I just spent too much time today arguing with ChatGPT that the nutritional information it gave me for pearl barley seemed off. It couldn’t reason whether it was for cooked pearl barley or not. Having had a look online, it’s no wonder.
Show HN: OpenNutrition – A free, public nutrition database
71–80 of 160 posts
Re: Show HN: OpenNutrition – A free, public nutrition database
#72Took a minute to find things that are in the DB, but once I did this is pretty slick. If I can join the endless queue of feature requests, the ability to scale the portion size and update the nutrition facts would be great. I've been tracking my macros daily for about 18 months and for me to hit my protein and stay under my calories I do a lot of what I call speculative food tracking. Sitting on the couch, thinking about a snack, if I go into the kitchen without a plan I'm gonna eat a lot of unintentional things but if I can go "okay, I have about 300 calories to spare and I'm short 40g protein today, so I can put 5 oz chicken breast on a 60 calorie wheat pita with some hot sauce for (calorie) free flavor and I'll win today".
Also IDK where AI is wrt automated scraping but I've had some success feeding recipes into AI and getting the nutrition facts out. The ability to plop a URL in and get a scraped recipe with a name and nutrition facts would be immense.
Re: Show HN: OpenNutrition – A free, public nutrition database
#73What's the main difference between this and OpenFoodFacts really ?
The problem with OpenFoodFacts is that it just has nutrition label info for packaged goods.
So, very little nutrient info beyond calories and protein. No info about micronutrients. No info about minerals, vitamins, amino acids, fatty acids.
It's useless for nutrition tracking since if you're eating packaged food, then you already have that information yourself.
It doesn't answer basic questions like "I ate 100g of extra firm tofu, how did it move me towards my daily mineral/vitamin targets?"
Re: Show HN: OpenNutrition – A free, public nutrition database
#74>> User-generated databases can be unreliable >> Foods discovered via these searches are fed back into the database, Aren’t LLMs also unreliable? How do you ensure the new content is from an authoritative, accurate source? How do you ensure the numbers that make it into the database are actually what the source provided? According to the Methodology/About page >> The LLM is tasked with creating complete nutritional v…
Not really sure how the author thinks anybody who tracks their calories/macros seriously is going to trust a website that literally just makes up values for the vitamins, minerals, etc: > TL;DR: They are estimates from giving an LLM (generally o3 mini high due to cost, some o1 preview) a large corpus of grounding data to reason over and asking it to use its general world knowledge to return estimates it was confident…
That’s the best part! People don’t care and won’t check! They’ll just pay money!
Most of the data being close enough to be better than nothing and not actively harmful + a disclaimer and the author is absolved of all responsibility!
Even better, this will now be used in all sorts of other apps, analyses, and for training other LLMs! And I expect all those will also prominently include an “all of this was genereated by an LLM” disclamers. For sure.
Re: Show HN: OpenNutrition – A free, public nutrition database
#75> Final nutritional data is generated by providing a reasoning model with a large corpus of grounding data. The LLM is tasked with creating complete nutritional values, explicitly explaining the rationale behind each value it generates. Outputs undergo rigorous validation steps, including cross-checking with advanced auditing models such as OpenAI’s o1-pro, which has proven especially proficient at performing high-quality random audits. In practice, o1-pro frequently provided clearer and more substantive insights than manual audits alone.
This is not a dataset. This is an insult to the very idea of data. This is the most anti-scientific post I have ever seen voted to the top of HN. Truth about the world is not derived from three LLMs stacked on top of each other in a trenchcoat.
Re: Show HN: OpenNutrition – A free, public nutrition database
#76Can you make the app available outside the US ? Can't download it and I have active recurring subscriptions that will be canceled if I switch my Apple ID region.
Re: Show HN: OpenNutrition – A free, public nutrition database
#77[deleted]
Re: Show HN: OpenNutrition – A free, public nutrition database
#78Earlier quoted context omitted.
> Those rigorous validation steps were also created with LLMs, correct? Not really. I do explain in the methodology post how good o1-pro is at the task, but there was a lot of manual effort involved in coming to that conclusion with my own effort to review the LLM's reasoning, and even still, o1-pro is not perfect.
Nice! Thanks for responding. >> Outputs undergo rigorous validation steps, including cross-checking with advanced auditing models such as OpenAI’s o1-pro, which has proven especially proficient at performing high-quality random audits. >> there was a lot of manual effort involved in coming to that conclusion with my own effort to review the LLM's reasoning So, the randomly audited entries seemed reasonable to you – n…
> not even the data itself, just the reasoning about the generated data
Poor phrasing on my end -- yes, absolutely the end data as well as the reasoning, as the reasoning tends to include the final answer.
Maybe I should! Appreciate the feedback.
Re: Show HN: OpenNutrition – A free, public nutrition database
#79> Final nutritional data is generated by providing a reasoning model with a large corpus of grounding data. The LLM is tasked with creating complete nutritional values, explicitly explaining the rationale behind each value it generates. Outputs undergo rigorous validation steps, including cross-checking with advanced auditing models such as OpenAI’s o1-pro, which has proven especially proficient at performing high-qu…
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Re: Show HN: OpenNutrition – A free, public nutrition database
#80> Final nutritional data is generated by providing a reasoning model with a large corpus of grounding data. The LLM is tasked with creating complete nutritional values, explicitly explaining the rationale behind each value it generates. Outputs undergo rigorous validation steps, including cross-checking with advanced auditing models such as OpenAI’s o1-pro, which has proven especially proficient at performing high-qu…
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It doesn't matter how accurate the models are, it's not a "data set" (in the scientific sense), it's more of a conclusion set. Maybe the conclusions are spot on. Maybe not. I have no idea.