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Darkslide – A keyboard-centric photo editor

Hacker News

Darkslide – A keyboard-centric photo editor

Hi HN, I'm building Darkslide ( https://darkslide.app ) - the keyboard-centric photo editor. It is an early, rough, and completely free macOS app looking for feedback from anyone who shoots photos and hates Adobe. The project was born out of two main ideas: 1. Exploring a keyboard-centric UX: I enjoy vim motions and wanted to see if a purely keyboard-focused approach to photo editing is actually valuable. I can basically sum it up as "vim for photos"—the most stereotypically over-engineered developer solution to a creative problem, so feel free to roast me for it. 2. Killing the catalog: I hate how Lightroom or Capture One forces you to manage a .library file, or how others rely on sidecar files. Darkslide manages adjustments automatically behind the scenes. You can just move or duplicate your original photo file in your OS, and the app handles the edits correctly without corrupting a catalog. How it works (90-second demo): Edit one photo, "yank" the edits, select 200, paste, and export. https://youtu.be/4R9l4fgsvT4 Try it out: You can download the current macOS DMG directly here: https://darkslide.app/download/Darkslide.dmg I am not selling anything right now, this is a validation test. Whether you edit photos as a professional or just a hobbyist, I would love your feedback. Is this keyboard-driven approach something you would actually use? Thanks for taking a look. If you want to share feedback - feel free to connect: https://x.com/rmn_vlkv P.S. Keep in mind that macOS will prevent the app from opening right now because Darkslide isn't notarized yet. You can use this solution in your terminal to run it: xattr -r -d com.apple.quarantine /Applications/Darkslide.app

Visit product View on Hacker NewsJul 27, 2026

Actual performance

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

82%82% 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, apple · Missing: agents, agent, cursor

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% 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

15%15% 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.

Correct prediction on native model

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