Clipboard Genie – a Windows clipboard manager inspired by Paste on Mac

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Hi HN, I built Clipboard Genie, a Windows clipboard manager inspired by Paste on Mac. I loved Paste, but couldn’t find a good equivalent on Windows – so I made one. Key features: - Unlimited history for text, links, files, etc. - Fast search & filtering - Contextual actions (open links, translate text, send to ChatGPT with custom prompts) - Simple automations (e.g. run an action when something is copied) - Cloud sharing (10 GB free quota for clipboard items including files) - AI integration with your own API keys (OpenAI, Gemini) - Clean UI, light & dark mode Tech background: The first functional version was written in WPF. However, after discovering Avalonia (which I believe is the future of cross-platform UI for .NET), I rewrote the entire application. It was the perfect way to learn the framework. Right now it runs on Windows only, but Avalonia should make Linux/macOS builds feasible. I’d love your feedback on: - What features are essential in a clipboard manager for you? - Do you use/need automations or integrations? - Are there any dealbreakers (privacy, performance, UI choices) that would stop you from trying a tool like this? Thanks for taking a look!

Visit product View on Hacker NewsSep 30, 2025

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

95%95% 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: mac, macos, context · Missing: agents, agent, cursor

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

53%53% 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: way · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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