AI is writing more code than ever. Anthropic’s own engineers saw their code output jump roughly 200% year over year. But here’s the catch: someone still has to review all that code. Human reviewers can’t scale at the same rate, and the result is a growing gap between code produced and code properly checked. That’s… Continue reading

If you’ve been building with MCP (Model Context Protocol) servers, you already know the pain: every tool schema gets injected into your LLM’s context on every single turn, whether the model uses those tools or not. With 30 tools, that’s roughly 3,600 tokens burned per turn doing absolutely nothing. Scale that to 120 tools over… Continue reading

There’s a growing gap between what AI coding tools promise and what they actually deliver. Most of them are great at autocompleting a function or generating a single file, but ask them to build a real, multi-file feature across a codebase and things fall apart fast. That’s exactly the problem [P0 by Purple AI](https://www.bepurple.ai/) is… Continue reading