ajagatobby

ajagatobby / Mane-mac-app

Public

Search for documents on your mac using natural language

33
3
100% credibility
Found Feb 05, 2026 at 23 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Swift
AI Summary

Mane AI is a native macOS app that builds a private knowledge base from your local documents, code, images, and audio files for AI-powered semantic search and chat.

How It Works

1
📱 Discover Mane AI

You find the Mane AI app, a private helper that lets you chat with your own files on your Mac.

2
🚀 Download and launch

Grab the app from the releases page and open it to start building your personal knowledge base.

3
🧠 Wake up your AI assistant

Connect your local AI brain with a quick setup so it can understand and answer about your files.

4
📁 Add your files and folders

Drag in documents, code projects, photos, or audio recordings to create your searchable library.

5
💬 Start chatting naturally

Ask questions like 'What’s in my notes?' or 'Summarize my code' and get helpful replies.

🎉 Unlock your data insights

Enjoy private, instant answers with sources from your own files, all running safely on your Mac.

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Star Growth

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AI-Generated Review

What is Mane-mac-app?

Mane-mac-app is a native macOS SwiftUI app that lets you index documents, codebases, images, and audio files into a private local knowledge base, then search and chat about them using natural language powered by Ollama LLMs. It solves the problem of sifting through scattered local files—semantic search finds code handling database connections or images with landscapes, while RAG chat delivers cited answers without cloud uploads. Developers get quick access via a Raycast-like overlay (Cmd+Shift+Space) for searching documents or GitHub code repos on their machine.

Why is it gaining traction?

Unlike cloud-based tools, everything stays local with LanceDB vectors and no telemetry, appealing to privacy-focused users tired of data leaks. Multimodal support shines: transcribe audio, caption images, auto-detect projects from package.json or Cargo.toml for intelligent code search. Native performance and bundling (Node backend as sidecar) make it feel snappy, standing out from clunky Electron alternatives.

Who should use this?

Mac developers drowning in project folders who want semantic search over GitHub repos or local code without leaving their machine. Researchers indexing papers, images, or audio transcripts for quick queries. Power users needing private alternatives to cloud search for documents, bypassing issues like search documents not working in fragmented setups.

Verdict

Grab it if local AI search fits your workflow—solid README and quick-start guide make setup straightforward despite 26 stars and 1.0% credibility signaling early maturity. Test on non-critical data first; lacks broad validation but shows real promise for private doc/code hunting.

(198 words)

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