Open Source

An email popped up in my inbox today: “You can just build things, enjoy 6 months on us.” Sender: the Codex team at OpenAI. The first line, in big letters: “You’re in.” Then: “Thanks for maintaining open source projects.” I stared at that line three times and couldn’t help laughing out loud. Free ride: secured A while back I filled out their Codex for Open Source application on a whim. Didn’t expect much — I just listed the PRs I’d submitted to context-mode, plus my own byf and dev-skills.

Twelve articles, from the Think-Act-Observe loop all the way down into the 500,000-line engineering abyss. Now, standing at the end of the series and looking back over the whole evolutionary path, a bigger question surfaces: What will AI coding tools ultimately evolve into? A better Copilot? A more powerful IDE plugin? Or something new that we haven’t named yet? Three Evolutionary Paths # Looking back at the development of AI coding tools, you can see three clear stages of evolution:

The claude-code-from-scratch project implements a “mini Claude Code” in roughly 3,000 lines of TypeScript. It has the Think-Act-Observe loop, a tool system, a system prompt, permission control, memory, Skills, multi-agent, MCP — every core concept we tore down over the previous ten posts. And the official Claude Code source? Roughly 500K lines of TypeScript/TSX. That’s a 100x+ gap between 3,000 lines and 500K. What’s in it? The gap isn’t “number of features”. from-scratch already covers every core concept. The gap is the road each concept travels from “it runs” to “it’s usable” — that road is called productionization.

Before MCP, every AI tool integration was a custom build. Want ChatGPT to reach a database? Write a plugin. Want Claude to call the GitHub API? Write a Function Calling definition. Want Copilot to access your internal systems? Write a Custom Tool. Every time, the same thing: define an interface, write adapter code, handle auth, manage errors. The wheel, reinvented countless times. The problem MCP (Model Context Protocol) solves is simple: give AI agents a standard protocol for connecting tools. Just as USB-C gave electronic devices a universal connector, MCP gives AI agents a universal tool interface.

There’s a counterintuitive phenomenon: take the same model, split it into multiple independent instances, and have them collaborate — the result is often better than a single instance. Not more compute (three instances burn more total tokens than one), not more capability (every instance is the same model). The gain comes from role separation — when each agent attends to only one facet of the problem, it works with more focus and more depth than a “do-everything agent”.

If you’ve used Claude Code, you may have noticed the /skills command listing installed “skills”. Writing standards, architecture design methods, TDD workflows, code review strategies — it sounds like a plugin marketplace. Skills are not plugins. That’s the first and most important thing to understand about the Skill system. A plugin is code — it extends what the program can do. A Skill is a prompt — it extends what the model knows.

Last time we covered context compaction — solving “how much the current session can remember”. But a coding assistant needs another kind of memory: long-term memory across sessions. You close Claude Code and reopen it tomorrow. What should it know? You prefer TypeScript over JavaScript You said last time “don’t use the underscore library” This project uses Vitest, not Jest You once corrected one of its misunderstandings Context compaction can’t handle these. The session ends, the history clears, tomorrow is a fresh window. If the AI needs you to re-explain everything each time, it isn’t an assistant — it’s a burden.

If you think a system prompt is just “giving the AI a persona”, Claude Code’s system prompt will upend that notion. It isn’t a few sentences. It’s a thousand-line template engine made of multiple layers, each responsible for something different: identity definition, behavioral constraints, tool documentation, project context, memory injection, skill loading. Some layers are static (identical every time); others are dynamic (injected in real time based on the project, the session, user preferences).

Last time we talked about the Think-Act-Observe loop — the skeleton of an AI coding assistant. A skeleton can’t work on its own; it needs muscle. In Claude Code, that muscle is tools. Without tools, the loop loses the Act between Think and Observe and degenerates into plain Q&A. With tools, the model can touch the real world: read your code, change your configs, run your tests.

If you dig through the Claude Code source, you will discover a counterintuitive fact: The core of the entire program is a while loop. Not a complex state machine, not an elaborate pipeline, not distributed coordination — just a plain while loop that does three things over and over: let the model think, let the model act, feed the results back. 1 2 3 4 5 6 7 8 9 while (user hasn't quit) { thinking = callLLM(conversation history + tool definitions) if (thinking.wants to call a tool) { result = executeTool(thinking.toolCall) conversationHistory.push(result) } else { output(thinking.reply) } } That’s it? The entire secret of an AI coding assistant lives in this loop?