John Coltrane's A Love Supreme moves through four movements — Acknowledgement, Resolution, Pursuit, and Psalm — each one a phase in a single sustained act of devotion. The AI Edge borrows that structure because learning has the same shape. You begin by watching and listening, acknowledging what others have understood. You move to reading, resolving to engage deeply with the work. You pursue your own making, building things that ship. And eventually you arrive at the Psalm — the moment when the practice has shaped you enough that a credential is the natural next step.
"Nothing is a mistake. There's no win and no fail. There's only MAKE."
— Sister Corita Kent, Rule 6 (popularized by John Cage)
I. Watch
Listen · absorb
Short, curated viewing. Talks and videos that resolved something for drC — a concept, a debate, a way of seeing the field. Not a feed. Not exhaustive. Entry points.
drC's picks
Andrej Karpathy — "How to jumpstart your own learning." Watch this first.
3Blue1Brown — Visual math for transformers, attention, neural nets. Chapters 5–7.
Matthew Berman — AI tech and interviews with the movers and shakers. Dylan Patel on the state of the GPU stack.
Dwarkesh Patel — Long-form interviews with top researchers. Dense. Re-listen with the notebook open.
The Scaling Hypothesis — Gary Marcus vs. Dario Amodei. The defining debate in AI right now.
See the full watch list →II. Read
Deepen · refine
Short, curated reading. Papers, essays, and posts worth the time. Each one earns its place — drC reads it, marks it up, and tells you why it matters. Click any title to go to the source.
drC's picks
The Batch — Andrew Ng's weekly newsletter. Clearest signal in AI.
One Useful Thing — Ethan Mollick. Applied AI for normal humans.
Import AI — Jack Clark. Research and policy. Dense.
Towards Data Science — Medium publication. Quality varies; the best pieces are excellent.
arXiv — Papers feel dense because they are. Search "AI" or specific topics. Use the abstract-to-Claude trick.
See the full read list →III. Make
Build · ship · learn
Pick something you care about, or something you want to learn. Then make it. Watch and read are inputs. Make is where the learning actually happens.
drC's approach
Pick a medium — code, data, research, IoT, or media. Whatever you can ship.
Use AI as a collaborator — Claude drafts, debugs, explains. You steer.
Push it to GitHub — code, design files, writing, all of it. README and screenshot.
↻ Loop when stuck — describe the problem to Claude, let it ask the clarifying questions.
See the full MAKE guide →A closing note
The notebook habit. Get a paper notebook. Not digital — the friction matters. For every video, podcast, article, or chapter, log one idea worth keeping, three terms to look up, and one question you're still chasing. Ten minutes a day. Non-negotiable.
IV. Certify
Arrive · certify
The CCA is Anthropic's professional certification for engineers who build production AI systems. Five exam domains — agentic architecture, tool design and MCP, Claude Code, prompt engineering, and context management — tested through scenarios that demand real architectural judgment, not memorized vocabulary. It's the first credential that maps to where AI engineering jobs are actually moving.
Build to Certify is the eight-week program that prepares you for it. Same MAKE philosophy as everything above. Same notebook habit. Same loop when you're stuck. The difference is that at the end, you have a portfolio that qualifies you to sit for the exam — and a credential that signals you didn't just learn AI, you built with it.
See Build to Certify →