In the early 1960s, Fred Brooks managed the development of IBM’s System/360 computer systems. After it was done he penned his thoughts in the book The Mythical Man-Month which became one of the most influential books on software development after its publication in 1975. Reading it in 2026, we’ll find some of it outdated, but
Over the last couple of months Rahul Garg published a series of posts here on how to reduce the friction in AI-assisted programming. To make it easier to put these ideas into practice he’s now built an open-source framework to operationalize these patterns. AI coding assistants jump straight to code, silently make design decisions, forget
Chris Parsons has updated his guide on using AI to code. This is his third update, what I like about it is that he gives a lot of concrete information about how he uses AI, with sufficient detail that we can learn from him. His advice also resonates with the better advice I’ve seen out
Once a team adopts AI coding assistants, the first gains show up at the individual level: one developer can draft, modify, and refactor code much faster than before. But delivery speed is rarely limited by typing. When you look at the full delivery lifecycle, from requirements through release, new friction appears: Ambiguous requirements become code
Last week Thoughtworks released the 34th volume of our Technology Radar. This radar is our biannual survey of our experience of the technology scene, highlighting tools, techniques, platforms, and languages that we’ve used or otherwise caught our eye. This edition contains 118 blips, each briefly describing our impressions of one of these elements. As we
I attended the first Pragmatic Summit early this year, and while there host Gergely Orosz interviewed Kent Beck and myself on stage. The video runs for about half-an-hour. I always enjoy nattering with Kent like this, and Gergely pushed into some worthwhile topics. Given the timing, AI dominated the conversation – we compared it to
Last night I saw Central Square Theater’s excellent production of Breaking the Code. It’s about Alan Turing, who made a monumental contribution to both my profession and the fate of free democracies. Well worth seeing if you’re in the Boston area this month. Source link
I mostly link to written material here, but I’ve recently listened to two excellent podcasts that I can recommend. Anyone who regularly reads these fragments knows that I’m a big fan of Simon Willison, his (also very fragmentary) posts have earned a regular spot in my RSS reader. But the problem with fragments, however valuable,
Teams have always had mechanisms for collective learning. Retrospectives, post-incident reviews, lunch-and-learns. The best of these share a property: they convert individual experience into shared practice. What one person encountered in a debugging session or a production incident becomes something the whole team knows. The knowledge escapes the individual and enters the team’s infrastructure: its
Over the past decade, hardware has seen tremendous advances, from unified memory that’s redefined how consumer GPUs work, to neural engines that can run billion-parameter AI models on a laptop. And yet, software is still slow, from seconds-long cold starts for simple serverless functions, to hours-long ETL pipelines that merely transform CSV files into rows