How to Run a Local LLM: A Complete Beginner's Guide
A local large language model runs entirely on your own hardware. No API keys, no per-token bills, no data leaving your machine. Here is what that means, what you need to run one, and how to start in about five minutes.
Read the guide →What Is an AI Agent? A Plain-English Guide to Agentic AI
An AI agent is a large language model that works in a loop: it reasons about a goal, takes an action with a tool, looks at what happened, and decides its own next step, repeating until the job is done. Here is how agentic AI actually works, what it is good and bad at, and how to build one.
Read the guide →How Reasoning Models Work: Test-Time Compute and the New Scaling Law
For years, AI got smarter by getting bigger. That playbook stalled. The breakthrough behind today's frontier models is a different knob entirely: let the model think longer before it answers. This is test-time compute, and it created a new class of reasoning models. Here is how they actually work.
Read the guide →How to Do Reinforcement Learning in 2026: A Practical Guide Using Claude
Reinforcement learning used to be a specialist's dark art: unstable, compute-hungry, and bottlenecked on the reward. In 2026 both hard parts got easy. Simpler algorithms and turnkey tools handle the training, and a strong model like Claude can write the pipeline and act as the grader that produces the reward. Here is how to actually run one.
Read the guide →What Is Mechanistic Interpretability? A Visual Guide to the Inside of a Neural Network
A modern AI is grown, not written: billions of weights that work without anyone being able to say exactly how. Mechanistic interpretability is the science of opening that black box, finding the concepts and circuits inside, and proving what they do. Here is the field, in pictures.
Read the guide →