Don't classify. Hallucinate!
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Simon Willison.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
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.
Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP).
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 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.
The article "Anti-Patterns in Software Blogging" highlights common pitfalls in technical writing, such as meandering introductions, overestimating readers’ prior knowledge, and overusing links to avoid explanations. It emphasizes the importance of a clear, conversational voice and warns against the blandness that can arise when developers rely too heavily on AI-generated content. The piece urges bloggers to write in a natural, engaging style to better connect with their audience.