u/Capital-Wind-8003

I’ve been testing both Claude and Codex lately for coding tasks. For those of you using AI tools in your workflow, which one do you prefer?

I’m especially interested in things like:

• code quality
• debugging help
• understanding larger codebases
• refactoring
• how well it follows instructions
• speed and overall developer experience

I know it probably depends a lot on the specific use case, but I’d love to hear which one you’ve found more reliable for development work, and why.

I’m just starting to look into wearable data APIs and I’m trying to get a better understanding of the field.

I keep seeing Terra and ROOK mentioned, but I’ve also come across Sahha, Validic, Spike, Thryve, and a few others.

For those of you who have actually used any of these, how did you decide which one to go with? Were there any issues you only noticed after integrating?

I’m looking at ways to unify data from devices like Garmin, Xiaomi, Oura, Apple Watch, Whoop, etc., and I’d love to hear what has actually worked for others, especially when the goal is to avoid a setup where every data source becomes its own custom integration that has to be built, debugged, and maintained forever.

For people who have already gone through this, what approach worked best?

Did you integrate directly with each provider? Use something like Apple Health or Google Health Connect as a middle layer? Work with a third-party aggregation API? Or build your own normalization layer on top?

I’m especially curious how you handled differences across sources for things like steps, sleep, workouts, heart rate, HRV, calories, SpO2, recovery/readiness scores, and timestamps.

If you had to start again today, what would you build from day one? What would you avoid?

Any real-world lessons, tools, architecture choices, or “don’t do what we did” stories would be helpful.