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If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I've prepared a 1-hour presentation on the development cycle of LLMs, covering everything from architectural implementation to the finetuning stages.
Over 11 hours of coding videos on evaluation, inference-time scaling, reinforcement learning, and distillation, with Python and PyTorch.
A Look at Recurrent Depth, Hidden Chains of Thought, and Recent Research on Looping Transformer Blocks
And How They Stack Up Against Qwen3
Methods and Strategies for Building and Refining Reasoning Models
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design
Things I Learned From Hundreds of Experiments
How coding agents use tools, memory, and repo context to make LLMs work better in practice
A few months ago, I shared the article, Understanding Large Language Models: A Cross-Section of the Most Relevant Literature To Get Up to Speed, and the positive feedback was very motivating!
An introduction to the core ideas and approaches
This article will teach you about self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama.
I frequently reference a process called Reinforcement Learning with Human Feedback (RLHF) when discussing LLMs, whether in the research news or tutorials.