Vibe-coded Steam, but in the browser

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Hi HN! Lifelong avid gamer here, hugely passionate about WASM and WebGPU. I firmly believe that these technologies will enable console and PC quality titles to be accessible through a browser, and with this, we'll need a new discoverability layer. Looking online, platforms like CrazyGames and Poki cater to a casual/hypercasual demographic, and I couldn't find anything out there that was for me, a core gamer that typically uses Steam and consoles. So I vibe coded my own! It features WASM ports of classic games, as well as some indie Unity titles. The goal is to host mainly WebGPU titles moving forward, and to serve as a way for smaller developers to get discovered outside of crowded channels like Steam. Here's a few features from the platform I wanted to highlight: • Controller support • A console-like UI/UX • Community forums (much work to do here) • Basic achievements • Store pages, modeled after Steam • Social features • Asset chunking to enable faster load times I'd love to get feedback on the portal, to make it even better. Thanks!

Visit product View on Hacker NewsMay 23, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, code · Missing: mac, agents, macos

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

best fitHighest predicted score across all platforms for this description.

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

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

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

62%62% predicted probability of success on AppSumo, 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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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

14%14% 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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