mac-screen-search: CLI find, highlight, redact text on screen, in files

Hacker News

mac-screen-search captures your screen (or takes a glob of existing image files), OCRs the text, and draws colored boxes around every match. Use the `-r` flag to have it fill those boxes solid for redaction or `-b` to blur. It also handles batch processing (overwrites in-place, preserves mtime), enhanced OCR for degraded images (Zoom calls, transparent terminals) with `-e` flag, and Levenshtein fuzzy matching to catch OCR misreads with the `-d N` flag. It includes: screenshot and file-glob modes, exact/fuzzy whole-word matching, redact/blur/outline annotation, multi-monitor, multi-term search, list mode, and output control. Free, open source, written in Swift. Single file, no deps beyond macOS itself. brew install jftuga/tap/mac-screen-search https://github.com/jftuga/mac-screen-search Disclaimer: Vibe coded with Claude Opus 4.6.

Visit product View on Hacker NewsApr 29, 2026

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

94%94% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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: calls · Missing: plus, platform, intuitive

32%32% 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

14%14% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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