ScrollSnap – Open-Source macOS App for Scrolling Screenshots in Swift

Hacker News

Hi HN, I built ScrollSnap, a free, open-source macOS app to capture scrolling screenshots (e.g., long web pages, documents, or chats) in a single image. It’s built in Swift using Apple's native APIs for a lightweight, seamless experience. Features: - Auto-scrolls and stitches content with no manual effort - Customizable capture area with a visual overlay - Multi-monitor support GitHub: https://github.com/brkgng/ScrollSnap This is v1.0, so expect some rough edges (e.g., occasional stitching glitches on complex pages). I'd love feedback, especially from macOS and dev tool enthusiasts. It’s very early (v1), so bug reports or pull requests are more than welcome. Thanks for checking it out!

Visit product View on Hacker NewsMay 22, 2025

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

96%96% 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

84%84% 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

43%43% 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 · Missing: mobile apps, ios, personal

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

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

34%34% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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