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Auto-research with codex: How I achieved a 232x Faster Kernel
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Overview
Codex was utilized to achieve a 232x faster kernel for the qr_v2 problem in GPU mode. The implementation involved optimizing the blocked Householder algorithm and introducing idea diversity to enhance performance.
Summary from sankalp.bearblog.dev.
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Auto-research with codex: How I achieved a 232x Faster Kernel
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Hacker News · sankalp.bearblog.dev
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Based on 5 sampled comments from this thread. Analyzed 2026-08-15.
- Praise: The ability to leverage LLMs for kernel optimization is a game-changer, with users reporting up to 232x speed improvements in their projects.
- Praise: DeepSeek v4 is proving itself as a reliable partner for optimizing complex tasks, making previously daunting benchmarks feel approachable and achievable.
- Praise: Several commenters are excited about the potential of AI agents to tackle well-defined optimization problems, indicating a significant leap in developer productivity.
- Concern: There's a clear concern that many AI-generated solutions are overfitting to specific inputs, leading to brittle code that fails under varied conditions.
- Concern: Commenters worry about the lack of generality in the AI-generated code, emphasizing that while speed is prioritized, maintainability and robustness are often sacrificed.
- Concern: One user noted that while LLMs excel in specific optimizations, they struggle to create general solutions, highlighting limitations in current AI approaches.