AI Arena

Practical guides on building with AI — agents, retrieval-augmented generation, LLMs, and the architectures that tie them together.

Machine Learning Basics

A hands-on series covering the core building blocks of every ML model — linear regression, weights and biases, loss functions, gradient descent, overfitting, activation functions, and neurons, ending at next-token prediction. 11 parts, each with an interactive demo you can run in your browser.
LLM Basics

A plain-language series on how large language models actually work — from the simplest feedforward network, through networks that read and remember, to attention, the transformer, and GPT vs BERT. 8 parts written for nontechnical readers, each with an interactive demo.
How LLMs Talk

A plain-language tour of everything between the model file and your chat window — inference engines, model servers, chat templates, thinking mode, tool calling, and agents. 8 parts following a single message end to end, written for nontechnical readers.
Inside the GPU

A beginner's tour of GPU hardware from the vacuum tube and the first transistor up to Nvidia's Ada, Hopper, Blackwell, and Vera Rubin. Logic gates, the memory wall, streaming multiprocessors, tensor cores, CUDA, NVLink, and how to read a spec sheet. 18 parts in the spirit of Inside the Machine, most with an interactive demo.
Building AI Agents

A hands-on series covering the full stack of AI agent development — LLM clients and chains, chunking, embeddings, query transformation, RAG strategies, tool calling, agent architectures, and guardrails. 9 parts, implemented with LangChain and LangGraph.