Ic

Icebreakers – A Fresh Collection of Conversation Starters

Hacker News

Icebreakers – A Fresh Collection of Conversation Starters

This was a weekend project I wrote when my team's regular ice-breaking question website began to recycle questions. Use it on your Monday Daily to shake off the weekend. Or use it every day to build trust in your remote-first team. Or hit it on mobile to start a conversation with a stranger. It's open-source, never-track, open to submissions, and with over 2000 questions, seeing the same question twice will be a rare event. More info here: https://github.com/rendall/icebreakers#readme

Visit product View on Hacker NewsMar 26, 2023

Actual performance

7points

1comments

Made the leaderboard

Launch Intel predictions

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

61%61% 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.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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