Tooling4d ago
Karpathy builds HackerTube in about 2 hours with Claude Code and Opus 5.5: a YouTube clone of 6,454 videos recommended on Hacker News
On Oct 31 Andrej Karpathy published HackerTube (hackertube.org) on his personal blog: a YouTube-style site that only lists the 6,454 YouTube videos recommended in Hacker News comments from 2007 to 2026, grouped into 150 topics. He says Opus 5.5 in Claude Code did the work: it downloaded every HN comment, ran Sonnet over them in a Workflow to narrow things down to about 40K comments recommending a video, wrapped the result in a YouTube UI, then deployed and hardened it on a VPS, all in about 2 hours. His only manual steps were creating a YouTube API key, booting the VPS and buying the domain. His HN submission reached the front page with about 1,005 points and 308 comments.
Research21d ago
Last Secure Code Benchmark: Vibe Coding Under Fire as Top Agents Produce Secure Code on Only 25.1% of Tasks
While AI coding agents and 'vibe coding' workflows synthesize an unprecedented volume of production software, code security remains highly fragile. Researchers from NUS and Monash University introduced the Last Secure Code Benchmark, evaluating 450 tasks derived from 150 real CVE vulnerabilities across 7 languages (Java, C, C++, JS, Python, PHP, Go) and 24 CWE categories (including 21 of the 2025 CWE Top 25 and 8 of the Top 10 KEVs). While frontier agents achieve 73.6% to 91.3% functional completion rates, even the strongest model (Claude Fable 5) produces code that is both functional and secure on only 25.1% of tasks, exposing acute supply-chain risks in autonomous vibe coding.
Tooling9d ago
Microsoft Research and UC Berkeley open-source TokenEconomizer, a plugin that cuts Claude Code and Codex costs by 18.4–30.9% on SWE-Bench without changing the agent
Microsoft Research, Microsoft Security and UC Berkeley open-sourced TokenEconomizer on GitHub (Apache 2.0, v0.1.0 released October 26), a plugin for Claude Code, Codex, GitHub Copilot CLI and Pi, with experimental opencode support. It learns from earlier sessions in the same repository: micro-skills remember repo facts, tool pruning drops tool definitions that go unused, and a loop guard nudges an agent that is stuck. The paper reports 18.4–30.9% lower mean cost across five agent and model combinations on 277 SWE-Bench tasks (paper figures).
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