Engineering Fluency OS
From zero to system-level architectural fluency. Master the smallest set of concepts that unlock the largest mental leverage — one idea at a time.
Domains
Each domain is a self-contained curriculum. Complete them in order, or jump to what you need.
Core concepts every programmer needs — variables, control flow, and functions.
The building blocks of efficient computation — arrays, trees, graphs, sorting, and searching.
Track changes, collaborate on code, and ship with confidence using Git and GitHub.
How computers manage processes, memory, and I/O under the hood.
From bare metal to boot — CPUs, memory, storage, and how hardware becomes software.
How computers communicate — protocols, DNS, HTTP, TLS, and load balancing.
Storing, querying, and managing data — relational models, indexes, transactions, and replication.
Architecting scalable systems — client-server, microservices, caching, and observability.
Building reliable systems across multiple machines — consensus, replication, and fault tolerance.
Shipping and operating software — CI/CD, containers, observability, and incident response.
The math behind computing — linear algebra, probability, optimization, and calculus essentials.
Teaching computers to learn from data — supervised learning, loss functions, and model evaluation.
Neural networks, transformers, and large language models — from perceptrons to prompt engineering.
Number sense, operations, fractions, and the foundation all mathematics builds on.
Variables, equations, and learning to think abstractly about quantities.
Shapes, space, measurement, and the first encounter with formal reasoning.
Propositions, logical connectives, truth tables, and proof techniques.
Advanced functions, exponentials, logarithms, and preparing for calculus.
Angles, triangles, unit circle, and periodic functions.
Limits, derivatives, integrals, and the Fundamental Theorem of Calculus.
Integration techniques, sequences, series, and Taylor expansions.
Vectors, matrices, transformations, eigenvalues — the math behind ML and graphics.
Partial derivatives, multiple integrals, vector fields — calculus in higher dimensions.
Counting, distributions, hypothesis testing, and Bayesian reasoning.
Modeling change over time — ODEs, systems, and the Laplace transform.
Counting, graphs, recurrences, and number theory — the math of CS.
Rigorous foundations — epsilon-delta, convergence, and the structure of the real numbers.
Groups, rings, fields — the deep structure beneath arithmetic and beyond.