Docket Fleet – mobile device cloud

Hacker News

Hello Hacker News. Boris here from Docket (YC P25). Excited to alpha Docket Fleet with the broader community! It's a mobile device cloud (like AWS Device Farm or BrowserStack) built mainly for agentic use cases, and with (much needed) UX improvements for manual interaction (built my own WebRTC to device pipeline). I've spent pretty much all of last year working on autonomous QA agents for web and mobile testing. Earlier this year I realized a lot of devs started building their own agents (qa, rpa, scraping, etc) but drew the line at building the infra. I spent last week spinning out our mobile infra as a separate service and this is the result. We also support Windows executables, macOS apps, and HTT2 tunnels (for access to private and localhost networks). These aren't exposed in the alpha but support for them does exist. Try it out for free! If you have an iOS app, make sure it's built for the simulator (not the ipa file). If you need these at scale, let me know, we have a bunch of compute allocated already. Thanks!

Visit product View on Hacker NewsJun 24, 2026

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3points

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

96%96% 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.

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

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: started, para, ios · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

16%16% 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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