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.
Start anywhere. It all connects.
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
Math for Machine Learning
Linear algebra, calculus, statistics, and probability, the mathematical language behind ML algorithms.
Linear Algebra
Scalars, vectors, matrices, and the directions that matter
Calculus for ML
Derivatives, gradients, and the chain rule, how models find the bottom
Statistics & Distributions
Variance, bell curves, and the shapes data takes
Probability
Bayes, conditional probability, and maximum likelihood, reasoning under uncertainty
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
Applied
Design Patterns
Observer, Strategy, Decorator, Factory, see them hiding in code you already write.
Behavioral Patterns
Observer, Strategy, State, patterns for runtime behavior
Structural Patterns
Decorator, Adapter, Facade, Proxy, patterns for combining objects
Creational Patterns
Factory, Builder, Singleton, patterns for object creation
Patterns in Practice
Beyond the catalog
.NET Platform
CLR, JIT, GC, DI, configuration, and the NuGet/MSBuild supply chain that decides which DLLs the compiler ever sees.
The .NET Runtime
How your code actually runs
Memory & the Type System
Stack, heap, GC, and the type machinery
Dependency Injection
The pattern that wires everything together
Configuration & Options
Strongly-typed config done right
Build & Distribution
Restore, metaproj, source mapping: the supply chain
Regex Engines
Patterns compile to graphs. Engines walk them by NFA, DFA, or backtracking. See why one regex is instant and another hangs.
Anatomy of a Pattern
Characters, classes, anchors, alternation, quantifiers, groups
The NFA
Thompson construction, state machines, the regex as a graph
The DFA
Subset construction, when engines convert, the speed trade
Backtracking & Catastrophe
Recursive descent, capture, backreferences, the patterns that hang
Beyond ASCII
Unicode properties, grapheme clusters, normalization, the multi-codepoint character
Why it's not a course
Tutorials teach the part AI already does. The interesting part is everything underneath.
The usual course
SkipNothing
Follow the steps, copy the code
Change the inputs, watch what breaks and why
Add an AI layer to the same old lessons
See the why behind what the agent builds
Learn what the agent already does
Build the understanding that makes it an amplifier