50B+ Tokens Later: A Practitioner's 6-Step Path Into Applied AI
brandon_galang · x · 2026-10-12
An engineer who made the leap into applied AI (after processing 50B+ tokens) shares the learning path he'd follow today, arguing you should stop over-preparing and learn through a real project.
His six priorities:
- Master the ReAct agent pattern
- Study what makes a good harness (he recommends starting with Pi)
- Experiment with models on real tasks to learn how price, capability, model family, size, and modality affect choices
- Learn embeddings and vector retrieval
- Learn state machines
- Use agents to build traditional software guardrails around parts where agent behavior varies
On methodology: when a project breaks, investigate with your agent — what happened, what did you misunderstand, what do you need to learn next. Give yourself a project that makes the answers matter.
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