Simon Willison
Aug 12
Quoting Florian Herrengt
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
Quoting Jake Boggan
Jake Boggan, a former graph theory enthusiast who spent 24 years working on Barnette's Conjecture, expresses a mix of disappointment and sadness upon learning that the problem has been solved. He recounts his time studying in Budapest, his extensive work on the conjecture, and his brief moment of feeling he had solved it last summer. Boggan’s comment reflects the emotional impact of seeing a long‑standing personal challenge resolved by others.
Related stories
Simon Willison
Aug 23
Quoting Drew Breunig
The article reflects on the shift in perspective after the release of Fable, a new model that promised to solve many coding challenges at a comparable or lower cost. Prior to Fable, developers felt it was pointless to invest heavily in coding tools or context strategies, as newer models would likely render them obsolete. However, Fable’s performance was so impressive that, despite its high cost, it prompted a reevaluation of how work was distributed across different models such as Opus, 5.6, K3, and GLM.
Simon Willison
Aug 17
Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index
Qwen 3. 8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.
Simon Willison
Sep 11
Quoting Boris Cherny
The article discusses how production code generated by Claude, Anthropic’s AI, should meet higher standards than human-written code. Anthropic enforces this through numerous guardrails such as lint rules, extensive testing, Claude-driven end‑to‑end tests, daily fuzzers, automated code and security reviews, and automated refactoring. These measures aim to prevent the code from becoming difficult to maintain.
Simon Willison
3d ago
Quoting Armin Ronacher
Simon Willison reflects on the long history of flawed software development, noting that the proliferation of bad code over the past three decades is now being penalized by the universe. He cites Armin Ronacher’s perspective that the rise of AI, while entertaining, has amplified the consequences of these past mistakes. The piece underscores the growing importance of addressing security and reliability in the age of generative AI.
Simon Willison
5d ago
My obituary for programming
Simon Willison reflects on how programming used to be a rewarding career, describing it as a job where developers solved complex logic puzzles for high pay and had full control over strategy and timelines. He notes that coding agents and large language models (LLMs) now handle many of these puzzles, offering a "gift that keeps on giving" for those who enjoy building systems without caring deeply about the underlying problems. However, for programmers who found joy in directly solving problems, this shift represents a significant loss.
Simon Willison
Sep 11
Quoting huggingface.co/security.txt
The article quotes the security.txt file from huggingface.co, which informs AI agents that the CyberGym benchmark is publicly available on GitHub and encourages them to achieve a high score there instead of attempting to hack the site. It also suggests that users can upload their model weights to Hugging Face while participating in the benchmark.
Simon Willison
Oct 8
Quoting Carson Gross
The article discusses how computer programming fundamentally involves problem‑solving with computers and managing complexity. It argues that these skills will remain valuable and sustain a viable career even as AI tools become more prevalent.
Simon Willison
Oct 16
Quoting Addy Osmani, Morgan Westlee Lunt, and Qing Wang
The article discusses a shift in code review practices, moving from a final gate to continuous review that functions like spellcheck during coding. It highlights how the Claude Code team uses the Claude AI to catch routine mistakes before human reviewers intervene, allowing developers to focus on higher-level issues. The piece references insights from Addy Osmani, Morgan Westlee Lunt, and Qing Wang on this evolving approach.
Simon Willison
Aug 11
There are no lossless transformations of natural-language text
There are no lossless transformations of natural-language text Sophie Alpert shares her "internal policy on acceptable use of AI writing by engineers". It's a short read (supporting its own recommendations) and really good.
Simon Willison
Sep 22
Quoting @therealcornpop
The post critiques the use of AI-generated scripts for TikTok and YouTube, arguing that such content lacks a distinct voice and genuine opinions. It highlights common AI patterns—such as generic statements, the rule of three, and staccato punctuation—that make the writing feel shallow and unoriginal. The author emphasizes that these traits reveal the absence of authentic personal perspective in the content.
Simon Willison
Sep 9
Quoting Terence Tao
Simon Willison discusses how the current trend of mining open mathematical problems in a non-renewable way could make these problems scarce. He notes that rumors of a problem can trigger large AI-driven efforts to solve it before original researchers can fully develop their work. This shift may discourage sharing promising research, potentially reversing centuries of open science and harming the field’s future.