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Lost $500 because I couldn't find a receipt – built SlipCrate

Hacker News

Lost $500 because I couldn't find a receipt – built SlipCrate

Hi HN, Last year, my washing machine broke. It was still under warranty, but I couldn't find the receipt anywhere. Ended up paying ~$500 for a repair that should've been free. That stung — so I built SlipCrate to avoid it happening again. Here's what it does: - You upload receipts (photo or PDF). - It extracts purchase + warranty details automatically. - Everything is stored in one place. - It reminds you before warranties expire. - Works as a web app (installable on phone as a PWA). I've kept it simple — not another "all-in-one finance app," just a focused tool to stop losing money because of misplaced receipts or forgotten warranty dates. Try it here: https://slipcrate.com (It's free to try, early testers get a discount code for premium.) Thanks for reading — and if this saves even one person from paying out of pocket like I did, it'll be worth it.

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Actual performance

3points
7comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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 NewsMay not resonate with HN audience · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, code · Missing: agents, macos, agent
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% 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
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
20%20% 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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