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  1. The Decoder88

    Open-weight model GLM 5.3 built full Firefox exploit chains with zero human input in under 48 hours

    Epoch AI researchers report that the Chinese open-weight model GLM 5.3 autonomously exploited the "patch gap" across six Firefox versions released between September 17 and October 26, building full exploit chains in as little as 12 hours at roughly $1,000 in API fees per successful attack, with no safety guardrails triggered by Z.ai or other API providers. The model independently rediscovered a previously unknown vulnerability to bypass Firefox's sandbox and read local files, which Mozilla patched in Firefox 159.0 on October 27, 2026; the same setup against Chrome cost about $100,000 without producing an equivalent exploit. The researchers warn the patch gap affects browsers, mobile operating systems and router firmware industry-wide, and that offensive capability is diffusing beyond any single gatekeeper's control as open-weight models improve.

    Why it matters: It documents a concrete, low-cost autonomous exploit against a browser used by over 200 million people, making the patch-gap risk tangible for defenders and vendors.

  1. Replit Blog60

    ViBench 2.0: A Benchmark for Long-Horizon Software Building

    Replit releases ViBench 2.0, a long-horizon software development benchmark in which an agent builds a product from an empty project and then adds up to eighteen features on top of its own code, spanning seventeen products, 241 feature requests and 544 test plans. In Fireworks' official runs, GPT-6.1 Sol leads with a pass@1 of 65%, followed by GPT-6 Astra at 60% and Opus 5.5 at 49%, and no open-weight model passes a full build. Eight of the products are public on GitHub in the Harbor format, and results can be rerun from release v2.0.6 on the OpenHands-based harness.

    Why it matters: It offers concrete pass rates, costs and failure modes for frontier models on multi-feature product building, with a public, rerunnable harness.

  1. Hacker News AI76

    Rocky Linux Creator Built a New 'Not Linux' Kernel in 11 Days Using AI

    Gregory M. Kurtzer, creator of Rocky Linux, developed Ginux, a Rust-first x86-64 kernel and OS prototype that boots via UEFI without a Linux host kernel and runs unmodified Linux userspace inside kernel-native domains, including Rocky Linux 10 systemd reaching multi-user.target. The project was built over eleven days by one persistent OpenAI Codex task using GPT-5.6 Sol, producing roughly 139,000 retained lines of implementation and test code under a method called Human-Directed Agentic Engineering. Ginux is explicitly experimental, with incomplete Linux ABI coverage, device support, and security validation, and is not intended as a production replacement for Linux.

    Why it matters: It documents a notable experiment in AI-assisted kernel development and is candid about the resulting system's limits.

  2. Artificial Analysis Articles60

    Announcing Artificial Analysis Intelligence Index v5

    Artificial Analysis released Intelligence Index v5, its biggest upgrade since v4, adding FrontierCode 2.0 and Terminal-Bench-Science and upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, with 60% of the Index now based on private test sets or held-out answers. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while the open weights gap widens from 3 points to 12. It also launched the Progress Index v1, which tracks model capability over time on one continuous scale.

    Why it matters: It details a major benchmark revision and new leaderboard results, useful for readers tracking how frontier and open-weight models are measured.

  1. The Decoder78

    Anthropic's Claude now writes code and proves it safe automatically

    Anthropic's Frontier Red Team, working with partner Theorem, used Claude to generate roughly 180 million lines of mathematical proof code in a few weeks, verifying existing software from 123 open-source libraries in Google's OSS-Fuzz project by translating crash tests into mathematical problems and analyzing compiled machine code, with the Rocq program automatically checking the proofs. The researchers say Claude found hundreds of bugs, including as many security bugs in one week in a widely used PDF reader as are typically reported for that software in a year, and caught a logical error undetected for over a decade. Anthropic notes the proofs hold only under strict conditions—a specific processor architecture, single-threaded programs, and inputs under 64 MB—and that compute costs are steep, with individual proofs running up hundreds of millions of tokens.

    Why it matters: It offers a concrete look at AI-assisted formal verification, including its demonstrated bug-finding results and the practical limits Anthropic itself acknowledges.

  1. GitHub Blog · AI & ML61

    ReviewBench: An open benchmark for AI code review

    GitHub introduces ReviewBench, an open benchmark for AI code review built from analysis of 103.9 million GitHub pull requests, containing 219 public pull requests across 19 languages with a multi-source golden set labeled for severity and category. It reports grounded and augmented precision, recall, and F1 metrics, uses Claude Sonnet 5 as the LLM grader under a published rubric, and had its ground-truth findings independently re-labeled by senior engineers with 96.6% agreement. GitHub says ReviewBench's offline predictions for a Copilot code review multi-model ensemble matched a later A/B test, which showed an 8.0% rise in addressed rate, 13.6% in recall, 61% in comment volume, and an 8.0% drop in cost per review.

    Why it matters: It offers a concrete, reproducible methodology and public dataset for comparing AI code reviewers, plus evidence on how offline scores tracked a production experiment.

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