đź”— How Claude Code is built
When the Anthropic team set out to develop Claude Code, their approach highlighted two key dynamics. First, they leaned heavily on the power of large language models to accelerate development in every way possible — from code review and writing to debugging. Just as importantly, they embraced a fluid process that felt less like rigid engineering and more like collaborative prototyping. By communicating with the model in natural language, they were able to iterate quickly, ship features at pace, and gather real feedback from both internal and external teams.
What kept this from becoming chaotic was the grounding in specifications and test-driven development. That discipline ensured the outcomes weren’t just fast, but also reliable and repeatable. It’s a balance that hints at where software development is headed: a shift away from keystrokes and memorization of language details, and toward insight, judgment, and architectural foresight.
The true value in engineering has never been in typing speed or arcane knowledge. It has always come from having the taste to identify the right problems and the foresight to build solutions that are secure, extensible, and dependable. With LLMs handling more of the mechanics, that value only becomes clearer.
I learned how Claude Code is built, and got insights into how a successful “AI-first engineering team” operates; it was a bit like a peek into a crystal ball and a potential future of how fast-moving startups will operate. The good news is that software engineers appear very much in demand in it…