[name-redacted] – Sharing screen is scary in SaaS age

Hacker News

Sharing screen is really scary today with all PIIs and secrets sprawling around your screen, so I built [name-redacted], a small Chrome extension that spots API keys, tokens, emails, and throws a blur overlay on them in real time. The goal is to make screen-sharing feel safe again without adding steps to a demo. Everything runs locally—regex + entropy heuristics compiled to WASM—and the extra CPU cost averages ~1 ms per mutation on my M1. Custom rules can be added with a JSON file for teams that have proprietary token formats. visit [url-redacted] Feedback please <3

Visit product View on Hacker NewsMay 29, 2025

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

83%83% 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

55%55% 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: io · Missing: https docs, excited, just released

49%49% 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 · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

28%28% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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