StartupRoasters – Brutally honest feedback to help you refine your idea

Hacker News

Hey HN, Today I'm launching StartupRoasters.com a community of people that give Brutally Honest Feedback to anything from a business idea, a Youtube video, a website... In today's internet, I feel like getting feedback to one's projects is tough because most online communities slaughter anyone who posts their own work stating that it's self-promotion and spam. That's why I built StartupRoasters.com where anyone can post their work and get feedback. You can even add a survey to your post so that people answer specific questions and make votes. The obvious issue is that everyone is just going to post their work and not care about the others. The solution I found was to implement a points system. You need 100 points to create a new post about your project and to get points, you need to review projects from others, get upvotes, comment... Thereby, everyone needs to contribute to get feedback on their own work. I hope this community will grow and be helpful to people in improving their work. Cheers, Yassine (@ybouane)

Visit product View on Hacker NewsSep 24, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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, io · Missing: https docs, excited, just released

35%35% 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

17%17% 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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