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AI Agents from First Principles

Understanding AI agents by building upon the most basic concepts of LLMs...
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Decoder-Only Transformers: The Workhorse of Generative LLMs
 • Cameron R. Wolfe, Ph.D.
14
Demystifying Reasoning Models
 • Cameron R. Wolfe, Ph.D.
3
Understanding and Using Supervised Fine-Tuning (SFT) for Language Models
 • Cameron R. Wolfe, Ph.D.
5
Mixture-of-Experts (MoE) LLMs
 • Cameron R. Wolfe, Ph.D.
10
A Guide for Debugging LLM Training Data
Data-centric techniques and tools that anyone should use when training an LLM...
 • 
Cameron R. Wolfe, Ph.D.
1
Llama 4: The Challenges of Creating a Frontier-Level LLM
The full story behind Llama 4 and Meta's huge pivot in research strategy...
 • 
Cameron R. Wolfe, Ph.D.
2
Vision Large Language Models (vLLMs)
Teaching LLMs to understand images and videos in addition to text...
 • 
Cameron R. Wolfe, Ph.D.
11
nanoMoE: Mixture-of-Experts (MoE) LLMs from Scratch in PyTorch
An introductory, simple, and functional implementation of MoE LLM pretraining...
 • 
Cameron R. Wolfe, Ph.D.
12
Demystifying Reasoning Models
Understanding reasoning models and their relation to standard LLMs...
 • 
Cameron R. Wolfe, Ph.D.
3
Mixture-of-Experts (MoE) LLMs
Understanding models like DeepSeek, Grok, and Mixtral from the ground up...
 • 
Cameron R. Wolfe, Ph.D.
10
Scaling Laws for LLMs: From GPT-3 to o3
Understanding the current state of LLM scaling and the future of AI research...
 • 
Cameron R. Wolfe, Ph.D.
8
Deep (Learning) Focus
Deep (Learning) Focus
I contextualize and explain important topics in AI research.
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