Every workout tracker I've tried has the same limitation: it records what you did, but it doesn't tell you what's going wrong.
You finish a workout, log your sets, reps, and weight, then the app stores the data and that's about it. If you've stopped progressing, consistently training too close to failure, or need a deload, you're left to figure that out yourself.
That's what led me to build WhyRep.
It's a workout tracker with a built-in coach that analyzes your training and explains what's holding you back. The key difference is that every coaching decision has to trace back to a methodology I wrote and approved beforehand, never something an LLM made up on the spot.
For context, I've spent the last three years studying exercise science with a focus on muscle hypertrophy. Rather than asking an AI to invent programming, I write and validate the coaching methodology first, then use AI to explain those decisions in a conversational way. I'm not fine-tuning a model on hypertrophy data and hoping it generalizes. The pipeline is closer to this:
I write the methodology myself first, leaning on my physiology background. Progression rules, deload and autoregulation logic, plateau diagnosis, and everything else starts as a document that I draft and sign off on, complete with concrete test vectors (specific input → specific expected output), before a single line of coaching code gets written.
Deterministic engines implement those docs. ProgressionEngine
, AutoregulationEngine
, PlateauEngine
, and others implement the methodology and are tested against the test vectors, not against "does this feel right?"
The LLM (Claude) only operates inside that fence. It handles the conversational layer by answering your questions, explaining decisions in plain language, and helping you modify your program. It's constrained to the approved methodology, not free to invent new training science mid-conversation.
I've spent a huge amount of time refining this interaction. The goal isn't just to answer basic questions. It's to have the kinds of nuanced coaching conversations you'd expect from a knowledgeable human coach, while keeping every recommendation grounded in the documented methodology.
For example, if you tell the coach, "I want to bring up my arms," it can recommend concrete changes such as prioritizing arms earlier in your workouts, adjusting weekly volume and frequency, and then update your program if you approve the changes. It also goes beyond the advice most people already know. Many lifters don't realize that if the brachialis is a weak point, it can be trained more effectively by using curl variations that place the shoulder into flexion to emphasize it separately from the biceps. The coach can recognize situations like that, explain the reasoning, and incorporate those changes into your program. Again, none of that is invented on the spot. Every recommendation has to trace back to the underlying methodology that I wrote and approved.
This is also why I'm comfortable letting people challenge the coach. If it recommends something, I should be able to point to the methodology that produced that recommendation and explain the physiology behind it. If I can't justify it scientifically, it doesn't belong in the product. I'd rather spend another week improving the methodology than ship a feature that sounds convincing but isn't something I'd stand behind as a coach.
This isn't a mockup. Here's what's built and running on a physical device right now:
Though I'd argue that the methodology is the product. I've probably spent more time writing, validating, and refining the methodology documents than writing the AI itself. Every progression rule, plateau diagnosis, autoregulation decision, and program modification starts life as a piece of methodology that I draft, challenge, revise, and test before it ever reaches the coach.
The methodology goes far deeper than "add two sets to chest." Every exercise in the library has documented fractional set contributions for every relevant muscle group. For example, a lat pulldown doesn't just count as one lat set. It also contributes fractional volume to muscles like the biceps. When the coach decides whether to increase, decrease, or maintain your weekly volume, those indirect contributions are already accounted for in the calculations instead of pretending every muscle only receives stimulus from isolation exercises.
That's the biggest difference between WhyRep and most AI fitness apps. I'm not asking an LLM to become a coach. I'm trying to encode an evidence-based coaching methodology into software, then using the LLM as the interface that makes it feel natural to interact with. The AI isn't the source of truth. The methodology is.
Top-of-funnel right now: educational, no-fluff gym content on TikTok and Instagram, slowly building an audience. If any of you are into training/hypertrophy content, I'd genuinely appreciate a follow — and if you have thoughts on what's working or not for build-in-public creators in this niche, I want to hear it:
This is as much a "help me think" post as a "look what I built" post. Specifically I'd love input on:
Will keep posting weekly as this moves forward. Thanks for reading this far, I'll probably show the demo next week!