1/ LLM tokens are commodities. There are 29 models across 6 providers, prices change constantly, and there’s no structured feed tracking any of it. So we built one. https://t.co/ch4txI0V9q — the pricing oracle for LLM inference.
@ivanajpavlovic 20% improvement in elderly flu vaccine response on low-dose rapamycin analogs. That's the Mannick et al. result that kicked off the human trials.
@maxmarchione The evidence hierarchy for longevity interventions is pretty clear: exercise > sleep > caloric restriction > pharmacological. Most people start from the wrong end.
@thanford7 Three pricing axes that determine your real bill:
- Input vs output (3-5x gap on most models)
- Sync vs batch (50% off batch on most providers)
- Context depth (kappa multiplier kicks in past 50K tokens)
@ds_nana_ 50% off batch on most major providers. If your workload tolerates a few hours of latency and you're running on sync endpoints, that's leaving half the bill on the table.
@metaviiii LiteLLM's pricing JSON had 39 deprecated models and 111 out of 302 had price changes in January alone. That's the state of the art for inference cost tracking.