Merge JPG – Privacy-first and fast image merging tool

Hacker News

Hi HN, I built https://mergejpg.me – a client-side image merging tool that combines multiple JPG images directly in your browser without any server uploads. The Problem: Most image editing tools require uploading your photos to remote servers, which raises privacy concerns for sensitive documents, business presentations, or personal images. The Solution: Complete client-side processing using modern browser APIs. Your images never leave your device. Technical Details: - Built with Next.js 15 and TypeScript - Uses HTML5 Canvas API for image manipulation - Progressive loading for handling 50+ images without performance issues - Supports multiple output formats (JPG, PNG, PDF) - Cross-platform compatibility with intelligent memory management Key Features: - Zero server uploads – everything happens locally - No registration required - Unlimited image processing - Professional-quality output with original resolution preservation - Customizable layouts (horizontal/vertical with spacing controls) Use Cases I've Seen: - Tax consultants merging document scans into PDFs - Real estate photographers creating property showcases - Students compiling research screenshots - Social media managers creating before/after comparisons Why I Built This: After seeing too many "free" tools that harvest user data or require accounts for basic functionality, I wanted to prove that privacy-respecting software doesn't have to compromise on features or performance. The entire processing pipeline runs in your browser – no backend image processing, no cloud storage, no analytics tracking. Just pure client-side functionality. Try it out and let me know what you think! Happy to answer any technical questions about the implementation.

Visit product View on Hacker NewsSep 20, 2025

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45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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