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Are your teammates deterministic output machines?

In a sense yes. I trust that my coworkers will be accountable for their outcomes and accomplish them with the high quality bar I know they intrinsically hold themselves to. And that they understand our shared goals and if they don't, they will work to become aligned.

And if I can't say that for a coworker, well, that's performance feedback.


Your coworkers are if anything less reliably deterministic than an AI. And the human version of a prompt injection is called social engineering and it’s far more of an issue than actual prompt injection and has been for an extremely long time.

I don't know what to say except that this has not been the case for me. Claude is a different person for every single prompt and has absolutely no core sense of what our goals are, or even who the "our" is that's having goals.

While my coworkers are very stable personalities of a consistent work drive, sense of pragmatism, things that they find more and less interesting, career aspirations, etc. they're not all the exact same, and I depend on that as I fit tasks and ownership to people.


Highly likely, your trust is just based on your feelings, and they don't do much for the product quality.

At least the tools they used to use used to be so...Now the tools do this https://blog.coredump.cx/p/recursion-into-madness

You mean the company which trained on others books, won't train on its own user generated data?

AFAIK, the court held that the problem was that they had acquired books by pirating them, not that they trained an AI on those books. They do train on user generated data by default, but you can opt out, that's the whole point.

If the mathematicians are using ChatGPT, then they themselves are benefiting from the work of other ChatGPT users, so ChatGPT using their work is not wrong!

Sam Altman has provided people with more generous usage than Claude or Gemini.

There is no doubt ChatGPT is the most generous LLM provider!


Just because a guy is giving you free meth, doesn't make him generous.

More like, "Who are you going to believe: a multi billionaire, or people competing for a million dollar math prize?"

If you think mathematicians do this kind of research just for the chance to win a million dollar prize, please gtfo

ChatGPT sends a lot of traffic to our app! In fact, most of our users come from ChatGPT.

My guess is that it’s because users talk to ChatGPT about our app, so it recommends it to more people?


Is your app AI-adjacent?

No it's fitness app, it's called macrocodex and we've something like 17,000+ users and it's a free app, no subscription or ads.

I think chatgpt recommends it because it produces guaranteed results within 2-4 weeks and I think this primary reason why users perhaps boost the app via chatgpt!?

I've not done any marketing other than mentioning it a few times on hackernews.

Edit: it seems many people here do not understand the innovation of macrocodex, so they just assume it's a calorie tracker or workout app. When it's neither.

It uses state space neural network to produce total average calories a person burns in a day without any sensor and it does it more accurately than apple watch, garmin, etc...

Half of the guys here won't be able to create something like this even with claude or codex, try it

Our neural network beats GLP1 like Trizepetide and Retarutide in results


We run several campaigns for brands on ChatGPT, brand terms consistently perform pretty low. It seems a lot of the prompts are about topics, generics, long tail things... Definitely making a jump, but I would guess people are searching for long tail prompts about working out, what apps could help them track, and you are showing up rather from the app store listing or other mentions on web. Really a black box to some degree, there are some AEO/GEO tools you could use (we have one I could run for you if interested) to try and see where you come up, where competitors come up, etc. That would only show presence, no one has any sort of real prompt volume counts at this point in time (outside Anthropic/openai/google obviously)

It's probably mentioned favorably on fitness forums or sites that were crawled and included in ChatGPT training.

They certainly just mentioned it favourably here, even guaranteeing results!!!

So how did we acquire thousands of users? And user reviews, if it doesn't work?

Probably the same way Sam Altman "acquired" millions of Loopt users.

Our app is free, we do not have money to pull Sam Altman like tactics.

Maybe they used an LLM powered bot to make the favorable comment here in order to guarantee the results.

And LLM powered bot also downloads our app and produces reviews?

It's an app on playstore which anyone can see including reviews.


React Native is slow.

Hermes VM doesn't even have JIT.

If the majority of your app is native code and only a few places are stitched together via JS code that runs in the Hermes VM, then React Native is suitable for you.

Look at V8 vs. Hermes performance.

We use Flutter; we rarely need to write native code. We have three apps: Symbiote workout app, CalorieCodex, an AI calorie tracker app, and MacroCodex with 17,000+ users, all of them completely free


Your data is out of date. React Native performs within spitting distance of raw native now because in the last 2 years... it basically became native - it's now false dichotomy

I tried it about 2 months ago, btw. No prior experience with Flutter or RN.

mind sharing a link or two on the native code generation bit? I'm far behind from the scene but still curious.

They probably mean that modern RN apps can now use a lot of native widgets. You can look at https://docs.expo.dev/versions/latest/sdk/ui/universal/

There's also a project to add a static JS compiler, but it's been in development hell for the last 3 years: https://github.com/facebook/hermes/tree/static_h


It's not. RN has always used native widgets (with a custom renderer) but the JS code itself is purely interpreted.

They increased the interpreter performance by quite a bit, but JITs are impossible on iOS, so it can never be fast.

We have a RN app that needs to do a lot of geometry processing and things like polyclip are unbearably slow, so we had to add native modules to accelerate them. The web version with a true JIT works just fine.


no it's just a secret stackoverflow, reddit.

I was trying to patent our maintenance tracking algorithm, which produces guaranteed weight loss or gain within 2–4 weeks by producing accurate calorie and macro targets for people to follow; in our test, it beats GLP-1s like Ozempic, Tirzepatide, and Retatrutide in results.

But later we found that algorithm and math cannot be patented.


Cool! is there a local model for LLM command approval?

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