If your app uses GPT-4o-mini you should probably update your Privacy Policy, as it states that everything stays on device and nothing is shared with third partys
I’m Joshua, a student, and I’m excited (and a little nervous) to share something deeply personal that I’ve been working on: Islet, my diabetes management app powered by GPT-4o-mini. It’s now on the App Store, but I want to be upfront—it’s still very much in its early stages, with a lot more to go.
I was diagnosed with Type 1 diabetes while rowing competitively, and that moment changed everything. It wasn’t just the practical challenges of managing insulin, carb counts, and blood sugars; it fundamentally shifted how I see myself and the world. It forced me to slow down, prioritise my health, and take control in ways I never had to before. My outlook on life became more focused on resilience, adaptability, and finding solutions to problems that truly matter.
This app started as a pet project over the summer, a way to see what I could create using ChatGPT and explore the potential of LLMs to help with real-world challenges. At first, it was just about making my own diabetes management easier—understanding patterns in blood sugars, planning meals, and adjusting routines. But as I worked on it, I realised it could do more.
Right now, Islet offers personalised meal suggestions, tracks activity, and provides basic insights based on the data you enter. It’s far from complete. Even so, the process of building Islet has already taught me so much about how powerful AI can be in creating personal, meaningful tools.
This project is deeply tied to how my diagnosis changed me. It’s about more than managing diabetes, it’s about showing how anyone, even a student experimenting over the summer, can use AI to potentially solve real, personal problems. I believe tools like LLMs have the power to democratise solutions for all, making life just a bit easier for all of us.
If you’re curious, you can check it out here: https://apps.apple.com/gb/app/islet-diabetes/id6453168642. I’d love to hear your thoughts what works, what doesn’t, and what features you think would make it better. Your input could help shape the next steps for Islet.
This is amazing, my daughter has been on pump therapy for almost eight years now and just now starting to feel like she has any control at all.
I downloaded the app, just to check it out, and the one thing that just struck me right off the bat is the permissions. Read is fine, it’s the write permissions, particularly glucose level and insulin delivery. I don’t know the full app ecosystem, and if it would be possible for your app to interfere with the insulin delivery settings on her pump.
if you have a website or anything that discusses how the application operates and what the permissions are used for it definitely check it out.
Congrats on the launch, Joshua! Super impressive work. I built a management app for T1D kids+parents, so I know how much work goes into a project like this. Happy to chat and share what I learned from my own attempt at this if that might be helpful.
I was founding engineer at a start-up tackling similar problems, albeit using 2014-2022 tech. Email me off-list and I’d be happy to talk through some experiences!
As a fellow indie iOS hacker working on a personal project, I’d love to know more about how you built it! Assuming SwiftUI? Any UI libraries or dependencies? Any learning resources you found particularly helpful?
Congrats on shipping, I’m hopefully a month or so away myself :)
There's been one for years already. Instead of sending your data to ChatGPT, it uses multiple scientific models to calculate your insulin resistance, basal dose and can give you an accurate amount of insulin to dose based on the number of carbs you input. What the Android app can do and iOS makes very hard is to control your pump and close the loop. It is illegal to distribute health apps in binary form if they control your pump, but very much legal in source code form. Try it out:
This is a life-changing app. It lowered my A1c values from around 8% to 5.5%. What is so special with Android is how easy it is to side-load apps, so you can compile AndroidAPS by yourself and keep using it. In Apple ecosystem you need the developer subscription and you also need to reinstall the app every now and then. There is still the Loop app if any iOS users want to try, but this complication from Apple has just pushed me to Android ecosystem for the past decade already.
My son (13) and I are also using AAPS for roughly 3 years now. The app does not look styled, however after taking the initial hurdle to find our way around in the app, it is greatly appreciated. Integrates nicely with Xdrip ( anf others)
Curious where AI added value for you here? I can see the obvious use cases like photo recognition of meals for tracking, but wondering if there's anything more unique.
I wish thyroid disorders were easier to measure as well, just like Blood Sugar is. Does anyone know of proxies to actual blood tests for thyroid level management?
It’s not explained so I have no idea where AI is involved, which makes this all the more alarming. I would seriously ask anyone considering the use of computer generated information in healthcare to do some soul searching and ask themselves if literally people’s lives are worth prolonging this bubble.
I would be wary to put AI functionality in my app, especially if it is a medical or health app.
It looks like the intentions are good, but it reminds me of some indie hacker with zero AI (apart from wrapper apps) or therapist background offered “therapist AI”.
Some people don’t understand how much garbage AI outputs, and these people might not be skeptical enough when it comes to taking medical advice from gpt
I’m not in the target market, but most people have no idea what gpt-40-mini is so while it might make sense to list it in the HN title it probably doesn’t make sense to use that term in the dashboard. AI likely makes more sense, or just machine learning
I signed up for the free week trial to test out some of the AI features. When I asked it to analyze my week the numbers weren’t very accurate compared to my graphs inside the app.
If you need help troubleshooting I’d be more than happy to help
It is absolutely not possible to carb-count through photo recognition in a way that is reliable enough for a diabetic to safely use to make treatment decisions.
However, if you dont have carb info, the alternative is to judge yourself. Your own model may be better than gpts model, though. I would use GPTs output and at least look at it on a case by case basis
I meant more in a general way, like a piece of pizza is usually around X carbs. We have apps that make the guess a bit easier but it's almost always a guess. I was thinking could this look at a photo and know there's a sweet potato, a piece of chicken and some corn and give a basic idea.
The answer is still Absolutely Not, especially since all food can involve a treatment decision for people with type 1 diabetes.
Pizza is a good example of why not. Slices come in very different sizes, sauces have very different carb content, so do crusts, and toppings.
Edit: for example this pizza(1) is 31g per slice and this pizza(2) is 73g per slice. The difference is very meaningful and the “general idea” given by photo recognition would likely be wrong to the point of dangerous for a diabetic in both cases.
If you’re looking for software that can make a guess simply for the sake of generating a number to write down and not be used in any way, a random number generator would be safer since the risk of output being misconstrued as actual information is much lower.
Yep. And the issue with pizza is the amount of fat that comes with the carbs. This quite often (depending on the position of the moon) gives you some of the carbs when you eat it to your blood, and the rest will come after several hours. What you want to do is to inject a bit of insulin before eating, then after two or three hours more while measuring your glucose levels.
Of course if you eat a Neapolitan pizza with not that much of cheese everything changes again. And YMMV, I'm just talking about my experiences.
Not only fat plays a role with pizza, but also the amount of protein in it.
When having pizza we usually add protein to the carbs.
50% immediately bolus.
Other 50% spread over 3-4 hours, and let AAPS dose the insulin.
What do you use then to make these decisions? If you use your eyes, app, nutrition label or Chatgpt, you would still have the same variables. You're still making the decision based on averages, and best guesses.
I use nutrition labels. I have absolutely no idea whatsoever why anyone would lump nutrition labels in with your eyes or chatgpt.
The people that make the label make the food. They know what they put in it. Because they made it. They wrote down what they put in it for you to read and make decisions off of. The difference is categorical.
I cook myself and i know which and how much ingredients i use and how much carbs they contain. Either from a food label or in general (like 100g of cooked potatoes contain about 16g carbs).
Then I calculate how much my serving contains.
Depending on what you eat, what type of diabetes you have and how it’s treated you may have to consider the amount of protein and fat as well (they slow digestion and cause a delayed rise in blood sugar levels). If you have an insulin pump you may want to program a delayed insulin dose to handle that.
Sounds complicated? It is, but only during the first weeks. You quickly learn the carbs content of the food you frequently eat and learn to estimate how much is on your plate. Like, two units for a bun.
There are also great nutrition apps out there that help a lot.
Personally, I take a representative sample and then use a calorimeter to test it. Anyone who doesn't do this is being grossly irresponsible and will only have themselves to blame when they eat so dangerously. I recommend a CK 5E-C5808J but you have to ensure a trained professional is helping you. Otherwise, you might as well not eat at all.
Yeah, you can try this on the ChatGPT app. Take a picture and ask ChatGPT to give you the nutrition info, then do your own calculations based on weight and the USDA database and see how it compares.
High blood sugar is a symptom of the underlying pathology—the lack of insulin in T1, low sensitivity to insulin in T2 (itself typically but not always secondary to other factors such as visceral obesity). Low carbohydrate intake can manage the deleterious effects of T2, but it is not a cure.
That applies to vegan militants as well. It is well researched now that low-carb puts T2 diabetes in remission, and calling it bro science won't invalidate it.
No, muscles and the liver constantly release glucose into the bloodstream. Type 1 diabetics can’t produce any insulin and would end up in hyperglycemia.
If your app uses GPT-4o-mini you should probably update your Privacy Policy, as it states that everything stays on device and nothing is shared with third partys
https://anthropometric.godaddysites.com/
Hi HN,
I’m Joshua, a student, and I’m excited (and a little nervous) to share something deeply personal that I’ve been working on: Islet, my diabetes management app powered by GPT-4o-mini. It’s now on the App Store, but I want to be upfront—it’s still very much in its early stages, with a lot more to go.
I was diagnosed with Type 1 diabetes while rowing competitively, and that moment changed everything. It wasn’t just the practical challenges of managing insulin, carb counts, and blood sugars; it fundamentally shifted how I see myself and the world. It forced me to slow down, prioritise my health, and take control in ways I never had to before. My outlook on life became more focused on resilience, adaptability, and finding solutions to problems that truly matter.
This app started as a pet project over the summer, a way to see what I could create using ChatGPT and explore the potential of LLMs to help with real-world challenges. At first, it was just about making my own diabetes management easier—understanding patterns in blood sugars, planning meals, and adjusting routines. But as I worked on it, I realised it could do more.
Right now, Islet offers personalised meal suggestions, tracks activity, and provides basic insights based on the data you enter. It’s far from complete. Even so, the process of building Islet has already taught me so much about how powerful AI can be in creating personal, meaningful tools.
This project is deeply tied to how my diagnosis changed me. It’s about more than managing diabetes, it’s about showing how anyone, even a student experimenting over the summer, can use AI to potentially solve real, personal problems. I believe tools like LLMs have the power to democratise solutions for all, making life just a bit easier for all of us.
If you’re curious, you can check it out here: https://apps.apple.com/gb/app/islet-diabetes/id6453168642. I’d love to hear your thoughts what works, what doesn’t, and what features you think would make it better. Your input could help shape the next steps for Islet.
Thanks for reading !
joshua
This is amazing, my daughter has been on pump therapy for almost eight years now and just now starting to feel like she has any control at all.
I downloaded the app, just to check it out, and the one thing that just struck me right off the bat is the permissions. Read is fine, it’s the write permissions, particularly glucose level and insulin delivery. I don’t know the full app ecosystem, and if it would be possible for your app to interfere with the insulin delivery settings on her pump.
if you have a website or anything that discusses how the application operates and what the permissions are used for it definitely check it out.
Ultimately though this looks like a great tool!
(Also really digging the company name!)
Congrats on the launch, Joshua! Super impressive work. I built a management app for T1D kids+parents, so I know how much work goes into a project like this. Happy to chat and share what I learned from my own attempt at this if that might be helpful.
fellow t1d + engineer + competitive rower (20 years ago...sigh) here.
props on shipping an incredible personal project! if you ever want to geek out about diabetes tech, DM me on X @kamens
scott hanselman would probably also love to chat about your project
I was founding engineer at a start-up tackling similar problems, albeit using 2014-2022 tech. Email me off-list and I’d be happy to talk through some experiences!
As a fellow indie iOS hacker working on a personal project, I’d love to know more about how you built it! Assuming SwiftUI? Any UI libraries or dependencies? Any learning resources you found particularly helpful?
Congrats on shipping, I’m hopefully a month or so away myself :)
Does the App need GPT to run or was it just used to develop the app?
For an app being in the early stages the feature list is solid. Good work! love these lovingly crafted apps
Looks great. I'd love to try an Android version.
There's been one for years already. Instead of sending your data to ChatGPT, it uses multiple scientific models to calculate your insulin resistance, basal dose and can give you an accurate amount of insulin to dose based on the number of carbs you input. What the Android app can do and iOS makes very hard is to control your pump and close the loop. It is illegal to distribute health apps in binary form if they control your pump, but very much legal in source code form. Try it out:
https://androidaps.readthedocs.io/en/latest/
This is a life-changing app. It lowered my A1c values from around 8% to 5.5%. What is so special with Android is how easy it is to side-load apps, so you can compile AndroidAPS by yourself and keep using it. In Apple ecosystem you need the developer subscription and you also need to reinstall the app every now and then. There is still the Loop app if any iOS users want to try, but this complication from Apple has just pushed me to Android ecosystem for the past decade already.
https://loopkit.github.io/loopdocs/
My son (13) and I are also using AAPS for roughly 3 years now. The app does not look styled, however after taking the initial hurdle to find our way around in the app, it is greatly appreciated. Integrates nicely with Xdrip ( anf others)
It is a life-saver. Learn it once, and it is the most important tool in your diabetic toolbox.
Curious where AI added value for you here? I can see the obvious use cases like photo recognition of meals for tracking, but wondering if there's anything more unique.
I wish thyroid disorders were easier to measure as well, just like Blood Sugar is. Does anyone know of proxies to actual blood tests for thyroid level management?
It’s not explained so I have no idea where AI is involved, which makes this all the more alarming. I would seriously ask anyone considering the use of computer generated information in healthcare to do some soul searching and ask themselves if literally people’s lives are worth prolonging this bubble.
I would be wary to put AI functionality in my app, especially if it is a medical or health app.
It looks like the intentions are good, but it reminds me of some indie hacker with zero AI (apart from wrapper apps) or therapist background offered “therapist AI”.
Some people don’t understand how much garbage AI outputs, and these people might not be skeptical enough when it comes to taking medical advice from gpt
Therac-25
https://en.wikipedia.org/wiki/Therac-25
I’m not in the target market, but most people have no idea what gpt-40-mini is so while it might make sense to list it in the HN title it probably doesn’t make sense to use that term in the dashboard. AI likely makes more sense, or just machine learning
How was the getting approved by Apple workflow? Historically speaking, they take a long time verifying your app. So just curious.
As a new diagnose T1D I love this!
I signed up for the free week trial to test out some of the AI features. When I asked it to analyze my week the numbers weren’t very accurate compared to my graphs inside the app.
If you need help troubleshooting I’d be more than happy to help
Does the photo recognition attempt to carb count what it sees? Is that even possible? My son is a T1D and he still struggles with carb counting.
It is absolutely not possible to carb-count through photo recognition in a way that is reliable enough for a diabetic to safely use to make treatment decisions.
However, if you dont have carb info, the alternative is to judge yourself. Your own model may be better than gpts model, though. I would use GPTs output and at least look at it on a case by case basis
I meant more in a general way, like a piece of pizza is usually around X carbs. We have apps that make the guess a bit easier but it's almost always a guess. I was thinking could this look at a photo and know there's a sweet potato, a piece of chicken and some corn and give a basic idea.
The answer is still Absolutely Not, especially since all food can involve a treatment decision for people with type 1 diabetes.
Pizza is a good example of why not. Slices come in very different sizes, sauces have very different carb content, so do crusts, and toppings.
Edit: for example this pizza(1) is 31g per slice and this pizza(2) is 73g per slice. The difference is very meaningful and the “general idea” given by photo recognition would likely be wrong to the point of dangerous for a diabetic in both cases.
If you’re looking for software that can make a guess simply for the sake of generating a number to write down and not be used in any way, a random number generator would be safer since the risk of output being misconstrued as actual information is much lower.
1 https://www.costcobusinessdelivery.com/kirkland-signature-ca...
2 https://sbarro.is/product/bbq/
Yep. And the issue with pizza is the amount of fat that comes with the carbs. This quite often (depending on the position of the moon) gives you some of the carbs when you eat it to your blood, and the rest will come after several hours. What you want to do is to inject a bit of insulin before eating, then after two or three hours more while measuring your glucose levels.
Of course if you eat a Neapolitan pizza with not that much of cheese everything changes again. And YMMV, I'm just talking about my experiences.
Not only fat plays a role with pizza, but also the amount of protein in it. When having pizza we usually add protein to the carbs. 50% immediately bolus. Other 50% spread over 3-4 hours, and let AAPS dose the insulin.
What do you use then to make these decisions? If you use your eyes, app, nutrition label or Chatgpt, you would still have the same variables. You're still making the decision based on averages, and best guesses.
I use nutrition labels. I have absolutely no idea whatsoever why anyone would lump nutrition labels in with your eyes or chatgpt.
The people that make the label make the food. They know what they put in it. Because they made it. They wrote down what they put in it for you to read and make decisions off of. The difference is categorical.
I cook myself and i know which and how much ingredients i use and how much carbs they contain. Either from a food label or in general (like 100g of cooked potatoes contain about 16g carbs).
Then I calculate how much my serving contains.
Depending on what you eat, what type of diabetes you have and how it’s treated you may have to consider the amount of protein and fat as well (they slow digestion and cause a delayed rise in blood sugar levels). If you have an insulin pump you may want to program a delayed insulin dose to handle that.
Sounds complicated? It is, but only during the first weeks. You quickly learn the carbs content of the food you frequently eat and learn to estimate how much is on your plate. Like, two units for a bun. There are also great nutrition apps out there that help a lot.
Personally, I take a representative sample and then use a calorimeter to test it. Anyone who doesn't do this is being grossly irresponsible and will only have themselves to blame when they eat so dangerously. I recommend a CK 5E-C5808J but you have to ensure a trained professional is helping you. Otherwise, you might as well not eat at all.
Yeah, you can try this on the ChatGPT app. Take a picture and ask ChatGPT to give you the nutrition info, then do your own calculations based on weight and the USDA database and see how it compares.
It's probably slightly worse than an educated guess?
Is there any concern about the similarity in name to the company Insulet (the maker of the Omnipod)?
Beautiful charts - may I ask what library you are using?
> what library you are using?
Not the OP but these look like they are Apple's own SwiftUI Charts framework:
https://developer.apple.com/documentation/charts
> GluCoPilot
I’m dead I love it.
Question: Doesn't a carb-free (carnivor/true keto) diets eliminate diabetes?
High blood sugar is a symptom of the underlying pathology—the lack of insulin in T1, low sensitivity to insulin in T2 (itself typically but not always secondary to other factors such as visceral obesity). Low carbohydrate intake can manage the deleterious effects of T2, but it is not a cure.
Depends on the type. For instance, my diabetes is caused by scarring of the pancreas due to another medical condition, and no diet can undo that.
no. type one diabetics do not produce insulin. the immune system attacks insulin producing cells in the pancreas. there is no "elimination."
Don't get your intel from social media gurus.
That applies to vegan militants as well. It is well researched now that low-carb puts T2 diabetes in remission, and calling it bro science won't invalidate it.
No, muscles and the liver constantly release glucose into the bloodstream. Type 1 diabetics can’t produce any insulin and would end up in hyperglycemia.
It can help with type 2 diabetes. Look up Dr. Sarah Hallberg.