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After 4 hours learning React, I made a joke app based on ur fav topics

Hacker News

After 4 hours learning React, I made a joke app based on ur fav topics

Hey HN, I built this app for 2 reasons: first, to apply my new React knowledge after watching a 4 hour React tutorial and second, to spread some laughter. We all need a little humor in our lives, and I wanted to create a space where people can enjoy jokes based on their favorite topics, like productivity, programming, relationships, and more. Please check it out, let me know any feedback you've got and i hope you'll enjoy the jokes. Mandy

Visit product View on Hacker NewsSep 28, 2024

Actual performance

2points

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

87%87% 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: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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

35%35% 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

18%18% 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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