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Fashionguessr – A Daily Fashion Trivia Game

Hacker News

Fashionguessr – A Daily Fashion Trivia Game

Hello! I created Fashionguessr, a daily fashion trivia game. Inspired by other daily trivia games like Timeguessr, Connections, and Wordle, Fashionguessr is my attempt at the same thing for people curious about fashion. The game is broken up into three parts: Runway Frames - Users have to guess and identify a fashion show, its brand, its season, and its designer based on a single random frame from the show. Users can use a variety of hints, each more expensive than the last, to reveal more information about the show to help them guess correctly. Trivia - Multiple-choice and short-answer questions to test their knowledge about fashion, the lore, the people, the processes, the history, and everything in between. Sketch - Users get a few moments to look at a fashion item and then are tasked with drawing it from memory. All of these contribute to a score you can see at the end on the last page, which sums everything up. Each section includes explanations, summaries, and sources, so users can get as much insight as possible for every question. There are new questions every 24 hours, and users can go back and play games from days past. I built it to help people learn more about fashion in a fun, low-stakes environment. Learning about fashion can be overwhelming, and I think Fashionguessr can present and package fashion education in a way that's more palatable. Would love feedback on the presentation, UI, accessibility, ease of use, and anything else that comes to mind.

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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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
89%89% 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: users, way, education · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, single · Missing: mac, agents, macos
51%51% 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: ide, io · Missing: https docs, excited, just released
38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% 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
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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Discover fashion inspirations daily

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