Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build an AI agent that takes error messages, traces through code to find root causes, generates fixes, and verifies them with regression tests. A practical guide to automated debugging.
Walk through building a complete calculator tool for an AI agent from scratch. Covers schema definition, safe expression evaluation, result handling, and integration with the agent loop.
Build an AI-powered calendar management agent that checks availability across time zones, resolves scheduling conflicts, and handles rescheduling workflows using the Google Calendar API.
Learn how to build an AI agent for car dealerships that searches vehicle inventory, schedules test drives, and generates finance quotes using tool-calling patterns and structured vehicle databases.
Learn how to build an AI agent that designs chaos experiments, controls blast radius, injects faults into production systems, observes behavior, and verifies automated recovery.
Learn how to build a production-quality chat interface for AI agents using React and TypeScript. Covers message bubble components, input handling, and smooth auto-scroll behavior.
Learn how to build a production-quality chat widget with a React frontend component, WebSocket backend in Python, message formatting, typing indicators, and persistent message history.
Learn how to build an AI agent that manages church service schedules, promotes events, handles prayer request submissions, and routes pastoral care needs appropriately.
Build an AI agent that translates natural language into shell commands, explains what each command does, asks for confirmation before executing dangerous operations, and learns from command history.
