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Practical guides on the tools developers use every day — JSON, regex, JWT, cron, and more.
12 posts matching
A hands-on guide to building a practical AI developer stack for 2026: models, agents, orchestration, and observability with copy-paste code.
Assesses realistically what parts of a developer's daily workflow AI agents can handle today and where human judgment still has to lead.
A hands-on guide to choosing and integrating the top 5 AI models for developer agents, with code and config examples.
Learn which parts of code review to automate with AI and which to keep human, with practical examples and a step-by-step setup.
Learn a practical workflow to map, query, and understand a large codebase using AI tools, with commands and configs you can run today.
Lays out the decisions you should make before building an AI agent, including task scope, tool boundaries, and how you'll know it's working.
Explains what separates an AI agent from a chat interface: a decision loop, tool access, and the ability to take real actions on your behalf.
Learn to build an AI agent that writes failing tests before implementation, using a test-first loop with real code examples.
Compare Claude Code, Cursor, and AI IDEs hands-on: agentic workflows, terminal commands, and configs to boost your daily dev loop.
Learn why small language models are winning for developers: lower cost, privacy, and speed. Get hands-on with local LLMs, API routing, and cost measurement.
Learn how to calculate the true cost of AI models including latency, retries, and token waste, with a script to compare providers.
Discover what makes AI agents practical in 2026: planning, tool use, and feedback loops. Learn to build safe, cost-controlled agent workflows.