Tutorials & Code
Stanford’s TRACE Explained: Turning AI Agent Failures Into Training Environments
Stanford's TRACE diagnoses exactly which capabilities your AI agent lacks, builds one verifiable synthetic environment per gap, trains a LoRA expert for each, and routes tokens between them. Full technical breakdown.
CrewAI Tutorial (2026): Build a Multi-Agent AI Team With Working Code
Build a three-agent AI team — researcher, writer, editor — in ~60 lines of Python...
Run LLMs Locally With Ollama: The Complete 2026 Guide
Free, private, offline AI on your own machine: install Ollama, pick the right model for...
Fine-Tuning vs RAG vs Prompt Engineering: How to Choose in 2026
The most expensive question in applied AI, answered with a decision flowchart: when prompting is...
MCP Explained: The USB-C Port of AI — How Model Context Protocol Works (With Code)
Why every AI tool now advertises MCP support: the architecture, tools vs resources vs prompts,...
LangChain 1.x Tutorial (2026): A-to-Z Guide With Working Code
The complete LangChain 1.x tutorial: create_agent, tools, memory with checkpointers, RAG-as-a-tool, streaming, middleware and LangSmith...
React 19 + AI: The Complete 2026 Guide to Building AI-Powered React Apps
React 19.2 + the AI SDK is the fastest way to ship AI products in...