Dim – An auto-tagging notes app

Hacker News

hi everyone, I’m building a note-taking app that auto-tags and groups your notes. The idea is simple: just write and don’t worry about organizing. I’ve always struggled with organizing my notes in just any tools. I tried creating tags & folders in the apple notes but as I have more and more tags and folders, I still got lost in organizing. It just became too much effort so I just gave up any organizing. So I started looking for something where I could just write. No setup, no structure. Just drop anything, and let the system organize the rest. I didn't find anything similar to what I need yet so I decided to build it my own. So the app I made does that—it auto-tags your notes based on content and groups similar notes together. That’s basically it. No extra features. It’s still super super early. If this sounds useful to you and you’d be down to try it out and give feedback, I’d love your help!! We just launched in App Store: https://apps.apple.com/us/app/dim-auto-tag-your-notes/id6741... Thank you.

Visit product View on Hacker NewsJul 24, 2025

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, notes · Missing: mac, agents, macos

77%77% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% 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 · Strong signals: apps, way · Missing: mobile apps, ios, personal

37%37% 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

29%29% 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

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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