Da

Daily Crypto Investment Game

Hacker News

Daily Crypto Investment Game

Update: I was asked to create this as a Show HN thread rather than Ask HN. It's really a bit of both. It's far from complete and I'm looking for ideas/feedback/contributors. As a fun side project I built a daily crypto investment game. It randomly selects five of the top 200 cryptos each day and users can compete by investing 100k (fake money) into the tokens of their choosing. The following day users can see if they made the leaderboard for both biggest gains and biggest losses as well as play again. Site: https://cryptable.club The site does not require email or registration to play. I'm running out of new feature ideas but I'm not ready to let this one go. If anyone has any ideas/feedback, please let me know. So you don't have to visit the site, current features include: - Dark/Light Mode - 7-day Spark graph of each currency - Summary section showing how much you made/lost per coin - Leaderboard showing biggest gainers/losers - Share to social Possible features I've considered: - 1v1 Challenge - Allow players to challenge friends for the day. Show the results in a separate 1v1 widget showing who won and how much profit/loss they both made. - Email daily recap. If the user chooses to register, we could email them their daily recap which would show how much they made/lost on each of the five coins. Tech stack: - DynamoDB - Lambdas running NodeJS to get price updates from the CoinGecko API every ~3 minutes, also for generating the leaderboard after the cutoff time - Vue/Vuetify for frontend

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Actual performance

3points
2comments
Did not reach leaderboard

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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, 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
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
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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