Blende – Simple, Shareable Photo Galleries

Hacker News

Blende ("Aperture" in German) allows you to upload photos via web, Lightroom Classic or Google Photos and arrange them in - IMO - good looking galleries which can easily be shared with friends, family or clients. I built this to fill a personal need: quickly create galleries directly from Lightroom Classic. I started by using the Lightroom Classic Web module but quickly realized it is a little clunky and not a great experience. I decided to write a Lightroom Plugin and platform to address my needs. From there - of course - things spiraled a little and I added things like maps, feedback (comments) and a slew of other features. You can check out some sample galleries (all photos mine) here: https://blende.io/blende/public/galleries You can take a look at docs/guides here: https://blende.io/docs I'd love any and all feedback. Thanks for looking!

Visit product View on Hacker NewsMay 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using · Missing: mac, agents, macos

84%84% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

50%50% 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: arr · Missing: mrr, revenue, profit

14%14% 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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