Clean Stash – Start Organizing Your Screenshots (Testflight App)

Hacker News

Hi HN! Today I'm launching Clean Stash - an iPhone app to organize your screenshots, as an open beta (Testflight). Here's why I built it: I take a lot of screenshots. Like a lot, and of all different use cases. Sometimes of smart marketing copy that I want to use myself, a visual I want to use in a future mood board, a sneaker I want to buy, a linkedIn post I want to quickly reference, places I want to travel...you get the point. In reality, these get lost in my phone's "Screenshots" folder. Now over 2,3k of them. I've even found myself taking screenshots of screenshots only to have them surface as more recent as I scroll. I probably have more screenshots than pictures of my kids (pls don't judge). I wanted a keep all these screenshots in one place, and given back to me in an actionable way. That's why I spent a few weeks building a solution, and today I think it's ready enough for you to use. I'd love to hear how you find this app useful, and what you'd like to have next. Feel free to add your comments below.

Visit product View on Hacker NewsAug 17, 2023

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3points

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Did not reach leaderboard

Launch Intel predictions

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

66%66% 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 · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 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.

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

33%33% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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