Cloakly – Hide sensitive windows from screen shares in real-time

Hacker News

I’m a developer who spends half my day on screen shares. I got tired of the "pre-call ritual" of closing every private app just to make sure I didn't accidentally show a bank balance or a private message during a demo. Existing "Focus" modes hide notifications, but they don't help if you need to share your full desktop for context. I built Cloakly to solve this. It’s a Windows utility that lets you "cloak" specific windows. They stay fully visible and interactive for you, but they are 100% invisible to capture software (Zoom, Teams, Discord, etc.). How it works (briefly): It leverages Windows OS properties to exclude specific window handles from capture streams. It allows you to keep your reference notes or private chats open on the same screen you are sharing. Features: Dual Reality: You see the window; the audience sees your wallpaper/desktop. Ghost Mode: Adjust window transparency to see through to what’s underneath. Stealth: The app can hide its own taskbar presence. I’m launching on Product Hunt today to see if this is a pain point for others or just me. I’d love your feedback on the implementation and whether you’d find this useful in your workflow. PH Link: https://www.producthunt.com/products/cloakly Site: https://www.getcloakly.com/

Visit product View on Hacker NewsJan 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, notes, open · Missing: mac, agents, macos

87%87% 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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

50%50% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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