Vagon Streams – No-code app streaming platform

Hacker News

Hey Hacker News! Today we're announcing our new application streaming platform Vagon Streams. Vagon Streams gives you superpowers to run any desktop app on the browser with no code. Make your users run your application through your website and make it device agnostic in minutes! We'd like to hear your thoughts about it. Use cases? [3D application streaming], [Instant desktop product trials], [Virtual experiences], [Real estate visualization], [Remote training and education], [Gaming and entertainment] Vagon Streams is fresh out of the box and we're looking forward to your feedback. Therefore, we're giving $25 credits for all HN community, available for our launch day. We're excited to hear your thoughts & feedback! Best, Vagon Team

Visit product View on Hacker NewsMar 23, 2022

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

77%77% 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, visual · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% 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, users · Missing: plus, intuitive, reviews

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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