• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

A practical, end-to-end walkthrough of training a working LLM on your own machine — covering the tokenizer, GPT architecture, training loop, and inference — so you understand the full stack rather than just calling an API. Its local, resource-constrained focus makes small-scale training approachable without cloud infrastructure.

• 0:00Introduction and background of the speaker
• 1:21Overview of the workshop objectives
• 3:12Inspiration from Andre Karpathy's NanoGPT
• 4:37The four fundamental building blocks of an LLM
• 7:08Prerequisites and setup tools (UV, Python, hardware)
• 9:06Part 1: The Tokenizer (character-level tokenization)
• 24:29Model architecture and parameters
• 30:13The GPT class structure and transformer blocks
• 37:52Parameter count and model sizing
• 40:54The training loop: next-token prediction
• 44:44Optimization and learning rate strategies
• 47:56Validation and monitoring loss
• 53:07Part 3: Text generation and inference strategies
• 56:30Putting it all together (project file structure)
• 58:46Monitoring training and debugging common issues
• 1:00:27Workshop challenge and competition details
• 1:05:24Q&A: Base models vs reasoning models
• 1:11:31Q&A: Applying concepts to audio and multimodal models

• videoYour company brain will leak secrets: how we stopped it for big banks — Tanmai Gopal, PromptQL
• videoTethered: Our Agents Are Us — Shu Fang, Two Sigma
• videoAgents' next frontier: agent-to-agent and network effects — Jean-Denis Greze, Town
• videoEveryone Gets A Software Company — Benjamin Guo, Zo Computer

• articleCoding LLMs from the Ground UpSebastian Raschka, PhD
• podcastReiner Pope – The math behind how LLMs are trained and servedDwarkesh Patel
• repoCoding LLMs from ScratchPrince Canuma

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