15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T74a,Hi HN,

For the past few years, I've been obsessed with a problem: my hard drive is a treasure chest of knowledge (PDFs, docs, notes) from the last decade, and I want to use modern AI to actually use it.

But every solution forces a trade-off: either convenience (upload everything to the cloud) or privacy (let my files sit dormant locally).

I got tired of this "hold your nose" compromise.

With capable SLMs (Small Language Models) exploding and on-device compute (Apple Silicon, NPUs) finally catching up, I believe privacy and intelligence is no longer a false choice.

So I (and my team) built KnowledgeFocus (Apple Silicon chips only)

It's an open-source (Apache-2.0) knowledge engine built with Tauri (Rust + Python + TS), and it's 100% local-first.

In v0.6.4, it focuses on one thing: unlocking your local file 'treasure chest'.

- Scans & Indexes: It scans your designated local folders (PDFs, .md, .txt, .docx, etc.).

- Auto-tagging: Uses a local model to auto-tag files, so you can aggregate and discover them.

- Local RAG: You can 'chat' with all your local files. It runs RAG 100% on-device. No data (vectors included) ever leaves your machine.

Website (Download): https://github.com/huozhong-in/knowledge-focus

This is just the first step of our "Data Workbench" vision.

I have a lot more thoughts on 'local-first agents', 'data aggregation' (like pulling in your cloud AI chat logs to store them locally), and building a real 'second brain' for knowledge workers.

I'll be in the comments to expand on these ideas and our future roadmap.

I'm here for all the feedback—especially the critical kind. Thanks, H

Charlie Javice Legal Bills

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