Adaptiv Performance
AI-powered strength training that adapts after every workout
I've spent the last few months building Adaptiv, an AI strength training coach that creates personalised programmes and then recalibrates them after every workout based on your actual performance.
Most fitness apps generate a plan once and leave it at that. I wanted to build something that adapts as you get stronger, miss sessions, or progress faster than expected.
Some things I've learned along the way:
Building the AI wasn't the hardest part—designing the training logic was.
Getting clear, evidence-based explanations for every programme change is surprisingly important for user trust.
Launching is much harder than building. Marketing has definitely been my biggest challenge.
I recently launched on Product Hunt and am now working on getting those first users and learning what people actually want.
If you're building in AI or fitness, I'd love to hear what's worked for you when it came to finding your first 100 users.
The real differentiator behind Adaptiv is its coaching engine. Most fitness apps create a personalised programme and then leave you to follow it. Adaptiv continuously analyses what actually happens in your training and uses that information to determine what should happen next.
It considers every set you log — weight, reps, effort and feedback — alongside your performance, recovery, soreness, readiness, progression and training history. It can then adapt load, reps, sets, weekly volume, exercise selection, conditioning, deloads and progression, while accounting for things like missed sessions, time away from training, plateaus, pain and equipment limitations.
Crucially, the engine is deterministic and evidence-informed. The numbers aren't invented by AI; they're generated from explicit coaching rules and thresholds based on established training principles. AI is used to explain the decisions in plain English.
So Adaptiv isn't simply a personalised workout generator. It's a coaching system that learns from every workout and continuously evolves the programme around the individual.
Measure what happened → adapt what comes next → explain why.
6 Comments
"Adapts after every workout" — but who's actually paying? Gym bros who want gains, or busy professionals who want efficiency? The first wants progressive overload, the second wants "just tell me what to do." Same AI, two different products.
That's a really good question, and it's something I've been thinking about.
My view is that the two audiences aren't necessarily mutually exclusive because they're both solving the same underlying problem. The adaptation engine is based on progressive overload and periodisation, principles that are already well established for building muscle and strength. What Adaptiv does is automate those adjustments based on how the user is actually responding (via logging RPE)
For someone chasing performance or gains, that means taking the guesswork out of when to increase weight, add volume or deload. For someone who's busy, it means they don't have to think about programming at all, they can just turn up and train.
So I see the adaptation engine as the product, with different people valuing it for slightly different reasons. The challenge for me is probably less about building two products and more about finding the messaging that resonates with each audience.
"Finding the messaging that resonates with each audience" — that's the hard part, and it's exactly what most founders get wrong. You can have the same engine, but the gym bro needs to hear "progressive overload" and the busy pro needs to hear "just show up." I simulate how each segment reacts to messaging before you spend on ads or redesign your landing page. Happy to run it on Adaptiv if useful — might save you from A/B testing in the dark.
The interesting part is that you've built an adaptive training loop rather than just an AI-generated workout plan.
What would convince you that the main growth challenge is finding the right audience, rather than proving that users actually value the adaptation loop enough to change their existing training habits?
That's a great question, and one I'm trying to validate.
The adaptation loop isn't just AI changing a programme for the sake of it. It's built around the well-established principle of progressive overload, which is one of the key drivers of building muscle and strength. After each workout, users log their RPE (Rate of Perceived Exertion), and Adaptiv automatically adjusts weight, reps and sets for future sessions based on that feedback.
In theory, this shouldn't require people to adopt a completely new training philosophy—it simply automates a process that experienced coaches and lifters already do. Instead of wondering whether to add 2.5kg, squeeze out another rep, or back off because a session was harder than expected, Adaptiv makes those evidence-based adjustments automatically and explains why.
The part I still need to validate is whether enough people value that automation to switch from their current approach. If users try it and consistently engage with the adaptation loop, then I know it's solving a real problem. If they don't, then the issue is likely the product rather than distribution.
So at this stage I'm testing both assumptions: whether I've found the right audience, and whether automating progressive overload creates enough value to change behaviour.
Appreciate the context.
Would be good to continue the conversation as you learn which of those assumptions holds true.
What's the best email to reach you on?