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Practical guides on the tools developers use every day — JSON, regex, JWT, cron, and more.
45 posts matching · page 1 of 4
Breaks down the think, act, observe loop that powers most AI agents, and explains what actually determines when it stops.
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.
Explains how AI agents break a high-level goal down into a sequence of concrete actions, and where planning approaches differ.
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.
Explains how to build uncertainty awareness into an AI agent so it escalates to a human instead of guessing when it should not.
Offers a practical framework for scoping what data and systems an AI agent should be allowed to touch, based on risk and reversibility.
Learn to build a local AI coding assistant with Ollama, Continue, and custom tools. Step-by-step setup, config, and troubleshooting.
Explains the layers of guardrails that keep AI agents from taking destructive or unauthorized actions, from permissions to runtime checks.
A hands-on guide to building a practical AI developer stack for 2026: models, agents, orchestration, and observability with copy-paste code.
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