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Local Notes History – Privacy-first Notes manager

Hacker News

Local Notes History – Privacy-first Notes manager

I built a notes history manager that stores everything locally in your browser (IndexedDB). No server, no tracking, no accounts. Features: Search through your notes history Tag and organize notes Version history with visual diffs Optional password encryption Dark/sepia themes Export/import your data It's just a single HTML file (~30KB), so you can download it and run it locally, or use it at https://anan.guru I made this because I wanted notes history on my work machine without installing software or sending my data to the cloud. Figured others might find it useful too. The UI is inspired by Swiss design principles – clean, functional, minimal. Open to feedback!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% 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: mac, visual, single · Missing: agents, macos, agent
77%77% predicted probability of success on Product Hunt, 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
45%45% 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 · 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
28%28% 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 · 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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