Bug or Feature?

Hacker News

Bug or Feature?

Dear HN, This is my first time posting here, and I hope I'm doing this right. The idea came to me by playing a game with my SO: 'Bug or Feature?' It can be harmless bashing: 'The French: Bug or Feature?', and it can be dark humour: 'Down's syndrome: Bug or Feature?' Anyway, for now I've only created this account, and I'm feeding it regularly through Buffer: https://twitter.com/bug_or_feature_ I'm thinking of maybe trying to make a bot, even though I have to admit that it's quite fun to come up with those. Please critique my - small - work, any help and constructive criticism would be greatly appreciated!

Visit product View on Hacker NewsDec 1, 2017

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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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

77%77% 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · 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

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.

Correct prediction on native model

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