Show HN: OpenNutrition – A free, public nutrition database

Overall reception & UI

• Many commenters like the interface: fast, “slick,” reactive, good dark mode, and clear tables.
• AI-generated food icons are praised for quick visual recognition, though some want click-to-zoom to better match ingredients (e.g., specific lentil types).

Search, performance & bugs

• Multiple reports of issues: Safari losing input focus on each keystroke, first character being dropped, filters not returning results, errors on certain queries, client-side exceptions, CORS errors, and 521/“hug of death” outages.
• Author confirms several fixes and scaling issues; web search is positioned as more of a demo vs. the primary in-app experience.

Data sources, coverage & units

• Database includes alternate and non-English names, which users find helpful.
• Users ask for regional variants (e.g., UK vs US Coke) and better handling of generic queries (“can of coke,” “avocado”).
• Debate over including raw vs cooked items (e.g., bacon); some see raw data as essential for logging before cooking, others question relevance.
• Per-100g view exists in the web table; some want it surfaced more prominently, especially for European use.

LLM-generated micronutrients & accuracy debate

• Micronutrients, vitamins, amino acids and some descriptions are inferred using LLMs with grounding data and audit passes (including o1-pro).
• Strong criticism: several commenters say this is “not real data,” call it anti-scientific, and worry about serious users or medical dieters relying on unvalidated estimates.
• Supportive voices argue many existing app databases are already noisy; approximate, well-described estimates can be more useful than missing values.
• Specific issues were found (e.g., choline unit error in eggs, mismatch between website and downloadable line for a cereal), seen as evidence that more validation or clearer labeling is needed.
• There are calls for stronger, possibly license-enforced downstream disclaimers that the data is LLM-derived.

Comparison to other nutrition databases

• OpenFoodFacts (OFF) is repeatedly mentioned as a more “factual” alternative with an API, but limited mainly to packaged foods and what labels report.
• OFF cofounder notes ongoing work to add generic foods and approximate micronutrients from reputable databases and invites collaboration.
• Others reference NCC, Cronometer, Swiss and Japanese government datasets, and nutritionvalue.org as benchmarks or validation sources.
• Some suggest using LLMs only to map natural-language queries onto authoritative databases rather than generating new values.

APIs, licensing & openness

• Dataset is downloadable; some still want an API for freshness and possible monetization.
• License requires prominent attribution (similar to OpenFoodFacts/OpenStreetMap). Some find this restrictive for independent apps and question use of “Open” branding for a commercial product.
• Author argues attribution is necessary to justify the effort and that without it the alternative would likely be no open dataset at all.

Feature ideas & use cases

• Requests: portion scaling, recipe/URL import, better barcode and international support, translations (e.g., Hungarian names), hover tooltips explaining nutrients, and clearer ingredient interactions.
• Some users report the companion iOS app’s macro tracking, goal-setting, and graphs are unusually good and less naggy than incumbents, and would like an option to rely solely on OFF or other “verified” sources with AI as opt-in.
• Several commenters share that even imperfect tracking significantly improves diet awareness and habits, while others insist nutrition tools carry a higher ethical bar for accuracy.