Cyberdesk, API for computer agents to control a desktop (open source)

Hacker News

Hey HN, We built Cyberdesk – an open source API for building computer agents that can control a full virtual desktop: click, type, scroll, and more. If you're working on an agent that needs to operate real apps (desktop apps, web apps in a browser, etc.), this saves you the headache of setting up and scaling VMs. Just call our API to start a desktop and send actions. Our entire infrastructure, frontend, backend, and SDKs are open source. GitHub: https://github.com/cyberdesk-hq/cyberdesk Docs: https://docs.cyberdesk.io Demo: https://cyberdesk.io Cyberdesk is ideal if you’re: - Building screenshot based computer use agents - Tired of duct-taping Docker, VNC, and cloud infra together - Curious about computer use We’re early and would love feedback — whether it's UX/API design, missing features, or performance. Happy to answer anything. Thanks!

Visit product View on Hacker NewsMay 20, 2025

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

98%98% 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: open source, ide, io · Missing: https docs, excited, just released

72%72% 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 · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

18%18% 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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