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 →The AI Hardware Stack, Explained: From GPUs and HBM to Neoclouds
Every model you use runs on a stack of physical parts: accelerators, stacked memory, advanced packaging, optical wiring, and the GPU-first clouds that rent them out. Here is what each piece is, what it decides, and why the names in the headlines (GB200, HBM, CoWoS, RISC-V) actually matter to anyone building on AI.
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 →Cursor vs Windsurf: How to Pick an AI Code Editor in 2026
Two AI editors, both forks of VS Code, both with serious agents inside them. One was bought by SpaceX; the other became Devin Desktop. Here is what each is actually good at, what they cost, and how to choose by the work you do.
Read the guide →AI Security for Builders: The Real Threat Model for LLM and Agent Apps
Most of what breaks an AI feature is not in your firewall. It is in the words your model reads and the tools you let it call. Here is the actual threat model for LLM and agent applications, grounded in the OWASP Top 10, and what to defend without security theater.
Read the guide →What Is MCP? The Model Context Protocol, Explained
MCP, the Model Context Protocol, is an open standard that lets any AI app connect to any tool or data source through one common interface. Think of it as a USB-C port for AI. Here is what it is, how the client and server model works, and how to use and build one.
Read the guide →What Is a Neocloud? The GPU Clouds Powering the AI Boom
A neocloud is a cloud provider built around one thing: renting GPUs for AI. Not databases, not general computing, just accelerators by the hour. Here is what they are, why they appeared, how their economics actually work, and how to tell a neocloud from a hyperscaler.
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