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Have fun betting virtual (not real) money on predictions from HN users

Hacker News

Have fun betting virtual (not real) money on predictions from HN users

I wanted to see how difficult it would be to build a web app using a sub-$300 android smartphone. Decided to build a fun predictions website where you could bet virtual (not real) money on predictions made by others, and also make predictions of your own. Building it turned out to be considerably easier and more fun than I anticipated. Primary tools used were: # QuickEdit as the mobile code editor (Note: the free version of the QuickEdit app is riddled with ads, it shows an advert each time you close a tab, but it unfortunately had the best UI of the 3 or so Android code editors I tested. Ended up using NetGuard to block it from retrieving & displaying ads), # PHP for the Backend ( custom PHP microframework I've used and built on over the past few years ). # jQuery for the frontend js ( cringing ) - it appears I'm simply too lazy to learn React/Vue/et al. Every once in a while, I pick one of them to learn, but I always end up returning to jQuery - or time-permitting - amateur level vanilla JS. # Bootstrap for the CSS - Battle-tested. For a purely backend dev with minimal design skills, good ol' Bootstrap (and in a growing number of cases, Tailwind) is always a life saver. # Whole thing is hosted on 2 VMs (1 hosting the web app, and 1 hosting Redis & MySQL). # As to the site itself, it turned out to be pretty cool to play around with. Go there, view the predictions, bet on the predictions you believe will come true, or against the ones you think will not. You get $50,000 to bet with (not real money). No signup is required to bet, but a quick signup is required to make a prediction. Hope you guys like it, and please be ruthless in telling me of any bugs you've found. So go on here => kudotap.com And Have Fun!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
66%66% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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