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Only Papers#GitHub
Nov 13Fri
  1. Hacker News AI58

    How much of GitHub's most-starred AI tooling is written by coding agents?

    An analysis of 324,088 commits across 52 of GitHub's most-starred AI-tooling repositories found that the median repo had 16.9% AI-signed commits in its last 180 days of activity, with 21 of 52 at 25% or more, and that AI share showed no detectable link to stars (Spearman ρ = -0.05, p = 0.72). At day 180, these repos labeled 2.0 times as many commits as fixes (34.1% vs 17.3%), had lower churn (1.92x vs 3.77x) and kept more written code (67.1% vs 41.7%) than 12 pre-Copilot projects, but the gaps are concentrated in the 31 projects started in 2025 or later, so the sample cannot separate AI use from recency. Of 24 repos publishing a benchmark harness with committed results, 15 measure whether the tool does its job, and several publish numbers that went against them.

Nov 12Thu
  1. Hacker News AI38

    Did it use REA? Checking viral AI reverse-engineering demos

    A verification page checks 39 cases described as "built with REA" against each project's README, agent files, author posts or official showcase, finding 26 confirmed, 3 partial, 4 unverified and 6 not REA (mostly Claude Code or Codex with Ghidra or IDA, or manual work). The README column was re-checked on 2026-11-10 with no changes, and X posts could not be opened from the authors' environment. It notes that two small controlled comparisons, rea-lab (482 preregistered runs) and leanrea (36 runs), found no significant gain in solve rate from REA at higher time and cost.

Nov 7Sat
  1. Hacker News AI33

    Pleasedonotescape.com: A dataset of sandboxes for AI coding agents

    The dataset lives at github.com/pjlsergeant/ai-sandboxes, where every claim traces to a primary source and pull requests to correct or add entries are welcome. It was built by the same person behind byre, described as a comfortable, constrained container box for coding agents. The post suggests byre may be the right amount of fence if the tools on the page feel like overkill for day-to-day use.

Oct 5Mon
  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.