How a Transformer Plays Tic-Tac-Toe
An interactive guide to the architecture behind modern language models. Instead of predicting the next word, this Transformer predicts the next move i…
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How Modern Transformer Blocks Work — From RMSNorm to MoE
The original Transformer idea is still alive. But modern LLM blocks are not just the 2017 Transformer copied and scaled. They are engineered for deepe…
Why KV Cache Matters — How MQA, GQA, and MLA Make LLM Inference Faster
LLMs generate text one token at a time. That sounds simple. But without KV Cache, every new token would repeat a lot of old work. That is why inferenc…
Why Attention Becomes the Bottleneck — And How Efficient Attention Fixes It
Your model got smarter. But suddenly it got slower. Why does increasing context length explode compute? Because attention is O(n²). And that becomes t…
How Self-Attention Works — QKV, Softmax, and Matrix Computation
Self-Attention is not just “looking at important words.” It is a matrix operation. And that is exactly why Transformers scale. Core Idea Self-Attentio…
[Day 7] Does Giving an AI More 'Thinking Time' Really Make It Smarter? Training an OpenMythos-Style Mini Model on DGX
[Day 7] Does Giving an AI More "Thinking Time" Really Make It Smarter? Training an OpenMythos-Style Mini Model on DGX Intro Day 7! Reddit kept surfaci…