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Exit Reviews - Learn how products performed over their lifetime

Hacker News

Exit Reviews - Learn how products performed over their lifetime

Problem: - We never hear about broken and worn-out products and there is no data about the longevity and repairability of products. - At the same time, environmental impact and sustainability are becoming increasingly important for consumers. - We don’t know how a product performed over its duration of service because most reviews are written when it first arrived and people haven’t spent much time with it to learn the quirks. Solution: That's why I'm building exitreviews.com to change the way people review products. Let's reflect upon how a product performed over its duration of service instead of when it first arrived and people haven’t spent much time with it to learn the quirks. We can then build a collection of how long products last, where they break, and how to fix them. Even if certain products are not available anymore, it still gives a good picture of brand deterioration.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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: io · Missing: https docs, excited, just released
54%54% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% 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.

Incorrect prediction on native model

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