Open-Source AI Engineering Curriculum From First Principles

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Source: instagram · Builders Central Saved: 20260531 Tags: instagram Display: Open-Source AI Engineering Curriculum From First Principles — A 416-lesson, 20-phase open-source curriculum teaching AI fundamentals from linear algebra to autonomous agents across four languages.

TL;DR

An open-source, 416-lesson AI engineering curriculum spanning 20 phases, covering everything from linear algebra and backpropagation fundamentals through transformers, PyTorch, and autonomous agent systems. It is structured around a four-step learning loop: read the problem, derive the math, write the code, and run the test. Addresses a gap in AI education where practitioners can use high-level AI frameworks and APIs without understanding the underlying mechanics (e.g., attention, backpropagation). The curriculum builds foundational understanding from first principles across four languages (PyTorch, TypeScript, Rust, Julia), targeting engineers who want to move beyond tool usage to genuine comprehension.

What the post showed

Key claims from transcript: Take a look at this, this is the most complete AI engineering curriculum I've ever seen. And now you all can shift AI apps in hours, but can you explain how attention actually works inside the module you're calling? No, right? So this guy, Rohit Kumaray, just to open source this AI engineering curriculum for all of us, 416 lessons in 20 phases. You'll start with linear algebra and then you build a

What it actually is

• What: An open-source, 416-lesson AI engineering curriculum spanning 20 phases, covering everything from linear algebra and backpropagation fundamentals through transformers, PyTorch, and autonomous agent systems. It is structured around a four-step learning loop: read the problem, derive the math, write the code, and run the test.
• Who built it / maintained by: Rohit Kumaray (individual open-source contributor; exact GitHub handle unverified — name may be a transcription approximation from video audio)
• Status: unknown
• Why it matters: Addresses a gap in AI education where practitioners can use high-level AI frameworks and APIs without understanding the underlying mechanics (e.g., attention, backpropagation). The curriculum builds foundational understanding from first principles across four languages (PyTorch, TypeScript, Rust, Julia), targeting engineers who want to move beyond tool usage to genuine comprehension.
• How it compares to alternatives:
• fast.ai Practical Deep Learning
• Andrej Karpathy's Neural Networks: Zero to Hero
• deeplearning.ai specializations
• CS231n (Stanford)
• The Little Book of Deep Learning
• GitHub stars: 0 · License: unknown · Archived: no

Links

• (no links found)

Kickstarter guide

Search GitHub for the author's username (possibly a variant of 'rohitkumaray') and a repository named something like 'ai-engineering-curriculum'. Once found, clone the repo and begin with Phase 1, which covers linear algebra fundamentals. Follow the prescribed four-step method per lesson — read, derive, code, test — and progress linearly through the 20 phases toward advanced topics like autonomous agent swarms.