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Koldsnapp – Teaser launch Site for college comp

Hacker News

Koldsnapp – Teaser launch Site for college comp

Hey Guys, long time rss lurker here. My college, Colorado College, is putting on a $100k startup competition on April 1st, and here's the teaser launch site for our product. Basically, Koldsnapp is a wireless sensor system for small business freezers and fridges. Getting warned before the ice cream melts or your produce goes bad will prevent >$1000 losses that many of these small family-run businesses incur. It only takes 20 minutes to setup, and our first vertical is ice cream stores. I'll release the product slides on the day of the comp, and i'll post here again if there's interest, and especially if we win some money! Thanks for being such a great resource over the years, HN! -Azeem PS. Since the pitch is on April 1st, can anyone think of a good april fools prank to do that wouldn't destroy a pitch?

Visit product View on Hacker NewsMar 24, 2014

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

78%78% 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 · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, 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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, 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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