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Written and curated here: courses from zero to professional, the engineering blogs worth following, and the papers that shaped software. No sign-in, no paywall.
Learning pathways
Four courses: Foundations, Distributed Systems, System Design, AI Engineering.
4 pathways · 56 stages mapped · 95 links outResearch papers, explained
20 landmark papers, from MapReduce to Raft, each in plain language with interactive demos.
4 fields · 20 papers · 20 interactive simsIndustry tech blogs
One reader over 90+ engineering blogs, filterable by company and topic, refreshed every six hours.
1,000 posts · 90+ engineering blogs · self-refreshingThe four learning pathways
1. Foundations
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 slo...
10 stages · about 2 hours2. Distributed Systems
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 Desig...
10 stages · about 2 hours3. System Design
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-index...
24 stages · about 4 hours4. AI Engineering
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, customis...
12 stages · about 2 hours