This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Nourai is an iOS food logger where AI drafts meals and you review foods, portions, and sources before saving. High-trust nutrition comes from databases, label OCR, or values you enter; visual-only estimates stay labeled. On compatible iPhones, depth adds an extra portion signal—not exact weight.
Hey Product Hunt — I'm Huibin, the founder and maker of Nourai.
I built Nourai after noticing that photo food loggers often compress three uncertain questions into one confident number: what food is this, how much is there, and where did the nutrition data come from?
Nourai is for people who want the speed of photo logging without giving up control over what gets saved. AI drafts the foods it sees; you review the foods, portions, and sources before saving. High-trust nutrition comes from food databases, nutrition-label OCR, or values you enter. If only a visual estimate is available, it stays labeled as an estimate. On compatible iPhones, LiDAR/depth can add another signal for volume and portion estimation, but it is not an exact-weight promise. Raw depth and AR frames are not uploaded or saved.
The release also keeps logging coverage visible in Insight and Weekly Review, so a missed day is not silently treated as zero.
Current limits: portions can still be wrong, database coverage varies by food and region, depth only helps on supported iPhones, and Nourai is not medical advice. The gallery uses synthetic demo data.
I'd love candid feedback: which trust signal matters most to you — portion evidence, a visible nutrition source, or review before save?
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About Nourai on Product Hunt
“Food logging with visible sources and a final say”
Nourai was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #118 on the daily leaderboard. Nourai is an iOS food logger where AI drafts meals and you review foods, portions, and sources before saving. High-trust nutrition comes from databases, label OCR, or values you enter; visual-only estimates stay labeled. On compatible iPhones, depth adds an extra portion signal—not exact weight.
Nourai was featured in iOS (110.8k followers), Artificial Intelligence (478.1k followers) and Nutrition (994 followers) on Product Hunt. Together, these topics include over 159.5k products, making this a competitive space to launch in.
Who hunted Nourai?
Nourai was hunted by Huibin Wu. 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.
Want to see how Nourai stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Huibin Wu
Hey Product Hunt — I'm Huibin, the founder and maker of Nourai.
I built Nourai after noticing that photo food loggers often compress three uncertain questions into one confident number: what food is this, how much is there, and where did the nutrition data come from?
Nourai is for people who want the speed of photo logging without giving up control over what gets saved. AI drafts the foods it sees; you review the foods, portions, and sources before saving. High-trust nutrition comes from food databases, nutrition-label OCR, or values you enter. If only a visual estimate is available, it stays labeled as an estimate. On compatible iPhones, LiDAR/depth can add another signal for volume and portion estimation, but it is not an exact-weight promise. Raw depth and AR frames are not uploaded or saved.
The release also keeps logging coverage visible in Insight and Weekly Review, so a missed day is not silently treated as zero.
Current limits: portions can still be wrong, database coverage varies by food and region, depth only helps on supported iPhones, and Nourai is not medical advice. The gallery uses synthetic demo data.
I'd love candid feedback: which trust signal matters most to you — portion evidence, a visible nutrition source, or review before save?