I

I Made a Christmas Quiz

Hacker News

I Made a Christmas Quiz

For the last couple of years I've created a Christmas quiz, which you can read about and play here: https://www.lloydatkinson.net/projects/2022/christmas-projec... . This year I created another quiz, this time general Christmas questions. Like last year the tech I used: - Astro - React - TypeScript - Framer Motion for the animation - A Netlify function that saves and loads the scoreboard from Azure Table Storage Unlike last year which was somewhat easy to cheat on by Googling, this time, the longer you take to answer a question the fewer points you get. I hope you have fun and a great Christmas

Visit product View on Hacker NewsDec 25, 2023

Actual performance

5points

Did not reach leaderboard

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

86%86% 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 · Missing: mac, agents, macos

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

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

36%36% 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

34%34% 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

28%28% 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

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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