web-based emulator system targeted at iOS

Hacker News

https://pocketga.me/ Hi guys, I've been working on an multi-system emulation environment to run NES, SNES, GB, GBA, Master System, and Game Gear games in the browser for us poor iOS users who have no access to native emulators through the app store and don't want to jailbreak our devices. I posted this as a comment on a JSMESS post a few weeks ago and got some good feedback and am certainly open to hearing any opinions or suggestions you might have. Since my last post, I've cleaned up the site and got it 100% working as a mobile web app to avoid Safari's UI. Performance is very good on newer iPhones and I've even devised a system for state saving with the NES and GameBoy emulators, and the touch controls are about as passable as they are going to get. I've put together a demo account with some simple home-brew games for people to try out, but to upload your own games, you'll need to register yourself an account. Thanks!

Visit product View on Hacker NewsNov 14, 2014

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

78%78% 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, new, open · Missing: mac, agents, macos

66%66% 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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

50%50% 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

22%22% 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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