Coach here, with a disclosure: Nick and I build a deterministic (non-LLM) planner on top of Intervals.icu, so I’m not neutral. But on @Gabriel_Vargas’s bad-data question, what we learned is that the model matters less than what happens when data is missing or wrong:
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Show where every number comes from. A threshold measured in a lab, one calculated from a field test and one estimated from eFTP should not look the same on screen.
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Don’t fill gaps silently. If a weight or a test is missing, say so instead of plugging in a default.
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Keep a person between the plan and the calendar. The coach or the athlete reviews before anything is published.
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Same inputs, same output. Then you can trace a strange week back to the input that caused it.
Bad files (heart-rate spikes, power dropouts, duplicates) are still the hard part. We flag activities that arrive without detail, but bad data in still hurts. I agree with WindWarrior: the principles are simple, and tools should make them easier to follow, not replace judgement.
If you want to see how we handle it: our beta thread.
Grts, Vince & Nick