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TL;DR * Claude Code is a terminal-based coding agent from Anthropic that runs autonomous, multi-file changes from the command line; Cursor is an AI-native IDE built on a VS Code fork with inline editing, tab completion, and an agent mode. * Choose Claude Code for long-running refactors,

Bifrost and Portkey compared on request overhead, self-hosted deployment options, governance controls and MCP and agent support.

TL;DR * AI cost optimization is the practice of measuring, routing, caching, and capping LLM spend so a team pays for the output it needs instead of the tokens it wastes. * The highest-return tactics, in order, are: meter spend per workflow, route each task to the cheapest capable model,

TL;DR * Coding agents bill by token, so the real cost of running them at enterprise scale is driven by context size, redundant calls, and model choice, not by seat licenses. * Connecting 8 to 16 MCP servers to a coding agent loads every tool definition into context on every turn;

TL;DR * LLM infrastructure is the full set of compute, serving, data, routing, and operational systems that take a large language model from a model file to a reliable production service. * The stack has five layers: GPU compute, model serving and inference, the retrieval and data layer (vector databases), the

A guide to attributing LLM tokens, requests and cost to the team that generated them, and the six tools and approaches that do it.