Open Weight Agents (How Kimi, GLM, DeepSeek, Qwen, Llama, and Open Models Compete with Claude, Codex, and Cursor) by Paul Brown

You picked up this book because the headlines will not stop. Kimi ships a trillion parameter model, DeepSeek publishes its reasoning weights, GLM drops an MIT licensed giant, Qwen powers 1000s of deployments, and Llama makes open weights a household name. Someone says open models now rival Claude, someone else says you still need the expensive tools, and you are left paying 2 subscriptions while wondering if any of this is actually usable for the work you do.
Or maybe you want to cut costs, protect privacy, or run a model on your own machine without asking permission from a vendor. You do not need a PhD and you do not need another benchmark table. You need a practical map that translates hype into a stack you can run today on hardware you already own.
This book is not a worship of open weights and it is not a dismissal of Claude Code, Codex, and Cursor. It is not a promise that 1 model wins at everything, and it is not a tutorial that assumes you already code for a living. What you need is a clear, honest framework for choosing, running, and verifying, with the cost and privacy questions answered up front.
Inside, you will discover:
• What open weight really means, and why it is not the same as open source
• The 5 families that matter, and the job each 1 is actually built for
• How to separate the harness from the model and run open models inside closed tools
• A 4 step build loop that turns any model into results you can verify
• A 5 axis decision framework for picking without paralysis when releases fly weekly
• How to run quantized models on a laptop with 16 GB to 64 GB of memory
• The true cost math, from $0 local to cheap APIs to premium subscriptions, so you stop overpaying
• How to build a small research agent that reads and organizes your documents privately, offline