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Practical guides on the tools developers use every day — JSON, regex, JWT, cron, and more.
2,895 posts · page 1 of 242
Breaks down the think, act, observe loop that powers most AI agents, and explains what actually determines when it stops.
Learn the true hardware, power, and time costs of running AI models on your own machine, with commands to measure them.
Learn how to run a 30B parameter AI model on a consumer GPU with quantization, llama.cpp, and practical setup steps.
Clarifies whether function calling and tool calling are actually different concepts or just different names for the same underlying mechanism.
Explains the mechanics of tool calling step by step, from schema definition to execution, and why it is the foundation of every AI agent.
Learn why VRAM, not raw GPU speed, determines which local AI models you can run, and how to measure it with real commands.
Learn what local AI models, games, and apps you can realistically run on 8GB, 12GB, and 16GB VRAM GPUs, with benchmarks and setup commands.
Explains how AI agents break a high-level goal down into a sequence of concrete actions, and where planning approaches differ.
Learn to calculate VRAM needs for local LLMs, estimate with formulas, and test real models on your GPU.
Compares single-agent and multi-agent architectures directly on reliability, cost, and complexity to help you pick the right one.
Learn practical techniques to give AI coding assistants context about your codebase without exposing or uploading the entire repository, using local tools and selective file sharing.
Explains what multi-agent systems actually are beneath the marketing language, and what problems they solve that a single agent cannot.