A lightweight, capture tool for Windows

Hacker News

Hi HN, I’ve always admired Xnapper on macOS for its ability to create beautiful, ready-to-share screenshots with nice backgrounds and padding. However, since there wasn’t a direct Windows equivalent that felt just as snappy and minimal, I decided to build my own. Key Features: Built with C++: Focus on performance and low resource overhead. Ultra-lightweight: The entire app is a single 329KB executable. No Installation Required: Completely portable; just download and run. Clean UI/UX: Designed to make your screenshots look professional instantly. Core Functionality: Includes essential features like smart cropping, customizable backgrounds, and adjustable padding. I’m sharing this because I wanted a tool that does one thing well without the bloat of modern "electron-based" utilities. I’d love to hear your feedback or any feature suggestions you might have! Thank you!

Visit product View on Hacker NewsFeb 27, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

41%41% predicted probability of success on TrustMRR, 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

35%35% 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 · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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