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.
Simon Willison reflects on the emotional impact of AI tools that can produce code quickly, noting that many developers experience an initial sense of disheartenment. He argues that recognizing the shift from coding to higher‑level problem solving allows experienced engineers to leverage new tools and add greater value. Willison emphasizes that software engineering has always faced rapid change, so adapting to AI is part of the profession’s ongoing evolution.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
Laurie Voss argues that while the cost of writing code has fallen dramatically, the costs of reviewing, fixing, and operating software are rising and will continue to do so. She emphasizes that the true expense lies in understanding user needs, precisely defining requirements, and ensuring a pleasant user experience—costs that are unique to each software product and do not scale with reuse. As software demand grows without an upper limit, these user‑centric costs will dominate the overall development effort.
Simon Willison discusses the pitfalls of attempting to replace a legacy system with a new one when technical debt is overwhelming. He explains that while the old system continues to evolve, developers lack incentive to improve it, and the new team, initially fast, eventually struggles to understand and deliver the required functionality. The result is often two partially functional systems in production, with the new one abandoned and the old one still running, increasing risk and complexity.
The article discusses Bryan Cantrill’s response to a tweet by former Anthropic employee Jacob Coxon, who claimed that AI could kill humanity by the end of the decade. Cantrill shares a personal anecdote about how his own youthful mistakes caused undue panic among non‑technical peers and warns against repeating that pattern. He emphasizes that domain experts must be cautious when making alarmist claims, especially about complex topics like critical infrastructure, bioweapons, and extinction, and that the burden of accurate information lies with those making such statements.
The article "How To Write With An LLM" by Thomas Ptacek explains how to use large language models (LLMs) as copyeditors rather than writing assistants. Ptacek advocates a strict rule: never use any single word or phrase suggested by an LLM, treating it as intellectual personal protective equipment. He shares his own practice of using LLMs for fact‑checking, spelling, grammar, and occasional thesaurus help, and provides a screenshot of his personal LLM copyediting tool along with a prompt to help readers build their own.
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.
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.
Claude Cowork and the standard chat interface are merging into a single Claude experience, allowing users to hand over tasks or ask quick questions that the system will continue to handle even after the laptop is closed. The rollout will begin with Pro and Max plan users across web, desktop, and mobile, and will extend to new users on these plans over the coming weeks. This integration suggests Claude is evolving into a general agent, simplifying the distinction between Cowork and regular Claude usage.
Anthropic has announced that it will donate the Agent Skills standard to the Agentic AI Foundation (AAIF), a Linux Foundation‑backed organization co‑founded with Block and OpenAI. The move follows Anthropic’s earlier declaration that skills would be an open standard and aligns with the AAIF’s recent role as the home of the Model Context Protocol (MCP). The donation is part of Anthropic’s commitment to keeping Agent Skills community‑driven and vendor‑neutral.