Browse HN Together in Three.js

Hacker News

Hey HN, We’re Philip, Amby, and Declan, and we made “multiplayer virtual computers” that you can embed anywhere, including 3D spaces. We decided to build this because we noticed that embedding third-party apps and websites can be a nightmare due to incompatible platforms, security issues, and poor UX. Adding multiplayer functionality to these embeds makes this problem exponentially more difficult. On the backend, we’re spinning up a VM and running a resource-optimized fork of Chromium which we then stream to participants via WebRTC. Since we’re hosting the servers running the applications, multiple users can connect and control the virtual computer seamlessly, and their client just needs to handle a video stream. If you want to add multiplayer virtual computers to your own app, you can sign up on https://hyperbeam.com/?ch=hn&cm=hn1 , grab a free API key, and throw the provided embed URL in an iframe in your app. You can also play around more with the Three.js demo in our interactive sandbox: https://app.sideguide.dev/hyperbeam/threejs/ If you have any questions or feedback, feel free to comment or shoot me an email at declan@hyperbeam.com. Thanks! Docs: https://docs.hyperbeam.com Discord: https://discord.gg/D78RsGfQjq

Visit product View on Hacker NewsAug 25, 2022

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268points

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

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, computer · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, video, users · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

17%17% 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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