[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1r61945r9t42l":3},{"code":4,"message":5,"data":6,"timestamp":46},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":16,"summary_md":17,"topic":18,"github_topics":21,"license":42,"homepage":43,"updated_at":44,"history":45},"rohitg00\u002Fai-engineering-from-scratch",6,"Learn AI Engineering! Learn it. Build it. Ship it for others.","Python",73010,12824,17479,219,[],"A 542-lesson curriculum that teaches you to implement, test and evaluate model internals, retrieval and agent runtimes yourself.","## The problem\n\nMost AI engineering material stops at calling an API. The internals that decide whether your RAG pipeline or agent loop actually works — tokenization, retrieval ranking, evaluation loops — stay opaque, and when something regresses there is no basis for diagnosing it. This project addresses that by making you implement the pieces and keep the code and evaluation output alongside them.\n\n## What's different\n\nTypical tutorials give you a runnable notebook and a happy-path result. This one is organised as 20 phases and 542 lessons, with staged project starters that fail until you implement them, reference implementations, and local graders — so correctness is checked rather than assumed. Its retrieval evaluation project explicitly asks for per-query regression reporting, not just a mean metric, which is closer to what release checks in production actually need. It spans Python, TypeScript, Rust and Julia, and offers three entry routes (model foundations, LLM systems, agents and delivery) instead of a single linear track. The trade-off is that this is a study corpus, not a library: nothing here ships as a dependency in your app.\n\n## Who it's for\n\nDevelopers who already write code but have only used models through SDKs, and who want to understand transformers, retrieval and agent runtimes by building them. Also useful for engineers who inherited an eval suite and need grounding in ranking metrics. Not for people looking for a drop-in RAG framework or agent runtime — that's a different category of project. Beginners with no programming background will find the 542-lesson breadth overwhelming rather than helpful.\n\n## Cost of adoption\n\nThe README is detailed but mostly promotional: it states 542 lessons, 20 phases and four languages, and points at an external site and per-project run commands, but does not enumerate every dependency. Expect Python 3.10+ for the flagship Python track, and additional toolchains if you follow the Rust, TypeScript or Julia lessons. MIT licensed, so reuse of the material is unconstrained. The main risk is scope, not licensing: 20 phases is a large time commitment, and the project's maturity is unproven — it was created in March 2026 and is roughly seven months old, with no Hacker News discussion found.\n\n## Verdict\n\nThis is a young project riding a sharp popularity spike: it gained about 17,479 stars this month and ranked #17 on the monthly all-languages board, despite never appearing on the daily trending board. That pattern — fast star growth on a course repository with no HN discussion — suggests broad distribution rather than independent technical vetting. The structure and the failure-until-implemented project starters are genuinely more rigorous than the usual tutorial, but the breadth-and-numbers framing is a marketing signal as much as a quality one. Treat it as a self-study syllabus, sample its first phases before committing, and don't mistake its star count for evidence that the later lessons are uniformly deep. Worth your time if you want to build the internals; not a substitute for reading the papers or the source of real frameworks.",{"slug":19,"name":20},"learning","Learning Resources",[22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"agents","ai","ai-agents","ai-engineering","computer-vision","course","deep-learning","dsh","from-scratch","generative-ai","llm","machine-learning","mcp","python","rea","reinforcement-learning","rust","transformers","tutorial","typescript","MIT","https:\u002F\u002Faiengineeringfromscratch.com","2026-10-24T14:35:22Z",[],1792864286]