←back to thread

311 points joshdickson | 3 comments | | HN request time: 0.001s | source

Hi HN!

Today I’m excited to launch OpenNutrition: a free, ODbL-licenced nutrition database of everyday generic, branded, and restaurant foods, a search engine that can browse the web to import new foods, and a companion app that bundles the database and search as a free macro tracking app.

Consistently logging the foods you eat has been shown to support long-term health outcomes (1)(2), but doing so easily depends on having a large, accurate, and up-to-date nutrition database. Free, public databases are often out-of-date, hard to navigate, and missing critical coverage (like branded restaurant foods). User-generated databases can be unreliable or closed-source. Commercial databases come with ongoing, often per-seat licensing costs, and usage restrictions that limit innovation.

As an amateur powerlifter and long-term weight loss maintainer, helping others pursue their health goals is something I care about deeply. After exiting my previous startup last year, I wanted to investigate the possibility of using LLMs to create the database and infrastructure required to make a great food logging app that was cost engineered for free and accessible distribution, as I believe that the availability of these tools is a public good. That led to creating the dataset I’m releasing today; nutritional data is public record, and its organization and dissemination should be, too.

What’s in the database?

- 5,287 common everyday foods, 3,836 prepared and generic restaurant foods, and 4,182 distinct menu items from ~50 popular US restaurant chains; foods have standardized naming, consistent numeric serving sizes, estimated micronutrient profiles, descriptions, and citations/groundings to USDA, AUSNUT, FRIDA, CNF, etc, when possible.

- 313,442 of the most popular US branded grocery products with standardized naming, parsed serving sizes, and additive/allergen data, grounded in branded USDA data; the most popular 1% have estimated micronutrient data, with the goal of full coverage.

Even the largest commercial databases can be frustrating to work with when searching for foods or customizations without existing coverage. To solve this, I created a real-time version of the same approach used to build the core database that can browse the web to learn about new foods or food customizations if needed (e.g., a highly customized Starbucks order). There is a limited demo on the web, and in-app you can log foods with text search, via barcode scan, or by image, all of which can search the web to import foods for you if needed. Foods discovered via these searches are fed back into the database, and I plan to publish updated versions as coverage expands.

- Search & Explore: https://www.opennutrition.app/search

- Methodology/About: https://www.opennutrition.app/about

- Get the iOS App: https://apps.apple.com/us/app/opennutrition-macro-tracker/id...

- Download the dataset: https://www.opennutrition.app/download

OpenNutrition’s iOS app offers free essential logging and a limited number of agentic searches, plus expenditure tracking and ongoing diet recommendations like best-in-class paid apps. A paid tier ($49/year) unlocks additional searches and features (data backup, prioritized micronutrient coverage for logged foods), and helps fund further development and broader library coverage.

I’d love to hear your feedback, questions, and suggestions—whether it’s about the database itself, a really great/bad search result, or the app.

1. Burke et al., 2011, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268700/

2. Patel et al., 2019, https://mhealth.jmir.org/2019/2/e12209/

Show context
probotect0r ◴[] No.43569522[source]
I have been looking for something like this! I really like the interface. I wish I could click on the picture to enlarge it, so I can confirm that what I am looking for is what I am looking at. For example, we use 3-4 types of lentils and I am not sure if "brown lentil" in the database is the same brown lentil I have at home. I also really liked that I was able to search for "masoor" and the results showed red lentils; often I don't know the English name for something so it's hard to search.

Also, there is an error on this page for me: https://www.opennutrition.app/search?search=Goya

replies(2): >>43569629 #>>43569648 #
1. lm28469 ◴[] No.43569629[source]
> I wish I could click on the picture to enlarge it, so I can confirm that what I am looking for is what I am looking at.

You want to enlarge an ai generated image to know if it matches what you have at home ?

replies(1): >>43569683 #
2. probotect0r ◴[] No.43569683[source]
I guess I missed where they mentioned their images are AI generated. I assumed they were being pulled from some database.
replies(1): >>43569847 #
3. hombre_fatal ◴[] No.43569847[source]
You can derive it from the fact that there's no feasible way to get such uniform (and cute) image data for every food item.

Though I want to add that this is a good application of AI image gen since the images are useful for quick visual confirmation that the item is in the same ballpark of the thing that you think it is.