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Brainstorm – a local-first, AI-native OS for knowledge work

Hacker News

Brainstorm – a local-first, AI-native OS for knowledge work

Hello, I've been building a local-first open-source knowledge management operating system for 3 months as an AI-native company and would love to hear genuine feedback from the Hacker News community. I've previously worked on a similar project for several years and tried to solve a problem that everything is glued together in common knowledge management apps and fixing one thing usually led to breaking other user experiences. What I was trying to build is disconnecting things and making apps first class citizens, responsible for one single piece of user workflow, like journalling or using whiteboards for example. As the app I was working on had heavy security measures to preserve data sovereignty, I decided that I want the security to be weaved into the system at the core, so all apps are small pieces of software, sandboxed and with their own permissions and capabilities. Furthermore, the system now is a marketplace of assets - apps, themes and agents next. Another major feature is that while being an operating system where agents and humans work together, there is a control and observability layer on everything agents do in the system designed, so the data control remains genuine. Worth mentioning that the system evolves with the apps you install, each app serving as an MCP tool for other apps, for example you can use Agent features in Notes if you have agent app installed and configured, but if you do not you can still use the app normally without these features. Hope to receive some feedback or feature requests. Website: https://getbrainstorm.online Github: https://github.com/brainstorm-os You can verify the security layer and give feedback as well, much appreciated!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
98%98% 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
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, month · Missing: mobile apps, ios, personal
52%52% 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
26%26% 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
16%16% 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.

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