A deterministic update to my tough-as-nails 2D unity mobile game

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Hello HN! I don't often see a lot of game dev content on hacker news, so I thought it would be fun to add some! I just spent the last 5 months updating my mobile game Command Center Earth to version 2.0.0. The trailer on the website posted is a culmination of all that work! The funny part is that while the "Fully Deterministic" refactor made to the game only gets a small callout in the trailer, it's what took the majority of the time! I could have never possibly imagined how much time, energy and testing it would take to get the game reproducibly deterministic with Unity! And even with all that work, I can't 100% guarantee that it is deterministic across all devices (although my testing shows things hold together on all devices I've tested). All things considered though, I'm very happy with how things came together! In addition to the deterministic refactor, I added 3 new levels to the game, 2 of which were only possible as a result of the refactor! Without it, all the bumping of those meteors (on Haxer & Frank) would have produced a different level scenario on each retry! Additionally, having things deterministic means that my leaderboard is now accurate to 0.01 seconds! There were some big visual overhauls as well, but I thought the HN community might be the most intrigued by the determinism updates! Thanks for checking out my game! P.S. I plan on writing a blog post on all that I've learned, in the hopes that the next developer to come along has a better go of it than I did btw.

Visit product View on Hacker NewsApr 19, 2023

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% 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: new, visual · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

18%18% 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.

Incorrect prediction on native model

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