Show HN: OpenNutrition – A free, public nutrition database

> 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.

>> 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 values, explicitly explaining the rationale behind each value it generates. Outputs undergo rigorous validation steps,

Those rigorous validation steps were also created with LLMs, correct?

>> whose core innovations leveraged AI but didn’t explicitly market themselves as “AI products.”

Odd choice for an entirely AI based service. First thought I had after reading that was: must be because people don’t trust AI generated information. Seems disengenuous to minimize the AI aspect in marketing while this product only exists because of AI.

Great idea though, thanks for giving it a shot!

The search is broken on safari, every time it refreshes you lose the focus on the text input, which means you have to click on the search bar after every single character you type. The filters are broken, type "chocolate", chose the M&M's brand, none of the labels return a result despite showing (xxx)

> I wanted to investigate the possibility of using LLMs

ah, yeah, I guess it makes sense then...

LLM generated nutrition for accuracy.

The first item I manually look up is has about double calories listed in the "dataset" versus reality. Honey bunches of oats honey roasted.

Just at a quick glance...

How can a large egg (50 g) contain 147 g choline?

https://www.opennutrition.app/search/eggs-eeG7JQCQipwf

Additionally, on https://www.opennutrition.app/search/brown-lentils-VwKWF7CQq... it says:

> Unlike larger legumes, they require no pre-soaking and cook in 20-30 minutes, making them ideal for soups, stews, and salads

That is not necessarily true. Based on my experience, it does require pre-soaking, otherwise you will have to cook it for a long time, as opposed to red lentils (which is done under 15 minutes, no pre-soaking needed), although red lentils taste more like yellow peas.

In any case, I think this could be really useful, once accurate enough. One could even implement other features on top, such as a calorie tracker and so forth, but that is a huge project on its own.

I wish you luck!

As this seems US focused, I'll share an alternative that works really well with European products (and a lot of US ones too, apparently): https://yuka.io/en/

Really easy to use (just scan the barcode and you get easily digested data about the product) has every product imaginable, also analyzes cosmetics and best of all, all the basic functionality is free.

Not affiliated, been using it for years at this point and now it's an essential partner when going shopping. That they let people decide their own premium pricing per year is just icing on the cake.

I'm not going to discuss whether or not to use an LLM, I just want to thank you for opennutrition because it's very useful for checking the nutritional values of each food, especially for diabetics.

Real and open nutritional datasources exist: https://support.cronometer.com/hc/en-us/articles/36001823947...

This is neat. I've spent a lot of time thinking about implementing something similar for my company Eat This Much, but end up pushing it off in favor of focusing on our core meal planning features.

When something doesn't have a reference listed, and just says "sourced from a publicly available first-party datasource", what does that mean? Crawled from other sources and you'd prefer not to say? The wording does feel a little sketchy when contrasted with entries that do list sources.

When something does list references that don't seem super close to the actual food, what is the process like there for interpreting those values? Example, this Chicken Salad inheriting from Chicken Spread: https://www.opennutrition.app/search/chicken-salad-37mAX17YX...

The quality of the data might feel rough now, but I can see this being valuable for our users even if it's just an opt-in "show estimated micronutrients" or something. Would require labeling values as not being directly from a source of truth.

One thing that a lot of people are missing is that there is already a lot of inaccurate nutrition data out there. Even on information directly from the manufacturer, sometimes there are errors, or just old versions of the product that never get scrubbed from the internet (I imagine the latter case would be tricky for an LLM to deal with too). Just logging your dietary intake in any form will get you 80% of the benefit of tracking via some self awareness of your intake. Of course, it's an easy argument to point out that if you had the choice between verified data and fuzzy LLM data, you should go for the human verified data (for now).

There’s an important caveat to keep in mind when it comes to food databases, especially those relying on branded or restaurant items:

U.S. law does not require food manufacturers to disclose everything that goes into their products. Under the Code of Federal Regulations (21 CFR § 101.100), there are exemptions to ingredient labeling... An example: flavorings, spices, and incidental additives (like processing aids or anti-caking agents) are not always listed explicitly. Also: proprietary blends and "natural flavors" can legally conceal dozens of chemicals (some synthetic), which consumers have no way of identifying.

Micronutrient data is often estimated or missing from labels and restaurant menus, which limits the accuracy of even the best-intentioned databases. Studies show that the nutritional information provided by restaurants and brands is frequently incomplete or inaccurate, especially when it comes to sodium, sugar, and actual serving sizes. (Urban et al. "The Energy Content of Restaurant Foods Without Stated Calorie Information" ; Labuza et al., 2008 and others)

IMO Food databases are only as accurate as the source data allows. Until food labeling laws mandate full disclosure and third-party verification, apps like this can support health awareness. Still, they shouldn't be treated as precise medical or dietary guidance—particularly for people with allergies, sensitivities, or chronic health conditions that require strict tracking.

What's the main difference between this and OpenFoodFacts really ?

Overall, very cool and seriously much needed! How does the micronutrient estimation work? Or is that part of the secret sauce?

I was looking at this page: https://www.opennutrition.app/search/original-shells-cheese-... and saw the amino acid, vitamin, and mineral sections; there are many things listed which aren't covered by the official nutritional data. These entries also have very precise numbers but I'm not sure where and how they're derived and if I could put any serious weight in them. I'd love to hear more if you're willing to share!