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Nimbus – Turn how you work into how your agent works

Hacker News

Nimbus – Turn how you work into how your agent works

Nimbus observes your screen as you work and uses AI to understand what you’re doing and why. It asks clarifying questions in real time to capture intent, then organizes everything into structured tasks, steps, and platform knowledge for computer use agents via MCP. The result is that your agent can do your work the way you do it, on internal tools it’s never seen, without being spoon fed every step. We think the missing piece in the agent stack is a production layer for workflow knowledge. I’d love any feedback on the product, especially the privacy tradeoffs of screen observation and whether this feels like the right way to teach computer use agents real workflows.

Visit productView on Hacker NewsMar 24, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
99%99% 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
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% 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 · Missing: plus, intuitive, reviews
19%19% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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