patchy631/ai-engineering-hub

A growing open repository of practical, code-forward tutorials aimed squarely at engineers building with large language models, retrieval-augmented generation pipelines, and autonomous agent systems. Rather than stopping at theory, each entry walks through real implementation decisions — how to structure a RAG pipeline, how to wire up an agent loop, how to handle retrieval quality issues that show up in production. The examples tend to be self-contained and runnable, which means you can pull one out, adapt it, and have something working in an afternoon rather than spending days translating concepts into actual code. The honest reservation is that this is a community-maintained collection, so depth and polish vary across tutorials. Some are thorough and production-minded; others feel more like quick experiments. You will need to triage before committing to a pattern it introduces. Still, for a founder or engineer trying to accelerate their own understanding of AI architecture choices without wading through academic papers, this is a genuinely useful shelf to have bookmarked. -> Best for: technical founders and solo AI engineers who learn by reading and running code, not slides.