Out of the box, Opus/GPT-5.4 uses checks like `tsc --noEmit` or `python -m py_compile ...`, so compile checks on a Swift/Objective-C app probably get you pretty far. Also just setting up a Dockerfile maybe with swift:5.10-focal would give the agent the right tools to verify its own work.
Sandboxing isn't about having the perfect devex environment, most dev sandboxes (looking at codex cloud) don't actually have full verification available. Sometimes Github actions CICD is all you can get!
If choosing a Bayesian approach in a clinical trial can reduce the number of recruited subjects, I would imagine the pharma industry is strongly incentivized to adopt it.
Would a wearable model like this gain in predictive power by adding FHIR/EHR inputs? CoMET [1] has great AUC even without daily/lifestyle metrics like steps, awakenings at night, or vo2max estimates (which are present in say apple health). It would be interesting to know how much you would gain from having a holistic model that sees both inputs.
[1] https://news.ycombinator.com/item?id=48028790