ai-engineer-notebooks MCP Server

From the README

# AI Engineer Notebooks

**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. You build working systems on top of foundation models (model APIs, RAG,
evals, agents, adaptation, serving) using raw APIs, not frameworks.

### What makes this different

- **Framework-free, on purpose.** You write the agent loop, RAG, and evals from
  raw API calls first, so you understand what LangChain/LlamaIndex actually do
  before you reach for them (and can judge when *not* to). Patterns are durable;
  wrappers churn.
- **Evals are the spine.** "Measure before you tune" is installed early and
  returns in every section. It's the habit that separates an engineer who shipped
  a system from one who built a demo.
- **Free to run, end to end.** Everything runs on the **free
  [Gro