cost control

Reasoning tokens are billed at the output rate and re-read every turn after. How to compress what a model says without compressing what it knows.

Your limit is a compute budget: tokens consumed times the model that reads them. Audit where a session spends, trim the context every chat preloads, and send grunt work to cheaper models so the budget lasts the day.

When a new model family ships, treat your routing as expired. Re-measure which size and effort give the most score per dollar, spot the size you can skip, and drop the speed modes that do not pay.

In ChatGPT, any chat can search every other chat, send work into them, and collect the results. How to find a lost conversation by asking, delegate work between chats, publish a result to a shareable page, and reach it all from your phone.

Cody Schneider builds agents that find leads and post content while nobody watches. The method: turn the human process into scripts on a schedule, and call the model only where a judgment is needed. Both of his builds, tool by tool.

Agent workflows run one step after another by default, which is the slowest shape. Ask at each step whether it needs the result of the step before it, then re-shape the work: fan out independent steps, route requests down branches, loop until a check passes, and cap what each shape can waste.