Areas of exploration
What changes outside the API?
Models & deployment
Local and open-weight models, inference, deployment, and the tradeoffs between capability, control, and cost.
Questions in this area
- How do local and open-weight models behave under realistic hardware and latency limits?
- Which tradeoffs appear when model choice, inference settings, and task context change?
- How can comparisons account for quality, cost, privacy requirements, and operational control together?
What useful evidence looks like
Deployment comparisons need the model version, hardware, inference settings, and workload to be stated together. Quality measurements should sit beside latency, resource use, and the effort required to operate the system. Repeated runs and documented conditions help distinguish a useful result from a one-off demonstration. This area connects model capability to the environment in which it must work.
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