Small, balanced or most capable? Match models to tasks by quality, latency and cost, build a tiny eval set, and route requests instead of picking one model for everything.
Give context, paste real errors, ask for a plan first, make done testable and review every diff. Practical habits that turn AI assistants into reliable pair programmers.
Prompt caching, trimming chat history, retrieval instead of stuffing, right-sizing models, output limits and batch APIs — a practical checklist to shrink your AI bill.
Connectors let AI assistants read and act in your real tools. How they relate to MCP, how OAuth scopes work, a safety checklist, and how to build one for your own product.
MCP is the open standard for connecting AI assistants to your tools and data. Learn hosts, clients and servers, tools vs resources vs prompts, and build a working server in Python.
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