Kiln – a fast, native batch image processor

Hacker News

I was tired of outdated UIs and performance of similar tools. Nor did I want to write ImageMagick scripts every time I needed to resize/convert/watermark a folder with a few thousand images. It's a native desktop app built with C++23, Skia for rendering, libvips for image processing and my own cross-platform UI toolkit - no Electron/Qt. It allows building reusable pipelines: resize, convert, compress, watermark, sharpen, blur, rotate, flip, rename, and metadata (EXIF/IPTC/XMP) editing, chained in any order and saved for reuse. Batches run in parallel. Everything is local — no uploads, no signups/account, no telemetry, no network calls during processing. It's currently in public beta (as mentioned, no signups or accounts needed - can just download), for Windows and Linux (Wayland only). Waiting for the Apple certificate to sign the macOS dmg installer, at which point I'll post the download link for macOS. Business model: free beta now, one I've received enough feedback and the tool is mature enough - a one-time license per major version once it's out of beta. There won't be any subscriptions or telemetry ever. Would love to get your feedback, especially on missing formats/operations (e.g. RAW file formats, watch folders, a CLI) to drive the roadmap to the first official version. Cheers.

Visit product View on Hacker NewsJul 28, 2026

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

76%76% 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, model · Missing: agents, agent, cursor

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

38%38% 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 · Strong signals: subscription · Missing: arr, mrr, revenue

20%20% 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

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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