Local Search Engine, Built for AI Agents.
From building a resume to digging up visa paperwork, from startup retrospectives to searching a 240,000-word ebook — 12 things I actually did with AI on my own 10,000 local documents.
Linkly AI v0.3.0 introduces library management — organize folders by project or topic, and switch search scope with a single keystroke. MCP tools and CLI are also upgraded so AI Agents can leverage your library structure.
Linkly AI v0.2.2 adds local OCR to automatically extract text from PNG, JPG, BMP, and WEBP images and index it. Screenshots, scans, whiteboard photos — if it has text, you can find it.
Linkly AI v0.2.0 introduces Remote Tunnel, letting ChatGPT, Claude.ai, and other cloud-based AI apps access your local knowledge base through a secure tunnel. Your documents stay on your machine — nothing goes to the cloud.
Evernote, Obsidian, Notion AI — personal knowledge management tools have undergone three paradigm shifts. But the real breakthrough of the 3.0 era isn't about which tool is smarter. It's about letting AI work across all tool boundaries, on your complete knowledge base.
Claude Code, Codex CLI, Gemini CLI — top AI companies have independently converged on the command line. This isn't nostalgia; it's because CLI is naturally suited to AI Agents across three dimensions: composability, predictability, and auditability.
You have 1,000 papers in Zotero, but you still Google Scholar every time you write a review? This post walks through a practical workflow: let Claude search, browse, and read your local literature library.
AI multiplies both the value and the risk of your data. Meanwhile, local compute power, the AI agent toolchain, and your own digital assets are all pointing toward the same conclusion: local-first is not a fallback—it's the future.
Claude is smart, but it has no idea what's in your files. Connect your local documents, and what it can do for you changes completely — here are 4 real-world scenarios.
We spent months building a complete RAG pipeline. It was technically elegant, but we had to admit: it wasn't good enough. Here are the six root problems we encountered, and how we solved them.
