AI_Tutorial

Why we included this project

This is a hand-curated index of AI practice notes rather than another framework or library. The maintainers collect engineering write-ups from big tech companies including Alibaba, Meituan, ByteDance, and the FAANG crowd, then mix in fresh arXiv and Papers With Code papers along with notes from technical communities, updating the collection daily. What earns it a bookmark is the filtering: only content with substantive engineering or academic value gets in, and hype pieces and marketing posts are explicitly left out, so every entry keeps a link back to its original source. For someone tracking how production search, recommendation, and LLM agent systems actually get built, it is a quick way to surface relevant material without drowning in feed noise, and the first-hand accounts of what went wrong in real deployments are often the most useful part.