index 6438dee..b96b664 100644
@@ -26,6 +26,12 @@ how to develop these: **build 3 real projects.** not tutorials, not clones, not
after these three, you'll have the meta-skills. the specific technologies don't matter nearly as much.
+## my path
+
+I started with p5.js in 6th grade — just making visual things because it was fun. moved to ML in 8th grade, not because "ML is hot" but because I had a specific question: can you classify anesthetic depth from EEG signals? that question led to a published research paper (CNN anesthetic depth classifier).
+
+the pattern: **a specific problem pulled me into each new domain.** I never sat down and said "I should learn ML." I said "I need to classify EEG signals" and ML was the tool that could do it. the learning was a side effect of the building.
+
## self-directed learning vs. courses
my take: **no courses. pull from resources, build things.**
@@ -45,33 +51,6 @@ that said, here's how I'd use structured resources:
- read source code. find a well-written open source project in your area and read how they solved the problems you're struggling with.
- read papers. once you're past the basics, academic papers are where the cutting edge lives. arxiv.org for CS/ML, Google Scholar for everything.
-## technical depth tracks
-
-### ML / AI
-- **start:** fast.ai course → build a classifier for something you care about
-- **go deeper:** Andrew Ng's Stanford CS229 (MIT OCW equivalent) → implement papers from scratch
-- **go even deeper:** read papers on arxiv daily. pick one paper a week and implement it.
-- **the 2024+ reality:** with LLMs, the game has changed. you don't need to train models from scratch for most applications. the skill is knowing how to use APIs, fine-tune, build RAG systems, and build agentic workflows. the people building AI applications right now are winning.
-- **my path:** I started with p5.js in 6th grade, moved to ML in 8th grade, published an EEG research paper (CNN anesthetic depth classifier). the key was always having a specific problem to solve — not "learn ML" but "can I classify anesthetic depth from EEG signals?"
-
-### web dev / product building
-- **start:** build a personal site. HTML/CSS/JS. deploy it.
-- **go deeper:** pick a framework (React/Next.js is the current default). build a full-stack app with auth, database, and deployment.
-- **go even deeper:** learn infrastructure. how does DNS work? what's a CDN? how do databases scale? what's a message queue?
-- **the builder's path:** React/Next.js → Vercel for deployment → Supabase or Postgres for database → Stripe for payments → you now have everything you need to build and charge for a product.
-
-### hardware / embedded
-- **start:** Arduino. build something physical that responds to the world.
-- **go deeper:** Raspberry Pi. ESP32. learn about sensors, actuators, serial communication.
-- **go even deeper:** PCB design (KiCad), 3D printing for enclosures, embedded C/C++.
-- **the reality:** hardware is harder than software because debugging is harder and iteration is slower. but hardware projects are more impressive because fewer people do them, and the problems are more constrained (which can actually make them easier to reason about).
-
-### research
-- **start:** read papers in an area that interests you. take notes. try to reproduce results.
-- **go deeper:** find a gap in the literature. can you improve on an existing method? can you apply it to a new domain?
-- **go even deeper:** reach out to a professor whose work you've read. ask specific questions. propose a collaboration.
-- **my path:** I read papers on EEG classification, found an approach I thought could work better, built it, tested it on real data, and wrote it up. the key was having a specific hypothesis, not just "doing research."
-
## the "vibe coding" path
this deserves its own section because it's genuinely new.
@@ -92,12 +71,8 @@ my approach: use AI tools aggressively for boilerplate, styling, and things you'
## key books and resources
-I'm not going to give you a reading list of 50 books. here are the ones that actually matter:
-
- **"Hackers & Painters" by Paul Graham:** the essay collection that defines builder culture. read at least "Hackers and Painters" and "How to Make Wealth."
-- **"Zero to One" by Peter Thiel:** contrarian thinking about startups and innovation. you'll either love it or hate it, but you should read it.
- **Paul Graham's essays (paulgraham.com):** the single best free resource on startups, building, and thinking clearly. start with "Do Things That Don't Scale" and "How to Get Startup Ideas."
-- **"The Lean Startup" by Eric Ries:** MVP thinking. build → measure → learn. the methodology that most modern startups use.
- **"Designing Data-Intensive Applications" by Martin Kleppmann:** when you're ready to understand how real systems work at scale. this is the book.
## the meta-advice