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.
Summary of key announcements and sessions from Microsoft Ignite 2025, focusing on Azure, hybrid cloud, AI, and developer tools.
An experienced freelance consultant shares strategies for finding clients in a challenging market, focusing on technical consulting and test automation.
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.
A developer shares their experience using Rust for a real-world project to create a resilient, scheduled health monitoring component.
An Intel Fellow shares advice on how to give effective, constructive technical feedback to hardware vendors like Intel to influence product development.
A curated collection of articles on software architecture, development practices, and Agile methodologies, focusing on platform engineering, code quality, and team dynamics.
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.
A senior engineer shares his experience learning to code effectively with AI, from initial frustration to successful 'vibe-coding'.
Practical steps for successfully leading a software or tech project, focusing on scope, communication, and iterative delivery.
Discusses the future of small open source libraries in the age of LLMs, questioning their relevance when AI can generate specific code.
Explores AI as a new computing paradigm (Software 2.0), where automation shifts from specifiable tasks to verifiable ones, explaining its impact on job markets and AI progress.
A curated collection of articles on software architecture, development practices, Java updates, and testing strategies for tech professionals.
A developer argues that AI tools, while feeling productive, actually create more low-priority busywork and reduce overall effectiveness.
