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Tuuree- Airbnb for Luggage Sharing

Hacker News

Tuuree- Airbnb for Luggage Sharing

Monetizing unused luggage space is now the new trend in sharing economy and it is no longer millennial preference, but a part of modern society. I am the co-founder of Tuuree : https://www.tuuree.com We connect Global shoppers to International travelers. Using our platform - shoppers can order global/cross-border or instore products and can save a lot on the shipping costs. These products are delivered to them by international travelers en-route, who are willing to share their luggage space for rewards (on an average 30% of a traveler’s luggage space goes unused) - we have custom developed algorithm that automatically calculates the sales tax of shopper’s product based on the product location. We do empower our shoppers to make changes if there is a difference in sales tax depending on their location/country. (40% of shoppers pay excess sales tax because they are unaware). - Risk Index is a proprietary scoring mechanism developed (in house) to assess the risk factor involved in purchasing and delivering merchandise. Basing on the index score we categorize shopping requests as Green, Yellow and Red. Green being the least risky while Red being the riskiest. In our phase 2 we are developing: - ML powered customs declaration engine that generates prefilled customs & excise declaration forms (for merchandise accepted on Tuuree) based on the order value, volume and destination. There is a huge misconception about crowd -shipping firms, they are considered as way to evade customs and excise duties by authorities. -a tool that lets travelers assess their empty luggage space in 3D and matches with the accurate merchandise Tuuree is a P2P platform that helps you to save on shipping costs and earn every time you travel. So, next time you travel or order cross-border products, Tuuree is the one stop solution.

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Actual performance

3points
2comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
60%60% 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: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
31%31% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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