Using an orchestration layer to cut Codex token costs
A heavy Codex user describes a setup for letting AI build projects with less hands-on supervision. The main idea is to have the strongest Codex model create the plan, then let an carry out much of the work with Deepseek in the middle because it is cheaper than OpenAI. This can reduce OpenAI token use while still using Codex for the higher-value planning step.
The setup depends on a strong with logging, validation, and , so the work can be checked instead of blindly trusted. A worker system across several household computers is also mentioned, with tasks delegated between machines to save resources such as CPU, RAM, and GPU power.
Key points
- Use the best Codex model to draft the project plan.
- Use Deepseek for some execution steps to reduce OpenAI .
- Put an between planning and execution.
- Add logging, validation, and so the system can catch problems.
- A worker system can spread tasks across several computers to save CPU, RAM, and GPU resources.