Ditch the spaghetti code. Learn how to use LangGraph to build resilient, stateful AI agents with built-in error handling and human-in-the-loop checkpoints.
Running large language models locally requires shrinking their file size without destroying quality. This guide walks through the full llama.cpp pipeline: downloading a Hugging Face model, converting it to GGUF format, and quantizing it to Q4_K_M or other levels to fit consumer hardware.
Boost your AI performance with vLLM and Docker. Learn to use PagedAttention, Tensor Parallelism, and quantization to scale LLMs for hundreds of concurrent users.
Optimize your Claude Code CLI experience by mastering the settings.json file. Learn how to automate command approvals, switch models to save costs, and set custom project instructions.
A practical guide to building a fully offline voice assistant using Whisper for speech recognition and Ollama for local LLM responses — no cloud APIs, no latency overhead, complete privacy. Based on six months of production experience with measurable performance data.
Is your AI getting confused by large projects? Learn how to use Claude Code's sub-agents to delegate tasks, prevent context saturation, and speed up your development workflow.
Transform your AI from a passive chatbot into an active agent. This guide shows you how to use the Claude SDK to handle tool calling, state management, and complex Python workflows.
Extend Claude Code's capabilities by building custom MCP skills. This guide shows you how to connect Claude to your internal tools and APIs with practical, production-ready patterns.