Vlly – Turn all your users into playtesters [VR]

Hacker News

Hello Hacker News, I'm Tremayne and I built Vlly - a tool that allows VR and Spatial Computing team to automatically generate video clips of key user moments so they can understand why users are leaving, how they're using features, where bugs exist, etc. You just install the unity plugin, add a line of code where you want to track, and watch clips of your real users in a dashboard. It's super easy to use and install because I wanted to make it easy for indie devs to use while focusing on building their product. It's like Hotjar/Fullstory/Sprig/etc for VR and Spatial Computing. I was working with some VR teams last year and the best advice anyone gave me for how to do user testing was to either be directly next to my user or hire a playtesting service to do these manual tests for me. I thought it was odd that there wasn't something like Hotjar/Fullstory/Sprig available for VR and Spatial that would allow me to just watch clips of when someone was using a feature. I took the idea on the road (shout out to AWE2023) and it turned out this wasn't just an issue I had so I built it. Happy to answer any questions or speculate on what could be. If you would like to try Vlly - it takes less 5 minutes to setup: https://www.usevlly.com Or get started directly by installing the unity plugin https://github.com/tremayne-stewart/vlly-unity

Visit product View on Hacker NewsAug 25, 2023

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Did not reach leaderboard

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

90%90% 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, using · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

59%59% 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: video, users · Missing: mobile apps, ios, personal

47%47% 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

15%15% 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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