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Gutsafe AI
Find the gut health score the nutrition facts are hiding.
Until now, there was no clear way to understand how a food’s ingredients impact the human gut microbiome. Nutrition labels would never tell you this. Now, you just need to scan a barcode, and GutSafe AI will analyze each ingredient’s known bacterial effects and generates a 0–100 Gut Health Score, enabling objective comparisons between products. The model reaches an R² of 0.99952 and an MAE of 0.000513, giving you precise, ingredient‑level insight into how a food supports or harms your gut.
Today, I’m excited to launch GutSafe AI, a computational model that finally makes gut health measurable. Until now, there was no clear way to understand how a food’s ingredients impact the human gut microbiome. Nutrition labels don’t warn you, and ingredient lists don’t tell you which additives feed harmful bacteria. GutSafe AI analyzes each ingredient’s known bacterial effects and generates a 0–100 Gut Health Score, enabling objective comparisons between products. The model reaches an R² of 0.99952 and an MAE of 0.000513, giving you precise, ingredient‑level insight into how a food supports or harms your gut. I’ve been building this for a while, and I’m excited to finally share it. I would love your feedback and ideas as GutSafe grows.
Curious how the model handles ingredients with limited published research on gut effects, does it flag those or just average them in?
How do you actually train a model like this when most ingredient-microbiome data is still pretty thin and inconsistent across studies?
Finally someone made gut health as scannable as calories. Tried it on a few products from my pantry and the scores were way different from what I assumed.
Scanned a few things in my pantry and the scores actually lined up with what I've read about fermented versus ultra processed foods, which was reassuring.
how does the score hold up for fermented or traditionally processed foods where the ingredient list doesn't really reflect what the microbiome actually ends up seeing?
How does the model actually calculate the score when an ingredient has mixed or poorly studied effects on different bacterial species, and does that uncertainty show up anywhere in the final 0–100 rating?
How does the model handle ingredients with limited or no published research on gut microbiome effects, and does that affect the confidence or accuracy of the score for those items?
How does the model handle ingredients where the gut microbiome impact isn't well-studied yet — does it flag those as low confidence, or just extrapolate from similar compounds?
the barcode-scan-to-score flow is such a clean way to bridge messy nutrition labels and actual microbiome impact, feels like the kind of tiny UX choice that makes the whole science usable in a grocery aisle.
How does the model actually handle ingredients with limited or conflicting research on their bacterial effects, since gut microbiome science still feels pretty early?
About Gutsafe AI on Product Hunt
“Find the gut health score the nutrition facts are hiding.”
Gutsafe AI was submitted on Product Hunt and earned 7 upvotes and 13 comments, placing #159 on the daily leaderboard. Until now, there was no clear way to understand how a food’s ingredients impact the human gut microbiome. Nutrition labels would never tell you this. Now, you just need to scan a barcode, and GutSafe AI will analyze each ingredient’s known bacterial effects and generates a 0–100 Gut Health Score, enabling objective comparisons between products. The model reaches an R² of 0.99952 and an MAE of 0.000513, giving you precise, ingredient‑level insight into how a food supports or harms your gut.
Gutsafe AI was featured in Health & Fitness (82.9k followers), Biohacking (15k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 136.1k products, making this a competitive space to launch in.
Who hunted Gutsafe AI?
Gutsafe AI was hunted by Shashwath. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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