What AI practices does this site document?
Three layers — study notes on transformers and deep learning, source-reading notes on agent mechanics (agent loop, tools, context compression, multi-agent collaboration), and small tools built from them, such as EnCoder and QQbot.
AI is the main line here, and the writing sits in three layers, from mechanism to something that runs.
The first layer is fundamentals: the transformer notes walk through tokens, positional encoding, Q/K/V attention, multi-head attention, and layer norm in data-flow order, while Simple_CNN reproduces the path from convolution and pooling to a full training run.
The second layer is agent engineering: reading three Claude Code implementations side by side (the notebook) lines up the agent loop, tool definitions, context injection and compression, parallel tools and sub-agents, multi-agent collaboration, and security boundaries. Minimind-notes covers model architecture and training methods.
The third layer is tooling that runs: EnCoder extends a minimal agent with cron scheduling, long-term memory, an agent team, and task management; QQbot uses pi as its brain and NapCatQQ as its connection, covering knowledge Q&A, timetable lookup, group management, long-term memory, and hot model switching. The project list is on the projects page.