Turn thinking about AI
into things you can build.

In sixteen days, I built a single-user Personal Agent around governed Finance tools, one visible Timeline with automatic Sessions, real-device acceptance, ECS deployment, backup, restore, and rollback. This is a record of the product decisions, production failures, evaluation gaps, and the rule that a model saying “recorded” is never completion.

From ChatGPT, Doubao, Claude Code, and Codex to iCloud Drive, GitHub, and multiple personal data sources: why I am no longer just looking for a better AI tool, but building a Personal Agent I own—one that can continue across devices and take responsibility for real actions.

My mental model of the LLM API was stuck at 'two strings in, one string out.' After actually reading through the docs of Anthropic, OpenAI, and several Chinese platforms, I found today's LLM API is a stateless conversation-replay machine bolted to a built-in tool-calling protocol. This is a point-by-point teardown through the eyes of traditional API design: stateless replay and four caching philosophies, stop_reason and content blocks, the suspend-resume loop of tool calls, sampling params being clawed back by the platform, four philosophies of API consolidation, the protocol's audience shifting from your code to the model, and a rarely-named protocol war over Anthropic-compatible endpoints. The conclusion: the periphery is all familiar old craft; the genuinely new species is only that small core.