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Game to see if you're over-/under-confident in your own knowledge

Hacker News

Game to see if you're over-/under-confident in your own knowledge

I wrote a simple "trivia" game to get a sense about how well calibrated you are in terms of how well you know things: https://est.alejo.ch You get ten simple "what happened first" questions and give a confidence level (for each answer). The game tracks your success rates per confidence level and gives you a calibration graph/table (e.g., "you consistently over-estimate your success rate"). You can play multiple times to see aggregated statistics. After 60 games (480 questions), I found out that I am relatively well-calibrated, though I should ~never bet under 60% (for this specific type of binary questions): https://alejo.ch/3mk The game is open source (GPLv3 at https://github.com/alefore/estimate ) and it's just static files. There's no server-side logic: the game log/stats are stored in your browser (local storage API). It's pure TypeScript, no framework. You can read more about it on https://alejo.ch/3m9 Just figured I'd share in case others find it useful and/or have suggestions to improve it. I'd also be curious to see the calibration stats of other people.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% 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 · Missing: mobile apps, ios, personal
53%53% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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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