20yo founder building ThermoAgent, patent pending AI infra that routes queries to the optimal model for the task, delivering reliable intelligence per dollar.
@ORNL We reached out to ORNL from Kingsport, TN. We want to test whether ThermoAgent’s AI control layer can extend into a datacenter control plane using utilization, queue state, power, energy and thermal telemetry to make better workload placement decisions w/o sacrificing reliability
@NVIDIAAI@JGrandillon If agents are becoming the software layer of every company, they’ll need an execution control layer underneath them, deciding which model/compute path to use, verifying results, recovering from failures, and tracking the economics. That’s where we fit.
@CrusoeAI@ollama I’ve built a provider-agnostic execution-control layer that routes workloads across cloud, local, and model environments based on quality, cost, latency, verification, and reliability. Crusoe looks like a natural infrastructure target. Open to testing it as one?
@NVIDIARTXSpark@ASUS I’d use it as a portable local inference node to test routing, verification, recovery, and model selection across cloud + local workloads. #ShareYourSpark
@NVIDIAAI@AIatMeta 816B is impressive, but not every coding task needs 816B parameters and a 1M+ token window. The interesting problem is deciding when that level of capability is actually justified, and when a smaller model can deliver the same result for less.
@_mctrinh That’s why I built ThermoAgent. If you use multiple LLMs, it pick the cheapest model that still meets the required quality/performance, and avoid wasting tokens on capability the task doesn’t need. Reliable intelligence per dollar.