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Andrej Karpathy's talks and lectures (karpathy.ai/index.html) are recommended for…

2026-07-29 · 17:52 UTC ·@adxtyahq ·0 min read

Brief

Recommended resources for AI Engineering include Andrej Karpathy’s LLM and AI-software lectures, Anthropic’s engineering research on agents and MCP/prompt engineering, Glean’s blog posts on Context Engineering and the Enterprise Agent Stack, and Microsoft’s GraphRAG documentation to compare knowledge-graph approaches versus standard RAG.

Why it matters

Andrej Karpathy's talks and lectures (karpathy.ai/index.html) are recommended for deep dives on LLM internals and AI software design.

Key details

  • Anthropic’s Engineering & Research pages (anthropic.com/engineering) are highlighted for agents, multi-component prompting (MCP), prompt/context engineering, and tool use.
  • Glean’s engineering blog (glean.com/blog and glean.com/blog/emerging-agen…) and Microsoft GraphRAG docs (microsoft.github.io/graphrag…) are recommended to learn context engineering, the enterprise agent stack, and when knowledge graphs outperform vanilla RAG.
Source evidence

Some additional resources I'd recommend if you're diving deeper into AI Engineering / Applied AI:

  1. Andrej Karpathy's talks and lectures on LLMs & AI software
    karpathy.ai/index.html

  2. Anthropic Engineering & Research (agents, MCP, prompt/context engineering, tool use)
    anthropic.com/engineering

  3. Glean Engineering Blog, especially the posts on Context Engineering and the Enterprise Agent Stack
    glean.com/blog
    glean.com/blog/emerging-agen…

  4. Microsoft GraphRAG docs if you want to understand when knowledge graphs outperform vanilla RAG
    microsoft.github.io/graphrag…