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I want to change how people buy health supplements

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I want to change how people buy health supplements

I made a table where you can find out the source/location of factory for where health supplements are made. Then, I spent a year reading product labels so you can save time and money when buying supplements. This is that update. This is still a work in progress but it functions fine. My previous post was a simple database of company data showing ingredient sourcing/location. That took 10 days, this has taken me close to 9 months. BackOfLabel is an extension of that initial interest with dosage information at the product & ingredient level. This update allows sorting by many more attributes at the product level (for 4000+ products at the moment) of manually scraped data. Now, for instance you can sort by specific types of ingredient - eg. filter by magnesium glycinate , magnesium orotate or any combination. eg. find ubiquinol or ubiquinone, two forms of coenzyme q10. This is useful for consumers but also companies seeking competitor analysis. You are able to filter products by – Ingredient – Filter by liquid, tablet, capsule, powder & more – Browse by UPC Code – Dosage Information – No. Individual Serving – No. Manufacturer Serving – Total Dosage For example You can also search by type of protein powder - eg. search for whey protein powder and find the dosage information for many products instantly. It frustrates me and I think the way that people buy supplements is wrong. And they don't know any better because there are incentive structures that keep them in the dark. This is a small effort to combat the misleading labeling and lack of regulation in the industry. full disclosure - i've provided a generic affiliate link in the table that means i earn a small percentage (5%) of total cart if you purchase through the link note: browse on desktop to filter & sort

Visit productView on Hacker NewsMar 19, 2023
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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
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Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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