Are you looking to furnish a space for a specific budget? We can help!

Hacker News

Hi, I'm searching for honest feedback about my new service. I am trying to help people with furnish. Basically, I give an online shopping list with direct links to online sources for every product local to purchase furniture. I also negotiate with the suppliers for a lower price. Any feedback (web content, the business itself, etc. ) is acceptable and important! Thank you for your valuable opinion.

Visit product View on Hacker NewsOct 24, 2022

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% 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 HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

40%40% 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

24%24% 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

16%16% 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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