An addendum to the Agile Manifesto for the AI era

With massive respect to Jeff Sutherland and all the thought leaders who met at a ski lodge in Snowbird, Utah, in 2001 and created the Agile Manifesto - it might be time to revisit / refresh / revitalize the Agile Manifesto in light of the emergence of LLMs. The scarce resource is no longer programming capacity, but organizational clarity and architectural coherence. As a thought exercise (and for fun) I took a stab at it. I would love to get some collaborative feedback to improve it. Of course I do not expect this to replace the Agile Manifesto but I'd like to use it when speaking…

Hey HN, I just wanted to share that after five months of intense work and countless caffeine-fueled writing sessions, "Master Software Architecture: A Pragmatic Guide" is now available. Writing this book was a crazy time. I entirely focused on it, and it took exactly 753 hours of writing, editing, and image editing & resizing(!) until the premiere. Yes, the last one (images) was a killer. If you ever think about writing a book, reserve a lot of time for it unless someone else does it for you. As a result, I am handing you the nearly 400-page book. It is a guide that will help you connect all…

Hey HN! Thank you for all the support and feedback on my original submission 2 months ago. I've been improving the backend using a MCTS/AlphaZero approach and it's currently producing much better results. My long term goal is to allow users to manage multiple projects, deployed autonomously, both from scratch and by making continual updates all prompted with natural language. The cost of each project has been lowered to $9 as performance with smaller models has improved (I migrated from Claude-3-Opus to gemini-1.5-flash). Thanks for checking it out!

This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…