From January to March, it felt like every single day there was another AI announcement. Two months later, Microsoft has told its engineers to drop Claude Code and switch back to GitHub Copilot by June 30. Uber burned through its entire 2026 AI coding budget in four months, with per-engineer token costs running between $500 and $2,000 per month. And yet, where are the major products built fully by vibe coding? Most of what we’ve seen so far is demos, wrappers, landing pages, and internal tools. AI is useful. It speeds up work. But writing code was never the hard part. Shipping, maintaining, securing, scaling, and building something people actually use still requires real engineering. The AI boom may not be over. But the “AI replaces software teams” narrative is starting to calm down.

🔺 The real comparison point is shifting. For coding agents, “better” is not only smarter output. It is execution reliability: clear plan bounded edits diff visibility test evidence rollback path human approval Once agents touch real repos, trust beats vibes.

🔺 This is the trust problem for paid agent tools. When the tool becomes part of real work, entitlement state has to be boringly reliable: clear expiry time grace window local warning audit log recovery path For agents, access control is not billing plumbing. It is part of the product.

Andrej Karpathy builds a personal wiki to think with LLMs. I built something that thinks back. Claude Code plus Obsidian equals an AI that actually knows you. Your goals. Your context. Your history. Your patterns. Not a chatbot you explain yourself to every session. A second brain that remembers everything and compounds every week you use it. Here is exactly how to build it.

You have $100 to spend on an AI model. Which model are you choosing? 1. Codex 2. Claude Code

Recently came across a really impressive project: obra/superpowers ⭐️ 200k+ Honestly, this star count goes far beyond a regular tool. When I was tinkering with agents before, the biggest headache was: the workflows getting messier and messier, skills copied everywhere, and never daring to make changes later. But then I saw superpowers’ approach—and it actually makes sense: • Skills are modular and composable • Agent collaboration logic is clearly designed • They’ve thought through the entire pipeline from demo to production Last week, I used it to build a code review agent. Before, I was just dumping everything into one massive prompt. Now, breaking it into skills makes multi-agent collaboration much smoother. It feels like building agents has shifted from “stacking prompts” to “building systems.”