While working on a marketing project from 2016 to 2019, I finally had enough spare time to build some library projects and explore open‑source solutions. In retrospect, it was one of my most productive stretches.
Harvard, Andrew Ng, and Karpathy will teach you AI engineering for free. Most people just do it in the wrong order:
Almost all of it is free, and the order matters as much as the resources.
1. Start with Python. It's the language the AI field runs on, and Harvard's CS50P teaches it better than most paid bootcamps.
2. Once the basics click, learn how Python is used in AI. Andrew Ng's "AI Python for Beginners" is a free four-part course that bridges writing code and building with models.
3. From there, get a feel for how LLMs work under the hood. 3Blue1Brown's visual explainers make transformers and attention click.
4. When you want to go deeper, build a small model yourself. Andrej Karpathy's "Zero to Hero" series takes you from one neuron to a working model, line by line.
5. Next, learn how AI agents actually work. Anthropic's "Building Effective Agents" is the most grounded guide, and its lesson is to use composable patterns, not heavy frameworks.
6. For hands-on practice, take the CrewAI short course. It teaches you to treat agents like a team of people working together.
7. After that, connect your agents to the real world. That's what MCP does, wiring models to tools, APIs, and databases, and the official docs are the cleanest place to start.
8. Now build real projects. The open-source ai-engineering-hub repo has dozens of working examples across LLMs, RAG, and agents you can adapt into your own work.
9. Finally, read one book instead of ten. Chip Huyen's "AI Engineering" covers what you need to ship real applications.
The throughline is simple. Frameworks come and go, so don't build your skills around them. Master the fundamentals once, and everything on top gets easier, and you'll stay ahead of the people chasing the framework of the week.
A nice Chrome extension keeps tabs on your tabs. Turn your "New tabs" page into a mission control, so you can close them easily.
https://t.co/foI0SAsaCW
I'm joining OpenAI next week!🥹 The job search turned out to be really challenging but also super rewarding, so I wrote a small blog to share what I learned along the way and hopefully make the process a little less mysterious for the next person. https://t.co/6FigSBdenD
A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE
90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you.
-> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other.
Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why.
Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read.
Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks.
Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓
The Algorithm Visualizer is my favorite tool for explaining algorithms while teaching.
The accompanying quick explainer is also quite useful.
https://t.co/1Z1Pztb94Y