The syntax barrier is gone. Now for the interesting part.
01
Pick a subject
Git, SQL, HTTP, Design Patterns. Your AI agent is already fluent in these. You decide whether to trust it.
02
Learn visually
Every concept has an interactive visualization. Manipulate inputs, see results instantly.
03
Play a position
You make the call, then watch the real consequence play out.
Knowledge has structure.
Foundation
Git
Branching, merging, rebasing, workflows, see what git actually does under the hood.
How Git Thinks
The object model, what git actually stores
Everyday Git
Staging, tracking, history, and setting aside work
Branching & Merging
Parallel work and bringing it together
Rewriting & Undoing
Amend, rebase, reset, bisect, and recovery
Collaboration
Remotes, pull requests, hooks, and team workflows
ML Foundations
What ML is, how data becomes predictions, when it fits a problem, and where it goes wrong, practical intuition, no heavy math.
What is Machine Learning?
Supervised, unsupervised, reinforcement, when ML works and when it doesn't
Data for ML
What kinds of data ML uses and why splitting matters
Supervised Learning
Regression, classification, trees, intuition not derivation
Evaluation & Model Selection
Why accuracy misleads and how to choose the right model
HTTP & Networking
DNS, TCP, HTTP, CORS, TLS, see what every request actually does between your code and the server. Read `curl -v` line by line.
Names & Addresses
IPs, ports, sockets, and how a hostname becomes a packet destination
Wire & Bytes
TCP handshake, connection lifecycle, UDP, and what packets actually carry
HTTP
Request lifecycle, methods, headers, caching, and HTTP/2-3
The Browser as Client
Same-origin, CORS, cookies, mixed content, the browser's invisible rules
TLS & Identity
Handshake, certificate chains, trust stores, mTLS