Four complete pathways from zero to professional — through the machine, distributed systems, system design and AI engineering. Every chapter is its own page: open one, finish it, take the next. Each ends with the best free reading on the web, linked straight to the people who wrote it. Walk a pathway in order, or jump to the chapter your work needs this week.
How the machine actually runs your code
From source code to silicon and back: execution, data representation, memory, the operating system, concurrency, networks, cost, and the mathematics behind ML. The slowest track to pay off, and the one that makes the other three legible.
Why two machines are more than twice as hard as one
Partial failure, communication, disagreement about time, replication, consensus, coordination — and how to test any of it. The theory track: everything in System Design leans on it.
From one server to a defensible architecture
The applied track, and the widest: requests, APIs, load balancing, caching, storage, transactions, sharding, streams, derived data, probabilistic structures, geo-indexing, IDs, files, realtime, background jobs, rate limiting, security, resilience, delivery — closing with a worked design and an atlas of the classic problems.
Shipping systems that use models, not just calling them
The whole arc: learning from data, the classical ML toolbox and deep learning fundamentals first — then transformers, serving economics, prompting, retrieval, customisation, agents, security and evaluation. Weighted towards building and measuring.