Frontend & AI-product engineer · Guangdong, China
I build AI products and learning-first engineering projects — kept in public, commit by commit.
Talk is cheap. Show me the code.
Products I'm shipping right now. Each one is live somewhere you can click; each one commits small.
An agent-native mind-map canvas. Muse reads your PDFs and draws editable maps live before your eyes — skeleton first, surgical node edits by id, and a two-way canvas that sees what you changed and builds on it.
The earlier open-source take on the same idea — an AI agent that reads your PDFs and draws editable mind maps, with a visible tool-calling loop and built-in RAG (no LangChain). Its lessons became Inkling.
Previewable Flutter animations — real running Flutter web, not GIFs — each one paired with its traceable pitfalls and a parameter playground. Reachable from Claude Code or Cursor as a remote MCP tool.
A multi-provider AI image playground — text-to-image, edit, inpaint. Entirely client-side: bring your own API key; nothing ever touches my server.
Convert an API's documentation into a working MCP service, so AI coding IDEs — Claude Code, Cursor — can call your APIs directly, without you writing any plumbing.
A deliberately minimal tennis scorer — offline-first and installable, with no account and no network required. Games, sets, and tiebreaks are tracked with state kept entirely on-device, so it never drops a point courtside.
Work landed in projects I use every day — small, defensible patches to codebases many multiples my size.
The best way I know to understand a tool is to write a small, honest version of it. Each of these is short enough to read in one sitting and complete enough to run.
↳ github.com/xianjianlf2/<name>