I

I built a site to track your buy it for life items

Hacker News

I built a site to track your buy it for life items

Hey HN, I've been on a mission to buy higher quality, sustainable products, especially when replacing things I use frequently. To better understand the value of these items, I built a site to track how often I use them. Here is my list so far: https://www.costperuse.com/@nahtnam Feel free to sign up and share the items that have served you well! https://www.costperuse.com/ You can also generate a sharable link for X (Twitter), complete with an open-graph image of your stats. Example: https://x.com/nahtnam/status/1797875287793004947 If there's interest, I have plenty of ideas to improve the site. With enough data, I aim to create lists of the best BIFL (Buy It For Life) items in various categories. Check it out and let me know what you think!

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Actual performance

3points
12comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, 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 · 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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