx/releases/tag/v0.7.4.dev1",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "ai",
        "hardware"
      ],
      "category_names": [
        "AI & machine learning",
        "Hardware & devices"
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      "depth": "three_judges",
      "published_at": "2026-11-07T10:15:17.000Z",
      "jev": 0.5974,
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      "flag_penalty": 0,
      "freshness_penalty": 0,
      "score_rank": 43,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "oMLX 0.7.4.dev1, a development release of the open-source local LLM server for Mac, adds a Prefill Accelerator that lets a faster-GPU Mac handle the first layers of prompt processing while a larger-memory Mac runs the rest, with decode speed unchanged.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/46/",
      "card": "https://rubric.hrih.si/og/2026-11-08/46.png",
      "breakdown": {
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          "name": "Significance",
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        "reader_interest": {
          "name": "Reader interest",
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          "name": "Practical relevance",
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        "technical_depth": {
          "name": "Technical depth",
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          "judge_b": 0.78,
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        },
        "new_information": {
          "name": "New information",
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        "promotional": {
          "name": "Promotional",
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        "overclaim": {
          "name": "Overclaim",
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      }
    },
    {
      "rank": 47,
      "id": "st_20261108_17942",
      "title": "Chainalysis says breach exposed credentials for all customers",
      "url": "https://www.bleepingcomputer.com/news/security/chainalysis-says-breach-exposed-credentials-for-all-customers/",
      "domain": "bleepingcomputer.com",
      "site": "BleepingComputer",
      "categories": [
        "security",
        "fintech"
      ],
      "category_names": [
        "Security",
        "Fintech"
      ],
      "format": "News",
      "depth": "three_judges",
      "published_at": "2026-11-07T16:57:37.000Z",
      "jev": 0.5458,
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      "score_rank": 44,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "Blockchain analytics firm Chainalysis says a recent attack exposed usernames, password hashes, and live tokens for all its customers, after earlier stating the impact was limited.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/47/",
      "card": "https://rubric.hrih.si/og/2026-11-08/47.png",
      "breakdown": {
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        "substantiated": {
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        "technical_depth": {
          "name": "Technical depth",
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      }
    },
    {
      "rank": 48,
      "id": "st_20261108_17651",
      "title": "Coding agent skills, tested on 15 backend tasks with hidden checks",
      "url": "https://github.com/globalmpc/backendskills",
      "domain": "github.com",
      "site": "GitHub",
      "categories": [
        "programming",
        "ai",
        "security"
      ],
      "category_names": [
        "Programming & dev tools",
        "AI & machine learning",
        "Security"
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      "format": "Paper",
      "depth": "three_judges",
      "published_at": "2026-11-07T07:34:15.000Z",
      "jev": 0.7056,
      "judge_a": 0.396,
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      "freshness_penalty": 0,
      "score_rank": 46,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "A Show HN post from the globalmpc team introduces open-source agent skills for Claude Code, Codex and Cursor meant to make AI-written backend code safer on auth, data, payments and schema changes.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/48/",
      "card": "https://rubric.hrih.si/og/2026-11-08/48.png",
      "breakdown": {
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        "reader_interest": {
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        "audience_breadth": {
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        "practical_relevance": {
          "name": "Practical relevance",
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        "substantiated": {
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        "technical_depth": {
          "name": "Technical depth",
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      }
    },
    {
      "rank": 49,
      "id": "st_20261108_18203",
      "title": "The Hardest Tarball",
      "url": "https://stoppels.ch/2026/11/07/the-hardest-tarball.html",
      "domain": "stoppels.ch",
      "site": "stoppels.ch",
      "categories": [
        "programming"
      ],
      "category_names": [
        "Programming & dev tools"
      ],
      "format": "Write-up",
      "depth": "three_judges",
      "published_at": "2026-11-07T23:05:03.000Z",
      "jev": 0.5609,
      "judge_a": 0.4039,
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      "score_rank": 48,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "Harry Stoppels, writing on his blog, describes a single crafted tar archive built with one GNU tar command that extracts in eight different ways depending on the tool used.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/49/",
      "card": "https://rubric.hrih.si/og/2026-11-08/49.png",
      "breakdown": {
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          "name": "Significance",
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        "practical_relevance": {
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        "substantiated": {
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          "name": "Technical depth",
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    },
    {
      "rank": 50,
      "id": "st_20261108_18104",
      "title": "Hacking South Korea's National Cyber Security Center",
      "url": "https://duti.dev/blog/2026/hacking-south-korea/",
      "domain": "duti.dev",
      "site": "Insomniac's Log",
      "categories": [
        "security"
      ],
      "category_names": [
        "Security"
      ],
      "format": "Analysis",
      "depth": "three_judges",
      "published_at": "2026-11-07T20:07:17.000Z",
      "jev": 0.421,
      "judge_a": 0.5413,
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      "score_rank": 49,
      "topic_penalty": 0.08,
      "repeat_penalty": 0,
      "summary": "A researcher writing as \"yuu\" describes finding expired nameservers delegated to government domains via certificate transparency, including one for South Korea's National Cyber Security Center (ncsc.go.kr) whose delegation pointed to a lapsed domain, which they registered. Since DNSSEC was absent, this allowed DNS hijacking.",
      "page": "https://rubric.hrih.si/editions/2026-11-08/50/",
      "card": "https://rubric.hrih.si/og/2026-11-08/50.png",
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        "practical_relevance": {
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        "substantiated": {
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      }
    },
    {
      "rank": 51,
      "id": "st_20261108_18025",
      "title": "TokiRecur – llama.cpp LLM launcher with KV-cache recycling",
      "url": "https://github.com/lhadzic377-netizen/TokiRecur",
      "domain": "github.com",
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