Tech for Builders
Everybody can build these days. Writing code was never the moat, and code generation is only going to get more automated from here. But if you do not understand the fundamentals of how software actually works, there is a cap on what you can build and how far you can take it. That knowledge does not go away as tools get smarter. It becomes the difference between assembling software and engineering it, the same way understanding the principles of design separates decoration from good product work. Seven modules, 39 lessons, read in order.
Module 0 How software actually works
0/4Module 1 Understanding the layers
0/51.0Where code runs, and what it turns into1.1HTML, CSS, JavaScript: what each layer actually does1.2Reading React, properly: from zero to genuinely fluent1.3Reading backend logic: routes, controllers, and the path of a request1.4The browser is your debugging tool: reading the console and network tab
Module 2 APIs
0/4Module 3 Databases
0/73.1What a database is, and why its structure is a product decision3.2SQL: the four queries every builder must be able to read3.3Relationships and data modeling: the skill that separates good builders from bad ones3.4Why queries get slow: indexes, N+1, and changing a schema safely3.5SQL, NoSQL, and object storage: choosing the right tool3.6How computers represent things, and the bugs that never go away3.7Database security: why an unguarded database is a public database
Module 4 Working on real code
0/64.0Getting a project running on your machine4.1Git: what it actually is, and why understanding it still matters4.2Branches, pull requests, and working on a codebase with other people4.3Testing, and the gate that protects the codebase4.4What deployment actually means: environments, builds, and configuration4.5When it breaks in production: reading logs and fixing things calmly
Module 5 System architecture
0/75.1Scalability: what actually breaks when ten thousand people arrive5.2When two things happen at once5.3Caching: the fastest work is the work you never do5.4Queues and async: how real backends handle slow work5.5Containers and Docker: solving "it works on my machine"5.6The CAP theorem: why there is no perfect database5.7Reading a system architecture diagram
Module 6 AI-native infrastructure
0/66.1How LLMs fit into a software architecture: not as chatbots, as components6.2Prompts are code: where they live, how they change, and why it matters6.3Agent harnesses: what they are and why the architecture is different6.4MCP: the integration problem, and the protocol that addresses it6.5Observability for AI systems: tracing, logging, and knowing what happened6.6Embeddings and vector databases: how AI systems find what's relevant
New modules land regularly
Module 6 never closes: as new patterns become part of real engineering, they get added. Drop your email and each new lesson reaches you first.
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