Memrem – iOS app to efficiently manage your photos library

Hacker News

“Photos” has been the ever growing place where my cherished memories go to die, smothered in a sea of useless content which I never felt I would be able to filter. Condemning me to pay an ever increasing amount to Apple for extra iCloud storage. Few solutions out there and especially none that was efficient, free or priced reasonably. I decided to learn swift and take matter into my own hands. I had encouraging feedback so far and I hope others enjoy it as much as I do! I deleted close to 2000 photos and 500 videos from my phone in just a few days and it felt enjoyable rather than painful.

Visit product View on Hacker NewsJul 13, 2024

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

90%90% 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.

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

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

53%53% 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 HuntUnlikely to reach the leaderboard · Strong signals: apple · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

23%23% 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

21%21% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

New iOS app to delete similar photos41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New iOS app to delete similar photos

Hacker News5

Unbound, the ultimate iOS app for your Dropbox photos38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Unbound, the ultimate iOS app for your Dropbox photos

Hacker News3

AnimalSoundFun – My first iOS app34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AnimalSoundFun – My first iOS app

Hacker News1

Trident for GitLab – iOS App29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Trident for GitLab – iOS App

Hacker News12

SwipeVideo, my first iOS App34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SwipeVideo, my first iOS App

Hacker News15

Thinglist, my first iOS app34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Thinglist, my first iOS app

Hacker News2

My iOS app to cure boredom34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My iOS app to cure boredom

Hacker News6

My first iOS app - PostParty34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My first iOS app - PostParty

Hacker News1

Never-Bored - My first iOS App34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Never-Bored - My first iOS App

Hacker News15

Yourtaximeter iOS App34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Yourtaximeter iOS App

Hacker News4

Browse all products Analyze your launch