Real AI agent use cases, with sources and setup prompts

How to cut AI coding agent costs and token use

Real setups that lower token bills and stop coding agents hitting usage limits. 24 matching use cases below, each with a public source.

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Get started in 3 steps

  1. Measure first: track your usage window and which tasks burn the most tokens before changing anything.
  2. Give the agent a search index or code map so it reads the right files instead of pasting large grep dumps into context.
  3. Route routine jobs to a cheaper model or a local one and keep the frontier model for planning and hard bugs.
Dev & opsHacker News · khalidkhair

Run a local model bigger than your laptop's memory

Janas streams mixture-of-experts weights from SSD so a 48 GB Qwen3-Next-80B model runs on a 32 GB laptop. The author measured 24-37 tokens per second writing versus 2.8 for llama.cpp on the same machine.

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