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.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
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.
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.
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.
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.
The article highlights Paul Dix’s astonishment that AI was able to generate one million lines of code and then refine it over several months into a reliable software product now used by millions of developers. Dix argues that this achievement is far more impressive than merely translating code between languages, emphasizing that with a verification system and clear guidance, AI can produce and iteratively improve highly complex, sophisticated software until it functions perfectly.
SF October 14th: A Birds of a Feather Session on Agentic Engineering
I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and in...
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.
The article reports a failed MX Keys Mini pickup where a customer, Usman, arrived at the building at 9:15 but was not met, leading to a negative rating. The author acknowledges that an auto‑reply incorrectly confirmed the author's presence at 9:27, worsening the situation, and has apologized on behalf of the account. They are considering disabling auto‑replies that promise the author is home when they cannot confirm it.
Simon Willison reflects on the evolving role of software developers in the age of AI, noting that while AI can produce high‑quality code, it also enables poor execution that leads to project failures. He argues that the industry is beginning to recognize the continued need for human collaboration and expertise to truly innovate. The piece highlights the tension between automation and the essential human element in software creation.
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.