New GitHub Repo Offers Framework-Free RAG and Agent Notebooks
A new open-source project called AI Engineer Notebooks has surfaced on Hacker News, offering a set of Google Colab notebooks that walk through building retrieval-augmented generation (RAG) pipelines, AI agents, and evaluation systems from scratch.
Unlike many tutorials that lean on frameworks such as LangChain or LlamaIndex, this repo strips things down to raw code, making it easier for developers to see exactly how each piece works under the hood rather than trusting abstractions they don't fully understand.
The notebooks are runnable directly in Colab, so anyone can experiment without local setup, and the project appears aimed at engineers who want a practical, hands-on primer rather than another high-level explainer.
Why it matters: Framework fatigue is real in the AI engineering community, and many developers find that debugging LangChain internals is harder than just writing the retrieval and prompting logic themselves. Resources like this help engineers build genuine intuition for how RAG and agent systems actually function, which pays off when frameworks break or need customization in production.