Cloakly – Hide notes and apps during coding interview screen-share

Hacker News

I built a Windows tool to solve a problem I kept running into during live coding interviews and proctored assessments. I wanted to keep reference notes or personal apps open on my own screen without exposing them to the interviewer or screen-share software. Cloakly hides specific windows from the screen-share stream while keeping them fully visible locally. It works with Zoom, Teams, Meet, Loom, and most proctoring tools. Right now the core engine works, and I'm looking for beta testers to help with edge cases before the public release. Would love feedback from anyone who regularly interviews, screenshares, or builds Windows utilities. Beta sign-up: https://www.getcloakly.com Happy to answer any technical questions about how the cloaking layer works.

Visit product View on Hacker NewsNov 26, 2025

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

75%75% 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.

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

46%46% 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: personal, apps · Missing: mobile apps, ios, entrepreneurs

43%43% 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

33%33% 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

17%17% 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.

Correct prediction on native model

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