A developer reflects on the boredom of AI-driven 'vibe coding' and why they still prefer hands-on programming for the joy and learning it provides.
A monthly retrospective on writing a book about effective writing for software developers, covering progress, goals, and metrics.
AI is predicted to bring formal verification tools like Dafny and Verus into mainstream use, aided by LLMs making them more accessible.
Bryan Cantrill discusses applying Large Language Models (LLMs) at Oxide, evaluating them against the company's core values.
Tips from David Crespo on effectively using Claude Code for understanding codebases and automating tedious coding tasks.
Introduces the ABCD framework for giving and receiving effective feedback on technical work like blog posts, tutorials, and projects.
A blog post discussing the importance of speed in technology and work, referencing Daniel Lemire's insights on avoiding obsolescence.
A developer reflects on an 11-year-old GitHub issue being closed, musing on career changes and tech evolution.
A summary of recent tech articles discussing AI's impact on code quality, AI-assisted healthcare, security risks, and developer productivity.
A critique of AI's role in software development, arguing that output is not productivity and that expertise remains essential for solving real problems.
Explores how natural language, like English, is becoming a key interface for software development with AI tools, lowering barriers to participation.
Argues that AI in software development should focus on automating non-coding tasks like meetings, docs, and testing, not just speeding up coding.
Simon Willison discusses data journalism, Django's origins, and tech's role in news on the Data Renegades podcast.
Analysis of Claude Opus 4.5 LLM release and the growing difficulty in evaluating incremental improvements between AI models.
Analysis of how engineering management trends shift with business cycles, highlighting core skills that remain constant.
An Intel Fellow shares advice on how to give effective, constructive technical feedback to hardware vendors like Intel to influence product development.
A developer discusses the non-deterministic nature of LLMs like GitHub Copilot, arguing that while useful, they cannot take ownership of errors like a human teammate.
Martin Fowler discusses the latest Thoughtworks Technology Radar, AI's impact on programming, and his recent tech talks in Europe.
A developer reflects on the balance between concise and clear code, arguing that too little code can be as harmful as too much.
Discusses the future of small open source libraries in the age of LLMs, questioning their relevance when AI can generate specific code.
