Yo

Youchoose.one – A site that helps you narrow down a list, one by one

Hacker News

Youchoose.one – A site that helps you narrow down a list, one by one

I built this website to help me narrow down the large list of appetizers our caterers could serve at our wedding down to just 4 favourites. I like the idea of being provided 2 options at a time, picking a favourite, and repeating until you have the desired number of winners. I was rather shocked to find no tool existed, so I made it myself. I tried to make it very simple to use, with a variety of input methods (enter one by one/paste in a list, click the button/press enter). Once the user clicks Go, the URL holds the state of the options submitted and any settings so they can be shared easily. The website is made with Nuxt/Vue - simply to stay in good practice using other frameworks (I'm an Angular developer). Any feedback/thoughts would be great! Especially SEO wise.

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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 · Missing: supports, reddit linkedin, podcasting
85%85% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos
71%71% 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
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
43%43% 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
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
11%11% 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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