Mi

Mindapp – open-source thought organizer

Hacker News

Mindapp – open-source thought organizer

Hi HN, I built my own note taking app that I've been using for several months at this point. Before I used Apple Notes, but the search became really slow after adding too many notes. Then I used VSCode with Markdown and that worked for a couple years, but there was no streamlined way to share notes and I realized often times I would have a note that could be categorized under multiple files. To solve this I made Mindapp. It has a tagging system that allows tags to be subsets of other tags so for example the "San Francisco" tag is a subset of the "Bay Area" and "City" tags - both of which branch out into their own subtags. It also has different spaces that you can store notes in so they can be public or private. Another cool feature is it uses public/private keys to publish under different personas and verify posts. A demo of how I use it can be found at https://www.linkedin.com/feed/update/urn:li:activity:7263871... and the source code at https://github.com/mileung/mindapp I made it for my own needs first but I think it would be cool if I had other users. The main problem is in order to get all the features, the setup process requires the command line and I don't have the bandwidth to streamline that process atm. Lastly, if you'd like to see more improvements to Mindapp, I submitted it to a hackathon and would appreciate it if you voted for it at https://hack.solidjs.com/submissions Let me know if you have any questions!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, user, activity · Missing: mac, agents, macos
86%86% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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: month, users, way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% 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
14%14% 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.

Incorrect prediction on native model

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