I love these! I recently designed a few decks of playing cards of mathematicians for my dad as a gift, my favorite part was designing the backs: a zeta function for the pure-maths deck, a Moore curve for the computation one, and two interfering waves for the physics one.
it's a weight-training app that helps you train along your "pareto frontier" of weight vs reps. The idea is to train at lower weight-higher rep, medium weight medium reps, and higher weight, lower reps for every movement. I tried to develop my own weight training program following bits and pieces of advice from bodybuilding forums and ended up injuring several tendons in my first year. So I did a bunch of research on tendon strengthening as well as what's most effective for hypertrophy (reps near failure) strength (reps near maximal load) and injury-prevention/frequency (not bringing yourself to failure too often) and designed an app to automatically prescribe and advance weights and reps based on your learned strength curve (Brzycki-like, with an added shape parameter)
The app is designed to make use of the free Cloudflare tier, so I can support thousands of athletes for just the cost of the domain name. I'm primarily interested in understanding the "Fatigue curve" - right now I have some basic per-set fatigue modeling (basically a log-linear strength dropoff) but I think it could be much better characterized with more data. I could go on and on about the modeling but my intention is to keep it free (maybe add some non-intrusive ads on content pages if it ever starts costing me a few pennies a month) but my primary interest is to be able to do statistical analysis on the data.
All 3 incorporate some form of RIR progression, varying rep ranges, program builders, and (at least in RP) some form of feedback mechanism to tell how impactful the last training stimulus was on recovery.
Not trying to bash it, but to either 1.) help you think through your offer or 2) help you realize there's already a lot in the market.
If you're really just doing this for you, and your interests then glhf :)
I don't intend to monetize, I only want to compete for data. I think it's an interesting field and I'd like to be able to contribute back to the science by using the data gathered. I think these workout apps try to be opinionated about what an effective program is, but they try harder to make money, so they generally bend over backwards to accommodate whatever program the paying customer wants to do. Mine is more opinionated. I think this is the best way to lift weights for a wide range of athletes who are interested in getting stronger and bigger with the least amount of wasted effort and lowest chance of injury, the simplest way to record it, and the best way to get a statistically defensible understanding of how your strength is changing over time. If you want to do something different, as you mention, the space is crowded with apps.
Ok, so if I'm synthesizing your words correctly, other apps are "Bring your program, any program" and you're really leaning on "We'll tell you exactly what to do based on Pareto frontier modeling" ?
I'm not trying to say that other apps don't also provide great advice (especially RP, I love watching Dr Mike and have learned a ton from him), but I think that a lot of the flexibility in the programs in these apps are primarily for marketing purposes (like are you really gonna look like Black Adam after following the Black Adam program? unlikely..)
My marketing is for a different niche - people who lift weights, see that they have variability set to set and day to day, and want a program that will help them quantify that variability and understand what it means. One thing that works really well in my program is detecting when you need a rest or deload, because the baseline comparison is rigorous and sensitive - if you have one bogey set, it might not mean much, but when you see that even your opening sets are at or below baseline after a stretch of workouts, that's a pretty good signal, and that's hard to get with most apps.
It looks interesting but as someone else noted, it needs more images and animations, way less walls of text.
There's a huge database of licensable animations that several apps I've seen use. E.g. take a look at the animations in Hevy. I forget the name of it now but you should be able to find it easily.
yeah, point taken, I do need to rewrite the user-facing documentation to be more accessible (and less obviously LLM-assisted, heh - although I did review and edit all of it). Workout design (including teaching a user how to do an exercise) and logging are two legitimately different tasks in a workout app and I wanted to focus on being the best logger. Maybe this is paranoid but I felt that saying "here's how you do a workout" introduced a little more potential liability than punting to "if you don't know how to setup a workout and learn to do the movements, hire a personal trainer to help you figure out movements appropriate for your fitness level."
Gave it a quick go as I'm looking to adopt some sort of fitness app. There's too much text/complexity for my taste.
The website could use some images/simplification to get going and then have more details and whatnot later on. It seems that you let the LLM generate the content itself (em dashes, emojis) - Personally I'd be skeptical of a fitness app that has most of its content LLM'izied.
On step 3 of the tutorial, it's not clear that you can scroll down and there's more there on a mac 16''. I was confused what to do. It didn't allow me to change to metric system either (which I later found in the user-settings). You can't 'esc' from the tutorial popup either.
yeah, it's a difficult balance to explain why this app doesn't work like every other weightlifting app and not inundate people with too much information. It actually is pretty simple once you start using it. I'll look at the tutorial scrolling issue, and eventually rewrite the content.
The tutorial can't be escaped because it requires a liability waiver checkbox at the end.
This is really awesome! I used to have an movement to tissue mapping in an earlier version of app but I opted to focus more on the weight-reps curve it because the tissue volume calculations added too much complexity to the interface, and it just encouraged me to do too many different movements. So exercise selection and tissue coverage is something I actually think about outside my app.
I had coefficients mapping each exercise to its tissue impact but it was kind of arbitrary and unscientific - even between athletes you will have different techniques on the same movement that will impact tissues differently. I do think that workout design is a super important area and I hope you crack it, it was too hard for me to do a good job on.
physiological papers. My main interest is contributing to the research by developing a fatigue model. My hypothesis is that athlete recovery factors like accrued fatigue in-day and cross-day can be used to predict workout log outputs of total tonnage, combination of weight and rep count, and proximity to failure. Eventually I hope to be able to link these recovery factors directly to provide targeted advice about your overall program's effectiveness, deload planning, etc. in order to most efficiently achieve strength, endurance, and hypertrophy goals.
yeah, I have been amazed at what I could do for free, and then amazed at how much more powerful it got for 5 bucks a months. Cloudflare is killing it in terms of value for small websites (and features and reliability).
3.7 flash was by far the best model for image recognition tasks according to my benchmarks. 3.8 flash didn't regress any candidates and improved some specificity (positive ID of common name vs species name of exotic fruit, correct identification of cast/replica of artifact and statue) but is still relatively weaker (26/30) on esoteric public figures (Korean beatboxers). I'm going to have to make my benchmark harder.
I’m very curious about your esoteric public figures benchmark, do you ask it in English or Korean to identify the person? Does it change the result? I wonder if having data labeled in only a given language (or web sources in only a given language) change the output.
I haven't tried asking it in Hangul but these particular artists (and the photos I'm using actually) are linked to their romanized english names on e.g. Fandom so it's not unfindable on the internet
I just ran my image recognition benchmark on it ("is this XXX public landmark"?) and it misses a lot that bytedance seed 2.1 turbo gets right; for example:
Asked "Is this Salisbury Cathedral" and supplied a picture of Wells Cathedral, it answers "Yes, the west facade of Salisbury Cathedral". Bytedance seed 2.1 turbo correctly says no. Similar results for a picture of Manhattan Bridge sent as Brooklyn Bridge, Chartres Cathedral sent as Notre Dame, etc.
I have a benchmark of 12 such images and seed gets 11/12 and deepseek only gets 6/12.
I have zero interest in world knowledge for my LLMs but this got me wondering : are there RAGs for that kind of data ? How could a LLM like DeepSeek-v4-flash-vision-exp accurately answer you question with an indexed database of labeled landmark pictures (or even 3D models ?).
My benchmark is tiny compared to WorldVQA or FG-BMK, which are available, so I'd point you in that direction if you're interested in a VLM benchmark. My use-case isn't exactly captioning as in "what is in this image?" -> caption, I am using the VLM to validate captions, as in "is this an image of [supposed subject]?" - my ranking of models I've benchmarked is gemini-3.7-flash > seed-2.1-turbo > gpt-5.6-luna > qwen-3.7-plus > qwen-3.7-flash. Gemini is almost perfect on my test dataset, only failing on some esoteric pop-culture minor celebrities and being over-specific in some cases (i.e. Q: is this [common name of fruit]? A: that's a [latin species name of fruit], not a [common name of fruit]; false). However, gemini-3.7-flash is only in my test list because openrouter has it on 75% introductory discount; otherwise it would be about 4x more expensive than seed.
the only thing that still kind of annoys me is constantly being told what something is not, but even that statement is load-bearing (see what I did there) because it records how it ended up with this decision, because it's not that other choice that it mentions.
FWIW, I also think the constant chorus about how new models are worse than old models is a human hallucination. They're certainly not perfect but every one becomes more steerable in terms of actually completing more and more complex work.
I don't know that much about math but I've read that one possibility is he had found a valid proof for n=4 and assumed that it generalized. Hopefully someone else who knows more about the subject chimes in!
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