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Laboratory.love – Crowdfund plastic chemical testing for food and drink

Hacker News

Laboratory.love – Crowdfund plastic chemical testing for food and drink

https://laboratory.love Last year PlasticList discovered that 86% of food products they tested contain plastic chemicals—including 100% of baby food tested. The EU just lowered their "safe" BPA limit by 20,000x. Meanwhile, the FDA allows levels 100x higher than what Europe considers safe. This seemed like a solvable problem. Laboratory.love lets you crowdfund independent testing of specific products you actually buy. Think Consumer Reports meets Kickstarter, but focused on detecting endocrine disruptors in your yogurt, your kid's snacks, whatever you're curious about. Here's how it works: Find a product (or suggest one), contribute to its testing fund, get detailed lab results when testing completes. If a product doesn't reach its funding goal within 365 days, automatic refund. All results are published openly. We're using the same methodology as PlasticList.org, which found plastic chemicals in everything from prenatal vitamins to ice cream. But instead of researchers choosing what to test, you do. The bigger picture: Companies respond to market pressure. Transparency creates that pressure. When consumers have data, supply chains get cleaner. Technical details: We work with ISO 17025-accredited labs, test three samples from different production lots, detect chemicals down to parts per billion. The testing protocol is public. You can browse products, add your own, or just follow specific items you're curious about: https://laboratory.love We think radical transparency about chemical contamination is the fastest path to cleaner products. Turns out a lot of people agree.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, open · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
49%49% 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 · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% 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
19%19% 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.

Correct prediction on native model

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