// HACKER NEWS — CYBERSECURITY
AI Engineer Notebooks – free, framework-free RAG/agents/evals on Colab
Learn the applied-LLM stack the way you'll actually be interviewed on it —
framework-free, on a free API, from prompting all the way to serving, fine-tuning,
and a red-team benchmark.
Runnable Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE)
skill set: building working systems on top of foundation models — model APIs, RAG,
evals, agents, adaptation, serving — using raw APIs, not frameworks.
Built as the hands-on companion to
Plan: Transitioning to Forward Deployed Engineer / AI Engineer.
The plan explains what to learn and why; these notebooks are where you run it.
Backend or full-stack engineers moving into AI Engineer, FDE, Applied AI,
or Solutions Engineer (AI) roles — different titles, largely the same job.
You can ship production code; you want the applied-model layer on top.
Work top to bottom. Each notebook is self-contained (installs its own
dependencies, reads API keys from Colab secrets) and ends with exercises.
Where the free Groq API can't run the topic (these frameworks need a GPU),
the notebook teaches it concept-first and fences an optional Colab-GPU
appendix — the same pattern as the section-06 LoRA appendix.
Where the skills come together into projects. First a case study — one
realistic scenario worked end to end, runnable — then the capstone, the
deployed repo you build yourself. (Section overview.)
Capstone: the brief for the deployed
project that goes on your resume — a real repo with a serving component and an
eval report. Case studies are for learning; the capstone is for hiring.
Running locally instead: pip install -r requirements.txt && pip install -e .
(the second installs the aien setup helper), export GROQ_API_KEY=...,
open with Jupyter.