Smriti: Your Photos, Your Memories

Hacker News

With a camera in hand, each one of us is a photographer. We take more photos in a week, than our parents did in a decade. Yet, getting to that one photo that matters has never been more difficult. Cloud based services claim to solve this, but are impractical. They want YOUR photos to be uploaded onto THEIR servers, and want you to pay for it, all for a service you'll perhaps use once a month. So I built Smriti, its a fully offline rust based photoviewer, that lets your browse your memories, all without a single photo leaving the device. People recognition, semantic search, Album suggestions, Memories are all the features baked into the application, so adding your photos gives a better than google photos application. Show can try it from the link given. Its built in Rust Tauri- so compatible for Linux, Mac & Windows. Check out the demo site first to see what Smriti offers. And the best part is, once youre done browsing, take out the drive, and all your memories travel with you. Plug it into another pc to get the exact same experience.

Visit product View on Hacker NewsJul 25, 2026

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81%81% 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.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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