SubKeep – Nested Labels for Google Keep (Chrome Extension)

Hacker News

I use Google Keep a lot and always felt limited by how labels work - they’re flat and get messy once you have more than a handful. I wanted a way to organize them into nested folders, so I built a Chrome extension called SubKeep. What it does: - Adds a persistent sidebar to Google Keep - Lets you create labels and nest them into sublabels (folders) - Allows expand/collapse of sections, with state remembered across reloads - Lets you toggle between SubKeep’s nested labels and Google Keep’s original labels Runs entirely locally in the browser (no servers, no external storage) Tech: Built with Svelte + Vite, packaged as a Chrome Extension (Manifest V3). State is stored only in chrome.storage.local. Why I built it: Google Keep is my go-to note app, but I wanted better organization without moving to another tool. SubKeep scratches that itch. What’s next: - Backup/export for labels - Possibly cloud sync (opt-in) - Feedback-driven improvements I’d love to hear your thoughts on whether this approach feels useful, or if you’ve hit the same frustrations with Keep. Link: https://chromewebstore.google.com/detail/subkeep/mhikglflelk... I hope that you find this useful. Thanks in advance!

Visit product View on Hacker NewsSep 16, 2025

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

80%80% 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.

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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 · Missing: arr, mrr, revenue

15%15% 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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