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There's an incredibly serious lack of education with how LLMs & carb-counting works. This entire article would be better suited to astrology.com than hackernews

There's an incredibly serious lack of education with how LLMs & carb-counting works. This entire article would be better suited to astrology.com than hackernews. When I opened it up, I assumed the author would have at least attempted a calculation service, maybe even placed something like the size of the meal into an actual model, using the integration of pre-existing tools that are (slightly more) accurate. Hell - most food literally is required to have calorie information, and you can query open source data for others! But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? This is akin to the instagram reels that talk to chatGPT and ask it to time how long they're run is. Except those are treated as funny jokes rather than being turned into studies. I'd like to see this study done using any kind of actual grounding knowledge, seeing what mistakes AI makes when attempting to query ground truth from picture analysis - there would at least be an interesting result methodology in that.

nextlevelwizard 5mo ago
As someone who used to do this. OpenAI models refuse to look up calories unless you explicitly tell them to and even then it is a hit and miss even if you tell them exactly what the product is. Easiest way to get good calculation is to just take a photo of the nutrition label or feed that info in by hand. Funny thing is 4o did look up calories but I guess it was too good for this world
- the_duke 5mo ago
  I exclusively use thinking mode, which is slower but much more likely to double-check things with web search etc.
  - nextlevelwizard 5mo ago
    Maybe. I stopped using OpenAI a while ago. But taking pictures of the nutrition labels was good enough
swalsh 5mo ago
It amazes me how much people try to build AI systems relying on nothing more than the models knowledge. I suspect a great deal of "failed" AI experiments we keep reading are people just not having any idea how to use AI at what its good at.
furyofantares 5mo ago
From the text of the article I believe the author is implying there are apps doing exactly this, and so this is why it was studied that way. Had the author written the article themselves rather than an LLM their motivation probably would have been clearer.
- Brendinooo 5mo ago
  > there are apps doing exactly this Yeah, for sure there are. And people will just ask ChatGPT as well. The funny thing is that for people who are just trying to lose weight without managing any health issues precisely, this type of extreme variance doesn't really matter, because the mere act of consciously quantifying food consumption is, based on my experience counting calories, the single biggest factor in success with weight loss.
  - criley2 5mo ago
    I actually think "just asking ChatGPT" is fine, because A) the data in these apps is suspect at best and B) the data behind calories is also pretty suspect (but we all play along because we can adjust other variables to make it all "work" well enough). Once or twice a year I spend a few weeks meticulously measuring ingredients/cooked foods and recording calories and on complex recipes apps are next to useless at getting accurate data. You're trying to input five or ten relevant ingredients, and then weighing your cooked outcome to try and divide the ingredients by proportion. Frankly it's a mess and most people aren't doing it for home cooked meals, and are getting very lossy outcomes (weighing cooked chicken and marking it as raw chicken, etc) With reasoning and tool calling (combined with me meticulously weighing before and after), it's producing fine data for my purposes.
    - ijk 5mo ago
      I was complaining about AI generated clothes being misleading marketing, deceiving customers as to whether the garment even exists. And then I learned that the pre-AI norms weren't any less fictional: they made an exemplar garment and did photoshoots, sure, but then they send the pictures and patterns to the lowest bidder factories with permission to make whatever edits are necessary to make it cheap and manufactureable. The whole thing was already a simulacrum.
throwaw12 5mo ago
I feel like you didn't understand the goal of this study > The DTN-UK stated earlier this year that generic LLMs must never be used as autonomous advisory calculators for insulin delivery. This data is the quantitative evidence base for that statement. This study is to prove that you should not rely on LLMs
- fabian2k 5mo ago
  The paper itself is a lot clearer about the purpose. The blog post reads very clickbaity and doesn't really explain the context well.
  - Aurornis 5mo ago
    I disagree, it clearly explains that AI carb counting apps are a problem and shouldn’t be used. They’re writing in a neutral way that reaches their audience without lecturing or being condescending. They lead the reader to the conclusion rather than shoving it at them. I think that’s why it’s triggering so many angry comments on HN, but it’s effective for the audience they’re writing for (non technical people who may need convincing but don’t like being preached at)
- snapcaster 5mo ago
  But it's stupid. If i smack myself in the head with a hammer is that proof hammers shouldn't be relied on?
  - deleted 5mo ago
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  - jmye 5mo ago
    Are there start-ups led by idiots suggesting that smacking yourself in the head with a hammer will help treat your diabetes? If not, then perhaps there's a problem in your analogy.
  - fc417fc802 5mo ago
    If you smack yourself in the head with a hammer and it injures you that's evidence that smacking people in the head with hammers is bad and shouldn't be done, right?
zipy124 5mo ago
Honestly it's scary how misunderstood this is by the general public, the media and EVEN scientists. There is a shocking amount of Computer Vision tasks where the scientists claim you can get X info from a picture of Y and it's like, even with ML/AI you can't extract data where there isn't any. The fact I can add an arbritrary amount of high-calorie fat to a meal without changing the appearance by defintion shows it's pointless. A 1000 calorie and 100 calorie milkshake can look identical, and you'd have no way of working that out via an image even if it was a super-intelligent system. Similarly I see it in things like extracting material of an object from an image of it in serious research papers, which for the same reason cannot be done, since how an object looks has very little to do with what its made of, else painting and other art would clearly be impossible. The information is just not there within the data.
- mortenjorck 5mo ago
  It’s like CSI “enhance!” AI image upscaling. People will do it, see it fabricated details, and then draw the wrong lesson from it, that “AI fabricates things!” when that is exactly what they asked the model to do and there is no magic math that would extract ground truth that was never in the image to begin with.
InsideOutSanta 5mo ago
There are apps in the app store right now that pretend to do this kind of thing, so having somebody actually show that it doesn't work is valuable, even if we already knew the outcome ahead of time.
- endymion-light 5mo ago
  I suppose i'd much rather a study analyse the apps in the app store that are attempting and claiming to do that kind of thing - rather than the base model they might be using.
giancarlostoro 5mo ago
> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? Reminds me of that one youtube video (I forget who it is so I have no idea how to pull it up) where he turns on the camera on his phone for ChatGPT and asks it what everything it sees weighs, then puts it on a scale, and ChatGPT was never right, ever, which makes sense, I couldnt tell you what most things weigh on sight alone either, but ChatGPT often got it dramatically off. I got the feeling he thought it was terrible AI for this, but I don't think a model looking at an image of something and trying to guess its weight / calories / etc... is a reason to call an AI model bad...
- wat10000 5mo ago
  People do not understand this stuff at all. So many times I've seen people post the output from one of these services with full confidence that it has to be correct because it came from a computer.
  - Izkata 5mo ago
    Between the marketing and the hype, plenty of people believe we already achieved true sci-fi-style superhuman general AI a year or two ago.
throwaway613746 5mo ago
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Aurornis 5mo ago
> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? The article explains this: There are apps targeting people with diabetes that claim to count your carbs with AI. > If you’re using AI carb counting in a diabetes app Before you dismiss a study, try to understand where it’s coming from. The authors of the study weren’t stupid. They knew the LLMs would provide poor results. They ran the study to quantify it and create a resource to spread the information in response to the rise of AI carb counting apps.
- ilivethere 5mo ago
  Typical case of the "curse of knowledge". We deal with AI on a daily basis on the technical level, so it's very easy to forget that the "common" folk really still believe that AI can replace dieticians, gym coaches, etc
- endymion-light 5mo ago
  I don't believe the authors of this study are stupid. If there are apps targeting people with diabetes that claims to count your carbs with AI, why haven't those been analysed? That would be a far more effective claim. I based the study off of the clickbait article that they wrote about the study - i'll read through the study to see whether they analyse that, but it would be far more effective to see if the 'carb-counting' AI app is returning similiar results to the frontier model - that's an interesting result that actually can forward discussion.
  - Aurornis 5mo ago
    > If there are apps targeting people with diabetes that claims to count your carbs with AI, why haven't those been analysed? That would be a far more effective claim. Because the apps aren’t going to let you submit 29,000 automated requests for statistical analysis. And if you did, the authors of those apps would just release an update saying they changed models and try to dismiss the study. The vitriol against this article on HN is sad. Commenters who agree with the article and its conclusions are grasping for reasons to be angry about it anyway
ilivethere 5mo ago
> But the author just took pictures of food & expected a realistic response? Outside our tech-enabled bubble, there are folks who have been sold the idea that ChatGPT et al is a miracle worker capable of replacing dieticians, gym coaches, psychologists, etc. So it's VERY plausible to believe that there are folks out there snapping pics of their meals and asking GPT to spit out nutritional values.
- endymion-light 5mo ago
  That's a good point - and I think there's a wider core lack of knowledge outside of the bubble. I suppose I just expected this study to be a little less 'water is wet' which made me dissapointed, but that may be coming at it from a more technical perspective.
sarusso 5mo ago
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kalleboo 5mo ago
> But the author just took pictures of food & expected a realistic response? There are very popular apps on the App Store right now that are going viral among non-techie people that do exactly this, and they have no concept of how AI works. My wife was talking about one and I had to give her a reality check that the AI had no idea what ingredients were used to make the food. And she's a licensed nutritionalist. Studies like this create something to point at for people who are confused and serve as a springboard for a conversation in the media.
- endymion-light 5mo ago
  That's true - I suppose i'm just dissapointed that this study hasn't seemed to include those within any analysis. Being able to point out that the top 100 calorie counting apps on the app store return similiar results to simple frontier models would be of interest. I think i'm just dissapointed that this study doesn't go deep enough, and stays at a surface level statistical analysis of frontier models.
  - dpark 5mo ago
    I think it’s a very useful study specifically to debunk the apps that support this flow. None of those apps have magic. They cannot do better than the frontier models.
- senordevnyc 5mo ago
  licensed nutritionalist Nutritionist?
  - Insanity 5mo ago
    [flagged]
    - busssard 5mo ago
      [flagged]
  - 5mo ago
coldtea 5mo ago
>But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? If there are commercial services where you take pictures of food and are promised a realistic (paid for) response, then yes. And there are.
- endymion-light 5mo ago
  But I don't see them using those commercial services in this study - instead, they're using frontier model companies? Is Gemini advertising that you get a realistic calorie count from a picture? Maybe so - in which case i'd take it back!
  - coldtea 5mo ago
    Are commercial services anything more than just UI facades on top of frontier model APIs?
    - endymion-light 5mo ago
      Great point - and i'd love a study to address that. If the study pointed out that X services sit perfectly within the analysis found, I think that would be a fantastic study that would be enlightening & useful to show.
      - swiftcoder 5mo ago
        The app the study is based on is open-source, so you yourself can verify that it does indeed just call a frontier model with the same prompts used in the study
        endymion-light 5mo ago
        That's not really the same thing as what I'm saying - which is to investigate the applications specifically advertising AI calorie counting capabilities
larodi 5mo ago
> This entire article would be better suited to astrology.com than hackernews. I laughed, but you nailed it. Sadly so many people lack even basic understanding of LLMs and the ViT tower that makes it vLLM, that I expect a whole industry, similar to fortune telling, to emerge out of it.
something765478 5mo ago
> The prompt I used asks each model to return a confidence score (0 to 1) for every food item it identifies. All four models dutifully returned confidence scores for 100% of items. Surely we can use those to filter out bad estimates? This is a problem with the companies selling the AI models, not the customers. It is their responsibility to inform consumers about the limits of their services, and to train the models to say "I don't know, there is not enough information".
macleginn 5mo ago
It doesn't really matter if the model cannot make a good educated guess about calories in the food if it cannot give a consistent response given the same input.
jmyeet 5mo ago
> But the author just took pictures of food & expected a realistic response? If someone sent me a picture of a meal and asked me what the macros were or how many carbs this is, I would say "I can't tell from a photo. Nobody can". The problem is that current LLM chatbots don't seem to have a concept of telling you "I don't know", "you can't do that" or even "you're wrong". You can say that somebody shouldn't trust an LLM for this but it's going to be a problem that LLMs give nonsencial answers. What I find particularly amusing is that there are still technical people (generally, not anyone specifically) who seem unable to acknowledge that LLMs hallucinate and lie. There was a post on here recently that I couldn't find with some quick searching but the premise basically was that chatbots were trained like neurotypical people: A lot of affirmation and basically lying. Separately someone else characterized this NT style of communication as "tone poems" [1]. I keep thinking about that because to me that's so accurate. Dunning-Kruger is a common refrain on HN, for good reason. Another way to put this is how often people are confidently wrong. I really wonder if this is an inevitable consequence of NT communication because most neurodivergent ("ND") people I know are incredibly intentional in what they say and mean. [1]: https://news.ycombinator.com/item?id=47832952 https://news.ycombinator.com/item?id=47832952
layer8 5mo ago
The author is doing what a non-sophisticated user would be doing, or would want to be able to do, and estimating calories based on a photo has been an often-cited potential or promised AI use case in recent years years. It makes a lot of sense to test current general-purpose AI’s performance on it as a reality check. It also exemplifies how current AI offerings are still quite limited in their capabilities, because one would expect that they’d do the intelligent thing on their own that you had expected, instead of the user having to come up with a working methodology.
jvanderbot 5mo ago
I did this too! For months (almost a year) I used descriptions, pictures, and measurements of food to get rough calorie counts. My diet is pretty simple and repetitive. I would occasionally check the estimates, maybe once every few days for meals I wasn't already pretty sure of, and it was generally accurate. Where it was extremely inaccurate was on portions, and anyone who has dealt with computer vision could tell you, you can't get scale from a picture. So I'd have to weigh some meals or ingredients, which would generally make things more accurate again. So, I think it's possible, but you need multimodal data and grounded with regular checks.
- thechao 5mo ago
  About once a week I ask ChatGPT to give me a reasonable diet for recomp with weight loss. It consistently insisted I have at least 7 meals consisting of at least 30g of protein per meal, but the protein source can't be whey or casein. When I ask "why" it cites a bunch of studies ... but most of those "studies" are N=1 of a college or Olympic level athlete. If, instead, I grab a large scale lateral analysis, it says "3 meals" with about 1/2 of the protein. It'll defend both sides (mutually contradictory) to the death. NOTHING will budge it from its initial stance.
  - ifwinterco 5mo ago
    To be fair this is a reflection of the general state of nutritional science and the actual answer seems to be "it depends on your genes". Some people do well on 6 small meals, others do well on no breakfast and two large ones. Studies can't tell you anything useful about that, you have to experiment and find out what works best for you
    - machomaster 5mo ago
      The answer does not really depend on genes. There are personal preferences, there are sex differences (women prefer more carbs), and the biggest component is where you are and in which direction do you want to go to. But in terms of physiology the answer is quite clear: 1. The protein is the most important macro to get, no matter if bulking or cutting. It is the building block. 2. Whatever the amounts (0.8g-1.8g/kg of bodyweight, depends a bit on a situation and the willingness to lose some potential marginal gains), try to divide your daily protein somewhat evenly between meals. 3. Pareto principle, you get the most benefits by having 3 meals. 4 if you really care about small differences and want to optimize. 5 meals give negligible additional benefits, for professional athletes who want to be anal. 4. So basically eat at least 3 meals and up to whatever works for you practically speaking. It's not that difficult or ambiguous.
black6 5mo ago
> There's an incredibly serious lack of education with how LLMs & carb-counting works The public's education comes from the incessant marketing from AI companies that their models are the panacea for everything.
andrewvc 5mo ago
One of the biggest gaps is that people don't understand that food labels are allowed by the FDA to be off by up to 20% in terms of the number of actual calories! In the real world you need to calibrate your behavior with the results. Are you gaining weight? You'll need to eat less if you want to lose any. You can do all the math with nutrition labels and macros you want but that's all theoretical. See this study below for the 20% figure, as well as their experimental results on real food items (some even exceeded this threshold though most were within it). https://pmc.ncbi.nlm.nih.gov/articles/PMC3605747/?st_source=ai_mode#:~:text=The%20Food%20and%20Drug%20Administration,shown%20here%20as%20dashed%20lines. https://pmc.ncbi.nlm.nih.gov/articles/PMC3605747/?st_source=...
- bonoboTP 5mo ago
  A large part of the effectiveness in counting calories is that you pay more attention and make more conscious decisions and are less likely to "cheat" if you have to enter it in your food log. It's indeed like astrology. Simply thinking about personality traits and thinking through your life and your desires and goals and current situation is already beneficial to take charge and navigate your life.
  - ijk 5mo ago
    That's an interesting bit, where reducing friction too much can eliminate the side effect that is actually driving the desired results. Do you want to count calories, or do you want to lose weight? Sounds like it's possible to hyper-optimize calorie counting to the point that it becomes counter-productive...
  - smallmancontrov 5mo ago
    I'd take the opposite point of view: just thinking about life, desires, and goals is how people wind up paying $10 to Whole Foods for a slice of "healthy pizza" that is nutritionally identical to any other pizza but comes out of a cute stone oven and is displayed on a wooden platform next to green leafy plants. Vibes astrology is notoriously easy to exploit, both by your own sugar/salt/fat-seeking instincts and by unscrupulous commercial forces, let alone the two working together. The unique thing about calorie counting is that it cannot be exploited like vibes astrology. Not even with a 20% error margin, which is (probably not coincidentally) the caloric deficit targeted by standard dieting advice.
winddude 5mo ago
as a t-1 diabetic, this is exactly what we do for nearly every meal we eat, especially in restaurants, look at it and try to estimate the number of carbs.
datsci_est_2015 5mo ago
The obvious meme to invoke here is: - AI will solve all of our problems - No not like that! Are the trillion dollars sloshing around the AI economy well-invested if the refrain is always “you’re holding it wrong”? So we’re trying to define, through trial and error, what problems “AI” will actually solve, and this paper is one of the many cobblestones on that road.
- endymion-light 5mo ago
  i mean it's more like "AI can solve this one problem, but it needs X, Y, Z, because it's not a omnipotent god entity" "I tried it without any of those things and it didn't work - this is worthless tech!" I don't know if more accurate calorie counting using AI exists - but it's like being upset that the screwdriver isn't gluing wood. AI is far more than frontier LLMs.
  - sleepybrett 5mo ago
    > "AI can solve this one problem, but it needs X, Y, Z, because it's not a omnipotent god entity" 0 advertisements from openai or anthropic say this. They all sell you an omnipotent god entity.
    - pertymcpert 5mo ago
      Skill issue in thinking.
  - datsci_est_2015 5mo ago
    There are plenty of positions on the spectrum from “omnipotent God entity” and “Casio SL-300SV”. What does the current valuation of LLMaaS companies represent though? LLMs are certainly not worthless, that’s a strawman in the same way my statement “AI will solve all of our problems” is a strawman. The question of their worth is being explored. “AI is a black box that can solve problems”. Which problems? How consistently? At what cost? How quickly?
  - muwtyhg 5mo ago
chromacity 5mo ago
> There's an incredibly serious lack of education with how LLMs & carb-counting works Oh! Do the vendors offer trainings to make sure the users understand how LLMs work? If not, surely, the LLM itself is trained to know its limitations and politely decline in situations like that?... The #1 use case for this tech is "here's a problem I don't feel like solving, let's have a computer do magic". It's how it's advertised on TV, it's how it promoted in the software I already use. Food preparation? Travel planning? Shopping? Tutoring your children? You can do anything now! I just talked to a realtor who will make a killing on a real estate transaction. Instead of offering human insights, they sent me "AI reviews" of several properties. The AI has never been to any of these properties and has no idea how they actually look like. But I guess it's how we operate now as a society. If you go to eBay, every other listing description for used items is AI-generated. This is an official platform feature for sellers. The AI doesn't know the condition of the item or what's included or missing. Doesn't matter, it's magic. It's AGI, it will figure it out. Most of the uses of AI I encounter as a consumer are like that, and the companies selling this tech are 100% complicit.
- endymion-light 5mo ago
  yes - the vast majority of labs offer a whole host of training material for users to understand how LLMs work. There's entire course websites created by each of the major vendors specifically to understand how LLMs work. Here's a couple of examples: https://academy.openai.com/public/content https://academy.openai.com/public/content https://www.commonsense.org/education/articles/practical-tips-for-teachers-to-use-ai https://www.commonsense.org/education/articles/practical-tip... Quote >Getting the most out of generative AI depends on what you put in. To quote our Outreach team, "It's a tool, not magic!" As the technology evolves, more and more chatbots are designed for specific purposes. https://www.anthropic.com/learn https://www.anthropic.com/learn https://anthropic.skilljar.com/ai-fluency-framework-foundations https://anthropic.skilljar.com/ai-fluency-framework-foundati... https://grow.google/ai https://grow.google/ai All of the above are completely free, you could start 3 coursers today that specifically teach you how AI tools work in practice. yes, this is different from the marketing that some of these tools use, but these resources are there, free and available. Maybe we need to create a form of driving license for responsible AI use, but saying the resources don't exist is not accurate
SirMaster 5mo ago
How about instead of blaming the user for not understanding how AI works, the AI makers stop letting their chatbots answer questions so confidently that they clearly can't answer... If I ask the AI about some health issue, it says something along the lines of warning I'm not a doctor etc. So if I show it a picture and ask it to tell me the carbs, how about a warning telling me it can try, but that it probably wont be very accurate.
deleted 5mo ago
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YeGoblynQueenne 5mo ago
>> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? The aim of the study was to understand the variation in results returned by models and how that could cause risks for patients using those models. The main result was measuring within-model variation. From the pre-print (https://www.diabettech.com/wp-content/uploads/2026/04/diabettech_preprint-1.pdf https://www.diabettech.com/wp-content/uploads/2026/04/diabet...): We aimed to characterise the within-image reproducibility of carbohydrate estimates from four large language model (LLM) vision APIs and to quantify the clinical risk for insulin dosing, stratifying accuracy by reference value quality. Methods Thirteen food photographs were each submitted 495–561 times to four LLM vision APIs (GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Pro, Gemini 3.1 Pro Preview) using an identical structured prompt adapted from the iAPS automated insulin delivery system (26,904 total queries, temperature 0.01). The primary outcome was within- image variation (coefficient of variation [CV], range, distributional normality). Secondary outcomes included accuracy against reference values for nine images, stratified by quality tier (packet label, weighed/measured, portioned, or visual estimate). Clinical risk was translated at an insulin-to-carbohydrate ratio of 1:10. >> I'd like to see this study done using any kind of actual grounding knowledge, seeing what mistakes AI makes when attempting to query ground truth from picture analysis - there would at least be an interesting result methodology in that The ground truth was established by the author. There's an appendix in the pre-print (Appendix I) that describes the methodology. Methods are described in page 4 of the pre-print: Reference values for accuracy analysis For nine of the thirteen images, the author estimated the carbohydrate content using methods described in Appendix 1. Reference quality was categorised into four tiers: Tier 1 (packet label): Carbohydrate values derived from manufacturer nutrition labelling. Two images (cheese sandwich, soup with bread) used bread with labelled carbohydrate content of 20 g per slice. Tier 2 (weighed/measured): Portions directly weighed and cross-referenced with established composition data. Three images (Bakewell tart, bakery cookie, breakfast burrito). Tier 3 (portioned): Portions estimated by the author (not weighed) and combined with USDA composition data. Three images (roast dinner, chilli con carne with rice, stuffed pork loin). Tier 4 (visual estimate): Portions and composition estimated from visual inspection. One image (churros). For the four restaurant dishes (pizza capricciosa, eggs benedict, crema catalana, paella), no reference value was established. These images were used for the primary reproducibility analysis only. Carbohydrate values follow the EU convention with dietary fibre excluded.
toasty228 5mo ago
> But the author just took pictures of food & expected a realistic response? There are dozens of ios/android apps with 100-300k+ ratings and god knows how many millions of installs which do exactly this "Cal AI - Food Calorie Tracker: Just snap a photo and our smart AI calorie tracker analyzes your meal instantly." 308k ratings on ios, 264k ratings on androids easily 5-10m installs across both platforms.
whstl 5mo ago
> But the author just took pictures of food & expected a realistic response? You say this (and I agree), but I know of quite a few companies in this area, including a couple accelerated by YCombinator, and that's pretty much 100% of what they do in their backend.
sleepybrett 5mo ago
> There's an incredibly serious lack of education with how LLMs & carb-counting works. This entire article would be better suited to astrology.com than hackernews. This is because the people who promote these technologies, and the companies that sell these technologies, engage in a massive amount of puffery (aka hyperbolizing aka just straight telling lies). These technologies are painted as the magical solution to whatever problem you have (all it costs you is a few tens of thousands of tokens, aka your water supply). There is literally nothing they CAN'T do if you will just let us build these gigantic small town destroying, noise polluting, water and electricity hungry 'AI data-centers'. So that we can use those datacenters to sell you more tokens to put into their slot machines.
slumpt_ 5mo ago
This is how a lot of regular people are engaging with AI, whether you consider it silly or not.
jrm4 5mo ago
This strikes me as a good "meta" article, though. As in, yes, people here probably don't need this. But perhaps a lot of other people do.
mathgradthrow 5mo ago
a realistic response? What's a realistic response to "how many calories are in an avocado?" If you are counting calories, you don't want the answer to "how many calories are in the average avocado?", you want to know how many calories are in this avocado. Remember that bodyweight is roughly linear with BMR, so a 10% error in calorie counting is an extra 10% of bodyweight.
cyanydeez 5mo ago
So, do you think his methodology is closer to a computer scientist, or someone on instagram.
joelthelion 5mo ago
In my opinion there is also a deficiency of the models who should be able to say "I don't know" when asked for something unreasonable.
deleted 5mo ago
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nclin_ 5mo ago
He was directly addressing apps that claim to do so, and proving that they can't, with laypeople who might have diabetes as the target audience. It's more a data-driven pub test, I think it explains itself well. The question is - are those apps actually so simplistic? Or is this a strawman.
- endymion-light 5mo ago
  I guess that's partially my original point summarised; I'd like to see this criticism levied at the apps advertising it - rather than using something that doesn't

nextlevelwizard 5mo ago

As someone who used to do this. OpenAI models refuse to look up calories unless you explicitly tell them to and even then it is a hit and miss even if you tell them exactly what the product is. Easiest way to get good calculation is to just take a photo of the nutrition label or feed that info in by hand. Funny thing is 4o did look up calories but I guess it was too good for this world

the_duke 5mo ago
I exclusively use thinking mode, which is slower but much more likely to double-check things with web search etc.
- nextlevelwizard 5mo ago
  Maybe. I stopped using OpenAI a while ago. But taking pictures of the nutrition labels was good enough

swalsh 5mo ago

It amazes me how much people try to build AI systems relying on nothing more than the models knowledge. I suspect a great deal of "failed" AI experiments we keep reading are people just not having any idea how to use AI at what its good at.

furyofantares 5mo ago

From the text of the article I believe the author is implying there are apps doing exactly this, and so this is why it was studied that way. Had the author written the article themselves rather than an LLM their motivation probably would have been clearer.

Brendinooo 5mo ago
> there are apps doing exactly this Yeah, for sure there are. And people will just ask ChatGPT as well. The funny thing is that for people who are just trying to lose weight without managing any health issues precisely, this type of extreme variance doesn't really matter, because the mere act of consciously quantifying food consumption is, based on my experience counting calories, the single biggest factor in success with weight loss.
- criley2 5mo ago
  I actually think "just asking ChatGPT" is fine, because A) the data in these apps is suspect at best and B) the data behind calories is also pretty suspect (but we all play along because we can adjust other variables to make it all "work" well enough). Once or twice a year I spend a few weeks meticulously measuring ingredients/cooked foods and recording calories and on complex recipes apps are next to useless at getting accurate data. You're trying to input five or ten relevant ingredients, and then weighing your cooked outcome to try and divide the ingredients by proportion. Frankly it's a mess and most people aren't doing it for home cooked meals, and are getting very lossy outcomes (weighing cooked chicken and marking it as raw chicken, etc) With reasoning and tool calling (combined with me meticulously weighing before and after), it's producing fine data for my purposes.
  - ijk 5mo ago
    I was complaining about AI generated clothes being misleading marketing, deceiving customers as to whether the garment even exists. And then I learned that the pre-AI norms weren't any less fictional: they made an exemplar garment and did photoshoots, sure, but then they send the pictures and patterns to the lowest bidder factories with permission to make whatever edits are necessary to make it cheap and manufactureable. The whole thing was already a simulacrum.

throwaw12 5mo ago

I feel like you didn't understand the goal of this study > The DTN-UK stated earlier this year that generic LLMs must never be used as autonomous advisory calculators for insulin delivery. This data is the quantitative evidence base for that statement. This study is to prove that you should not rely on LLMs

fabian2k 5mo ago
The paper itself is a lot clearer about the purpose. The blog post reads very clickbaity and doesn't really explain the context well.
- Aurornis 5mo ago
  I disagree, it clearly explains that AI carb counting apps are a problem and shouldn’t be used. They’re writing in a neutral way that reaches their audience without lecturing or being condescending. They lead the reader to the conclusion rather than shoving it at them. I think that’s why it’s triggering so many angry comments on HN, but it’s effective for the audience they’re writing for (non technical people who may need convincing but don’t like being preached at)
snapcaster 5mo ago
But it's stupid. If i smack myself in the head with a hammer is that proof hammers shouldn't be relied on?
- deleted 5mo ago
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- jmye 5mo ago
  Are there start-ups led by idiots suggesting that smacking yourself in the head with a hammer will help treat your diabetes? If not, then perhaps there's a problem in your analogy.
- fc417fc802 5mo ago
  If you smack yourself in the head with a hammer and it injures you that's evidence that smacking people in the head with hammers is bad and shouldn't be done, right?

zipy124 5mo ago

Honestly it's scary how misunderstood this is by the general public, the media and EVEN scientists. There is a shocking amount of Computer Vision tasks where the scientists claim you can get X info from a picture of Y and it's like, even with ML/AI you can't extract data where there isn't any. The fact I can add an arbritrary amount of high-calorie fat to a meal without changing the appearance by defintion shows it's pointless. A 1000 calorie and 100 calorie milkshake can look identical, and you'd have no way of working that out via an image even if it was a super-intelligent system. Similarly I see it in things like extracting material of an object from an image of it in serious research papers, which for the same reason cannot be done, since how an object looks has very little to do with what its made of, else painting and other art would clearly be impossible. The information is just not there within the data.

mortenjorck 5mo ago
It’s like CSI “enhance!” AI image upscaling. People will do it, see it fabricated details, and then draw the wrong lesson from it, that “AI fabricates things!” when that is exactly what they asked the model to do and there is no magic math that would extract ground truth that was never in the image to begin with.

InsideOutSanta 5mo ago

There are apps in the app store right now that pretend to do this kind of thing, so having somebody actually show that it doesn't work is valuable, even if we already knew the outcome ahead of time.

endymion-light 5mo ago
I suppose i'd much rather a study analyse the apps in the app store that are attempting and claiming to do that kind of thing - rather than the base model they might be using.

giancarlostoro 5mo ago

> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? Reminds me of that one youtube video (I forget who it is so I have no idea how to pull it up) where he turns on the camera on his phone for ChatGPT and asks it what everything it sees weighs, then puts it on a scale, and ChatGPT was never right, ever, which makes sense, I couldnt tell you what most things weigh on sight alone either, but ChatGPT often got it dramatically off. I got the feeling he thought it was terrible AI for this, but I don't think a model looking at an image of something and trying to guess its weight / calories / etc... is a reason to call an AI model bad...

wat10000 5mo ago
People do not understand this stuff at all. So many times I've seen people post the output from one of these services with full confidence that it has to be correct because it came from a computer.
- Izkata 5mo ago
  Between the marketing and the hype, plenty of people believe we already achieved true sci-fi-style superhuman general AI a year or two ago.

throwaway613746 5mo ago

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Aurornis 5mo ago

> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? The article explains this: There are apps targeting people with diabetes that claim to count your carbs with AI. > If you’re using AI carb counting in a diabetes app Before you dismiss a study, try to understand where it’s coming from. The authors of the study weren’t stupid. They knew the LLMs would provide poor results. They ran the study to quantify it and create a resource to spread the information in response to the rise of AI carb counting apps.

ilivethere 5mo ago
Typical case of the "curse of knowledge". We deal with AI on a daily basis on the technical level, so it's very easy to forget that the "common" folk really still believe that AI can replace dieticians, gym coaches, etc
endymion-light 5mo ago
I don't believe the authors of this study are stupid. If there are apps targeting people with diabetes that claims to count your carbs with AI, why haven't those been analysed? That would be a far more effective claim. I based the study off of the clickbait article that they wrote about the study - i'll read through the study to see whether they analyse that, but it would be far more effective to see if the 'carb-counting' AI app is returning similiar results to the frontier model - that's an interesting result that actually can forward discussion.
- Aurornis 5mo ago
  > If there are apps targeting people with diabetes that claims to count your carbs with AI, why haven't those been analysed? That would be a far more effective claim. Because the apps aren’t going to let you submit 29,000 automated requests for statistical analysis. And if you did, the authors of those apps would just release an update saying they changed models and try to dismiss the study. The vitriol against this article on HN is sad. Commenters who agree with the article and its conclusions are grasping for reasons to be angry about it anyway

ilivethere 5mo ago

> But the author just took pictures of food & expected a realistic response? Outside our tech-enabled bubble, there are folks who have been sold the idea that ChatGPT et al is a miracle worker capable of replacing dieticians, gym coaches, psychologists, etc. So it's VERY plausible to believe that there are folks out there snapping pics of their meals and asking GPT to spit out nutritional values.

endymion-light 5mo ago
That's a good point - and I think there's a wider core lack of knowledge outside of the bubble. I suppose I just expected this study to be a little less 'water is wet' which made me dissapointed, but that may be coming at it from a more technical perspective.

sarusso 5mo ago

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kalleboo 5mo ago

> But the author just took pictures of food & expected a realistic response? There are very popular apps on the App Store right now that are going viral among non-techie people that do exactly this, and they have no concept of how AI works. My wife was talking about one and I had to give her a reality check that the AI had no idea what ingredients were used to make the food. And she's a licensed nutritionalist. Studies like this create something to point at for people who are confused and serve as a springboard for a conversation in the media.

endymion-light 5mo ago
That's true - I suppose i'm just dissapointed that this study hasn't seemed to include those within any analysis. Being able to point out that the top 100 calorie counting apps on the app store return similiar results to simple frontier models would be of interest. I think i'm just dissapointed that this study doesn't go deep enough, and stays at a surface level statistical analysis of frontier models.
- dpark 5mo ago
  I think it’s a very useful study specifically to debunk the apps that support this flow. None of those apps have magic. They cannot do better than the frontier models.
senordevnyc 5mo ago
licensed nutritionalist Nutritionist?
- Insanity 5mo ago
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  - busssard 5mo ago
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coldtea 5mo ago

>But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? If there are commercial services where you take pictures of food and are promised a realistic (paid for) response, then yes. And there are.

endymion-light 5mo ago
But I don't see them using those commercial services in this study - instead, they're using frontier model companies? Is Gemini advertising that you get a realistic calorie count from a picture? Maybe so - in which case i'd take it back!
- coldtea 5mo ago
  Are commercial services anything more than just UI facades on top of frontier model APIs?
  - endymion-light 5mo ago
    Great point - and i'd love a study to address that. If the study pointed out that X services sit perfectly within the analysis found, I think that would be a fantastic study that would be enlightening & useful to show.
    - swiftcoder 5mo ago
      The app the study is based on is open-source, so you yourself can verify that it does indeed just call a frontier model with the same prompts used in the study
      - endymion-light 5mo ago
        That's not really the same thing as what I'm saying - which is to investigate the applications specifically advertising AI calorie counting capabilities

larodi 5mo ago

> This entire article would be better suited to astrology.com than hackernews. I laughed, but you nailed it. Sadly so many people lack even basic understanding of LLMs and the ViT tower that makes it vLLM, that I expect a whole industry, similar to fortune telling, to emerge out of it.

something765478 5mo ago

> The prompt I used asks each model to return a confidence score (0 to 1) for every food item it identifies. All four models dutifully returned confidence scores for 100% of items. Surely we can use those to filter out bad estimates? This is a problem with the companies selling the AI models, not the customers. It is their responsibility to inform consumers about the limits of their services, and to train the models to say "I don't know, there is not enough information".

macleginn 5mo ago

It doesn't really matter if the model cannot make a good educated guess about calories in the food if it cannot give a consistent response given the same input.

jmyeet 5mo ago

> But the author just took pictures of food & expected a realistic response? If someone sent me a picture of a meal and asked me what the macros were or how many carbs this is, I would say "I can't tell from a photo. Nobody can". The problem is that current LLM chatbots don't seem to have a concept of telling you "I don't know", "you can't do that" or even "you're wrong". You can say that somebody shouldn't trust an LLM for this but it's going to be a problem that LLMs give nonsencial answers. What I find particularly amusing is that there are still technical people (generally, not anyone specifically) who seem unable to acknowledge that LLMs hallucinate and lie. There was a post on here recently that I couldn't find with some quick searching but the premise basically was that chatbots were trained like neurotypical people: A lot of affirmation and basically lying. Separately someone else characterized this NT style of communication as "tone poems" [1]. I keep thinking about that because to me that's so accurate. Dunning-Kruger is a common refrain on HN, for good reason. Another way to put this is how often people are confidently wrong. I really wonder if this is an inevitable consequence of NT communication because most neurodivergent ("ND") people I know are incredibly intentional in what they say and mean. [1]: https://news.ycombinator.com/item?id=47832952 https://news.ycombinator.com/item?id=47832952

layer8 5mo ago

The author is doing what a non-sophisticated user would be doing, or would want to be able to do, and estimating calories based on a photo has been an often-cited potential or promised AI use case in recent years years. It makes a lot of sense to test current general-purpose AI’s performance on it as a reality check. It also exemplifies how current AI offerings are still quite limited in their capabilities, because one would expect that they’d do the intelligent thing on their own that you had expected, instead of the user having to come up with a working methodology.

jvanderbot 5mo ago

I did this too! For months (almost a year) I used descriptions, pictures, and measurements of food to get rough calorie counts. My diet is pretty simple and repetitive. I would occasionally check the estimates, maybe once every few days for meals I wasn't already pretty sure of, and it was generally accurate. Where it was extremely inaccurate was on portions, and anyone who has dealt with computer vision could tell you, you can't get scale from a picture. So I'd have to weigh some meals or ingredients, which would generally make things more accurate again. So, I think it's possible, but you need multimodal data and grounded with regular checks.

thechao 5mo ago
About once a week I ask ChatGPT to give me a reasonable diet for recomp with weight loss. It consistently insisted I have at least 7 meals consisting of at least 30g of protein per meal, but the protein source can't be whey or casein. When I ask "why" it cites a bunch of studies ... but most of those "studies" are N=1 of a college or Olympic level athlete. If, instead, I grab a large scale lateral analysis, it says "3 meals" with about 1/2 of the protein. It'll defend both sides (mutually contradictory) to the death. NOTHING will budge it from its initial stance.
- ifwinterco 5mo ago
  To be fair this is a reflection of the general state of nutritional science and the actual answer seems to be "it depends on your genes". Some people do well on 6 small meals, others do well on no breakfast and two large ones. Studies can't tell you anything useful about that, you have to experiment and find out what works best for you
  - machomaster 5mo ago
    The answer does not really depend on genes. There are personal preferences, there are sex differences (women prefer more carbs), and the biggest component is where you are and in which direction do you want to go to. But in terms of physiology the answer is quite clear: 1. The protein is the most important macro to get, no matter if bulking or cutting. It is the building block. 2. Whatever the amounts (0.8g-1.8g/kg of bodyweight, depends a bit on a situation and the willingness to lose some potential marginal gains), try to divide your daily protein somewhat evenly between meals. 3. Pareto principle, you get the most benefits by having 3 meals. 4 if you really care about small differences and want to optimize. 5 meals give negligible additional benefits, for professional athletes who want to be anal. 4. So basically eat at least 3 meals and up to whatever works for you practically speaking. It's not that difficult or ambiguous.

black6 5mo ago

> There's an incredibly serious lack of education with how LLMs & carb-counting works The public's education comes from the incessant marketing from AI companies that their models are the panacea for everything.

andrewvc 5mo ago

One of the biggest gaps is that people don't understand that food labels are allowed by the FDA to be off by up to 20% in terms of the number of actual calories! In the real world you need to calibrate your behavior with the results. Are you gaining weight? You'll need to eat less if you want to lose any. You can do all the math with nutrition labels and macros you want but that's all theoretical. See this study below for the 20% figure, as well as their experimental results on real food items (some even exceeded this threshold though most were within it). https://pmc.ncbi.nlm.nih.gov/articles/PMC3605747/?st_source=ai_mode#:~:text=The%20Food%20and%20Drug%20Administration,shown%20here%20as%20dashed%20lines. https://pmc.ncbi.nlm.nih.gov/articles/PMC3605747/?st_source=...

bonoboTP 5mo ago
A large part of the effectiveness in counting calories is that you pay more attention and make more conscious decisions and are less likely to "cheat" if you have to enter it in your food log. It's indeed like astrology. Simply thinking about personality traits and thinking through your life and your desires and goals and current situation is already beneficial to take charge and navigate your life.
- ijk 5mo ago
  That's an interesting bit, where reducing friction too much can eliminate the side effect that is actually driving the desired results. Do you want to count calories, or do you want to lose weight? Sounds like it's possible to hyper-optimize calorie counting to the point that it becomes counter-productive...
- smallmancontrov 5mo ago
  I'd take the opposite point of view: just thinking about life, desires, and goals is how people wind up paying $10 to Whole Foods for a slice of "healthy pizza" that is nutritionally identical to any other pizza but comes out of a cute stone oven and is displayed on a wooden platform next to green leafy plants. Vibes astrology is notoriously easy to exploit, both by your own sugar/salt/fat-seeking instincts and by unscrupulous commercial forces, let alone the two working together. The unique thing about calorie counting is that it cannot be exploited like vibes astrology. Not even with a 20% error margin, which is (probably not coincidentally) the caloric deficit targeted by standard dieting advice.

winddude 5mo ago

as a t-1 diabetic, this is exactly what we do for nearly every meal we eat, especially in restaurants, look at it and try to estimate the number of carbs.

datsci_est_2015 5mo ago

The obvious meme to invoke here is: - AI will solve all of our problems - No not like that! Are the trillion dollars sloshing around the AI economy well-invested if the refrain is always “you’re holding it wrong”? So we’re trying to define, through trial and error, what problems “AI” will actually solve, and this paper is one of the many cobblestones on that road.

endymion-light 5mo ago
i mean it's more like "AI can solve this one problem, but it needs X, Y, Z, because it's not a omnipotent god entity" "I tried it without any of those things and it didn't work - this is worthless tech!" I don't know if more accurate calorie counting using AI exists - but it's like being upset that the screwdriver isn't gluing wood. AI is far more than frontier LLMs.
- sleepybrett 5mo ago
  > "AI can solve this one problem, but it needs X, Y, Z, because it's not a omnipotent god entity" 0 advertisements from openai or anthropic say this. They all sell you an omnipotent god entity.
  - pertymcpert 5mo ago
    Skill issue in thinking.
- datsci_est_2015 5mo ago
  There are plenty of positions on the spectrum from “omnipotent God entity” and “Casio SL-300SV”. What does the current valuation of LLMaaS companies represent though? LLMs are certainly not worthless, that’s a strawman in the same way my statement “AI will solve all of our problems” is a strawman. The question of their worth is being explored. “AI is a black box that can solve problems”. Which problems? How consistently? At what cost? How quickly?
- muwtyhg 5mo ago

chromacity 5mo ago

> There's an incredibly serious lack of education with how LLMs & carb-counting works Oh! Do the vendors offer trainings to make sure the users understand how LLMs work? If not, surely, the LLM itself is trained to know its limitations and politely decline in situations like that?... The #1 use case for this tech is "here's a problem I don't feel like solving, let's have a computer do magic". It's how it's advertised on TV, it's how it promoted in the software I already use. Food preparation? Travel planning? Shopping? Tutoring your children? You can do anything now! I just talked to a realtor who will make a killing on a real estate transaction. Instead of offering human insights, they sent me "AI reviews" of several properties. The AI has never been to any of these properties and has no idea how they actually look like. But I guess it's how we operate now as a society. If you go to eBay, every other listing description for used items is AI-generated. This is an official platform feature for sellers. The AI doesn't know the condition of the item or what's included or missing. Doesn't matter, it's magic. It's AGI, it will figure it out. Most of the uses of AI I encounter as a consumer are like that, and the companies selling this tech are 100% complicit.

endymion-light 5mo ago
yes - the vast majority of labs offer a whole host of training material for users to understand how LLMs work. There's entire course websites created by each of the major vendors specifically to understand how LLMs work. Here's a couple of examples: https://academy.openai.com/public/content https://academy.openai.com/public/content https://www.commonsense.org/education/articles/practical-tips-for-teachers-to-use-ai https://www.commonsense.org/education/articles/practical-tip... Quote >Getting the most out of generative AI depends on what you put in. To quote our Outreach team, "It's a tool, not magic!" As the technology evolves, more and more chatbots are designed for specific purposes. https://www.anthropic.com/learn https://www.anthropic.com/learn https://anthropic.skilljar.com/ai-fluency-framework-foundations https://anthropic.skilljar.com/ai-fluency-framework-foundati... https://grow.google/ai https://grow.google/ai All of the above are completely free, you could start 3 coursers today that specifically teach you how AI tools work in practice. yes, this is different from the marketing that some of these tools use, but these resources are there, free and available. Maybe we need to create a form of driving license for responsible AI use, but saying the resources don't exist is not accurate

SirMaster 5mo ago

How about instead of blaming the user for not understanding how AI works, the AI makers stop letting their chatbots answer questions so confidently that they clearly can't answer... If I ask the AI about some health issue, it says something along the lines of warning I'm not a doctor etc. So if I show it a picture and ask it to tell me the carbs, how about a warning telling me it can try, but that it probably wont be very accurate.

deleted 5mo ago

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YeGoblynQueenne 5mo ago

>> But the author just took pictures of food & expected a realistic response? Is this genuinely what amounts to a study in AI? The aim of the study was to understand the variation in results returned by models and how that could cause risks for patients using those models. The main result was measuring within-model variation. From the pre-print (https://www.diabettech.com/wp-content/uploads/2026/04/diabettech_preprint-1.pdf https://www.diabettech.com/wp-content/uploads/2026/04/diabet...): We aimed to characterise the within-image reproducibility of carbohydrate estimates from four large language model (LLM) vision APIs and to quantify the clinical risk for insulin dosing, stratifying accuracy by reference value quality. Methods Thirteen food photographs were each submitted 495–561 times to four LLM vision APIs (GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Pro, Gemini 3.1 Pro Preview) using an identical structured prompt adapted from the iAPS automated insulin delivery system (26,904 total queries, temperature 0.01). The primary outcome was within- image variation (coefficient of variation [CV], range, distributional normality). Secondary outcomes included accuracy against reference values for nine images, stratified by quality tier (packet label, weighed/measured, portioned, or visual estimate). Clinical risk was translated at an insulin-to-carbohydrate ratio of 1:10. >> I'd like to see this study done using any kind of actual grounding knowledge, seeing what mistakes AI makes when attempting to query ground truth from picture analysis - there would at least be an interesting result methodology in that The ground truth was established by the author. There's an appendix in the pre-print (Appendix I) that describes the methodology. Methods are described in page 4 of the pre-print: Reference values for accuracy analysis For nine of the thirteen images, the author estimated the carbohydrate content using methods described in Appendix 1. Reference quality was categorised into four tiers: Tier 1 (packet label): Carbohydrate values derived from manufacturer nutrition labelling. Two images (cheese sandwich, soup with bread) used bread with labelled carbohydrate content of 20 g per slice. Tier 2 (weighed/measured): Portions directly weighed and cross-referenced with established composition data. Three images (Bakewell tart, bakery cookie, breakfast burrito). Tier 3 (portioned): Portions estimated by the author (not weighed) and combined with USDA composition data. Three images (roast dinner, chilli con carne with rice, stuffed pork loin). Tier 4 (visual estimate): Portions and composition estimated from visual inspection. One image (churros). For the four restaurant dishes (pizza capricciosa, eggs benedict, crema catalana, paella), no reference value was established. These images were used for the primary reproducibility analysis only. Carbohydrate values follow the EU convention with dietary fibre excluded.

toasty228 5mo ago

> But the author just took pictures of food & expected a realistic response? There are dozens of ios/android apps with 100-300k+ ratings and god knows how many millions of installs which do exactly this "Cal AI - Food Calorie Tracker: Just snap a photo and our smart AI calorie tracker analyzes your meal instantly." 308k ratings on ios, 264k ratings on androids easily 5-10m installs across both platforms.

whstl 5mo ago

> But the author just took pictures of food & expected a realistic response? You say this (and I agree), but I know of quite a few companies in this area, including a couple accelerated by YCombinator, and that's pretty much 100% of what they do in their backend.

sleepybrett 5mo ago

> There's an incredibly serious lack of education with how LLMs & carb-counting works. This entire article would be better suited to astrology.com than hackernews. This is because the people who promote these technologies, and the companies that sell these technologies, engage in a massive amount of puffery (aka hyperbolizing aka just straight telling lies). These technologies are painted as the magical solution to whatever problem you have (all it costs you is a few tens of thousands of tokens, aka your water supply). There is literally nothing they CAN'T do if you will just let us build these gigantic small town destroying, noise polluting, water and electricity hungry 'AI data-centers'. So that we can use those datacenters to sell you more tokens to put into their slot machines.

slumpt_ 5mo ago

This is how a lot of regular people are engaging with AI, whether you consider it silly or not.

jrm4 5mo ago

This strikes me as a good "meta" article, though. As in, yes, people here probably don't need this. But perhaps a lot of other people do.

mathgradthrow 5mo ago

a realistic response? What's a realistic response to "how many calories are in an avocado?" If you are counting calories, you don't want the answer to "how many calories are in the average avocado?", you want to know how many calories are in this avocado. Remember that bodyweight is roughly linear with BMR, so a 10% error in calorie counting is an extra 10% of bodyweight.

cyanydeez 5mo ago

So, do you think his methodology is closer to a computer scientist, or someone on instagram.

joelthelion 5mo ago

In my opinion there is also a deficiency of the models who should be able to say "I don't know" when asked for something unreasonable.

deleted 5mo ago

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nclin_ 5mo ago

He was directly addressing apps that claim to do so, and proving that they can't, with laypeople who might have diabetes as the target audience. It's more a data-driven pub test, I think it explains itself well. The question is - are those apps actually so simplistic? Or is this a strawman.

endymion-light 5mo ago
I guess that's partially my original point summarised; I'd like to see this criticism levied at the apps advertising it - rather than using something that doesn't