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Our first Firebase-powered game

Hacker News

Our first Firebase-powered game

We've been playing around with Firebase and over the last few weeks we've been working on our first project that uses it exclusively for its backend. What we built is a Facebook connected game called Hatchlings Match. It's a spin-off game that reuses the art from our original game, Hatchlings, but has completely different gameplay. Building on Firebase was great; it was also the first project we've built on node. The combination of those two things increased our development cycle tremendously. So far the nicest thing about launching on Firebase has been that we haven't had to worry about scaling. We're adding over 800 users per hour right now (and Firebase is showing over 350 concurrent players currently online and connected) and that's all running off of a single (small) EC2 instance that's not doing much more than serving static files. One other thing we're particularly proud of is that it (knock on wood) works completely cross platform and cross browser (tested so far on Chrome, Safari, Firefox, IE, iOS, and Android). [A friend has informed me that touch events aren't working on his MS Surface device -- trying to get ahold of one of those to test with at the moment.] I'd be interested in any tips you all have for improvement (especially the onboarding process and the first level's tutorial). Or any ideas you'd have to help us make it more social. Here's the link: https://www.facebook.com/appcenter/hatchlingsmatch

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, single · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, users · Missing: mobile apps, personal, entrepreneurs
51%51% 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
48%48% 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: platform, exclusive, users · Missing: plus, intuitive, reviews
48%48% 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
16%16% 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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