Stash Belts, for hiding USB keys and SD cards

Hacker News

Stashbelts is a physical product startup that aims to make the best moneybelt you can buy. The product is a regular looking belt with a slot for a USB key, and a zippered compartment in the back that can hold SD cards, documents or cash. I use one as my main belt, no complaints. A friend of mine started this company after living in Kenya and finding cheap leather and good craftsmanship. I haven't told my friend yet that I'm submitting this, but I'm sure he'd appreciate feedback on the site and how to market the belts. http://www.stashbelt.ca/

Visit product View on Hacker NewsNov 11, 2012

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Actual performance

13points

12comments

Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: physical, plain · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, 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 · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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