Since Vitalify is an agent and not just a static checklist, it adapts. A standard app sees a missed workout as a “failure.” Vitalify sees it as data. If you tell the agent “I’m feeling under the weather” or “I’m swamped at work,” it doesn’t guilt you. Instead, it updates that day’s plan (e.g., swapping a workout for “rest & recover,” or scaling things down).
Appreciate that. We don’t integrate with wearables yet. We focused first on building the execution layer (ingestion → actionable plan → calendar + nudges). Wearable ingestion (Apple Health / Google Fit) is on the roadmap so trends like sleep/steps/RHR can adapt the plan, but we wanted the “what should I do next?” part to be solid before syncing more data.
The problem is that the 'genius' of Einstein wasn't just synthesizing existing data,but actively rejecting the axioms of that data. The 1875 corpus overwhelmingly 'proves' absolute time and the luminiferous aether. A model optimizing for the most probable continuation will converge on that consensus.
To get Relativity, the model needs to realize the training data isn't just incomplete, but fundamentally wrong. That requires abductive reasoning (the spark of genius) to jump out of the local minimum. Without that AGI-level spark, a 'pure knowledge pile' will just generate a very eloquent, mathematically rigorous defense of Newtonian physics.
The point about "being your own doctor" hits the hardest. In the current -money first- healthcare systems, you are essentially forced to become the project manager of your own survival.
The fact that Anthropic has the best model with the least amount of data is a compelling plot twist, but considering that the latest Gemini 3 Flash has taken the lead in SWE benchmarks, either the talent at Google is better than you'd think, or data really does matter a lot.