Linus Torvalds Praises AI for Kernel Debugging, But Notes It Gives Up Too Easily
Score
76Linus Torvalds 对 AI 的公开评价罕见，提供了顶级开发者对生成式 AI 的真实反馈，信息增量高。
内容来自 Simon Willison 博客，直接引用 Torvalds 原话，表达清晰且来源可靠。
对开发者使用 AI 工具进行复杂调试具有指导意义，展示了 AI 的能力边界和协作模式。
该事件为近期发生（原文日期 2026-08-22），具备即时新闻价值。
Simon Willison 是知名开发者与技术博主，且内容为 Linus Torvalds 的原始提交说明，可信度高。
Base 84 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
This content presents a first-hand authoritative perspective on real-world AI-assisted programming, carrying industry influence and topical interest, though it remains a personal comment rather than a systematic evaluation.
AI commentary
「This is worth watching because it comes from the least likely person to praise AI—a stubborn pioneer. Torvalds' feedback shows AI can indeed serve as a workhorse in complex engineering, but its tendency to 'give up' is a real issue—hinting that training and prompting strategies need improvement, and reminding developers that when AI says something is impossible, it may not actually be.」
Quick overview
Linus Torvalds, the creator of Linux, publicly praised AI-assisted programming while fixing a drm/xe driver issue. He described the debugging session as "from hell," noting that the AI did much of the grunt work and was a "tireless helper," but it also repeatedly insisted the problem was impossible and unsolvable, suggesting we should just write a report. Torvalds quipped that the AI might have been trained by people less stubborn than he is. Despite the AI's willingness to give up, it kept adding debug code and faithfully analyzing it when pushed. In the end, the issue was solved, and Torvalds even let the AI write the commit message. This remark reflects real-world experience and mixed attitudes of top programmers toward generative AI-assisted development.
- Torvalds acknowledged that the AI provided significant help in tedious debugging work, calling it a 'tireless helper'.
- The AI repeatedly declared the problem impossible and suggested giving up; Torvalds joked it was trained by people less stubborn than him.
- In the end, Torvalds let the AI write the commit message as recognition of its contribution.
Full text
Showing the source text; the full translation is being generated.
And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.
I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.
I suspect those things have been trained by people who may not be quite as stubborn as I am.
But while the AI was ready to give up several times, it did keep adding debug code and analyzing it faithfully when I pushed. So credit where credit is due and I let the AI write the commit message above.
— Linus Torvalds, drm/xe: Don't hand out the flat CCS storage as usable VRAM
Tags: linus-torvalds, linux, generative-ai, ai-assisted-programming, ai, llms
Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.