Learning resources
Depth over breadth — pair every concept with building something.
Curated, not exhaustive. Pick ~1 book/month, but pair every concept with shipping an artifact — you learn this role by building, not just reading.
Key ideas
- 1
Leadership: The Staff Engineer's Path (Reilly) first; Staff Engineer (Larson) + lethain.com; Influence Without Authority; Team Topologies; Switch / Made to Stick.
- 2
AI engineering & evals: AI Engineering (Chip Huyen); Designing ML Systems; Anthropic's “Building effective agents”; Hamel Husain on evals; try promptfoo / Braintrust / LangSmith.
- 3
Security & responsible AI: OWASP Top 10 for LLM apps; NIST AI RMF; ISO/IEC 42001.
- 4
Governance: EU AI Act + insurance guidance; DORA overview; your own company's model-risk & privacy policies.
- 5
Architecture: Designing Data-Intensive Applications (Kleppmann); Fundamentals of Software Architecture.
Read with intent
Consume ~1 book/month, but always pair a concept with building something — the eval harness, the gateway POC, a reference implementation.
Follow practitioners
- Simon Willison (simonwillison.net), Latent Space, Eugene Yan, Chip Huyen, the Anthropic engineering blog.
- Will Larson (lethain.com) for staff-level leadership and org design.
- Hamel Husain for practical, opinionated LLM evals.
Watch
Reading checkpoint
Finished the ideas above? Bank the reading progress before applying them.
Do the work
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What's the recommended way to actually consume these resources?
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