# Linkly AI > Local Search Engine, Built for AI Agents. ## Blog Posts (en) - [Linkly AI v0.5.1: A Better Chatbot](https://linkly.ai/blog/v051-better-chatbot): Linkly AI v0.5.1 brings verifiable citations, @ context, Workspace, and PowerPoint indexing to Chatbot. - [Cloud Knowledge Bases Are Here](https://linkly.ai/blog/v050-cloud-knowledge-base): Linkly AI v0.5.0 introduces cloud knowledge bases, allowing selected local libraries to stay online 24/7 and be shared with more Agents. - [v0.4.0 Release: Chat with Tens of Thousands of Your Local Files](https://linkly.ai/blog/v040-chat-with-local-files): Linkly AI v0.4.0 introduces Linkly AI Chat — talk to thousands of your notes, PDFs, journals, and project records without ever leaving Linkly. Plus a new Data Privacy panel, local model download progress, in-app changelog, MDX support, and three new languages: Japanese, French, and Spanish. - [What Can AI Do with 10,000 Local Documents? 12 Real Scenarios](https://linkly.ai/blog/12-real-tasks-on-10000-local-docs): 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. - [v0.3.0 Release: Library Management, Finally Complete](https://linkly.ai/blog/v030-library-management): 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. - [v0.2.2 Release: Text in Images Is Now Searchable](https://linkly.ai/blog/v022-ocr-image-search): 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. - [v0.2.0 Release: Connect Your Local Knowledge Base to Online AI](https://linkly.ai/blog/v020-remote-tunnel-release): 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. - [Three Generations of PKM: From Folders to AI Collaboration](https://linkly.ai/blog/pkm-evolution-ai-second-brain): 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. - [Why the Command Line May Be the Most AI-Agent-Friendly Interface](https://linkly.ai/blog/cli-best-interface-for-ai-agents): 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. - [The Researcher's AI Workflow: How to Let AI Read 1,000 Papers](https://linkly.ai/blog/researcher-ai-workflow-managing-1000-papers): 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. - [Why We Believe Local-First Is Becoming More Important in the AI Era](https://linkly.ai/blog/privacy-first-local-ai-knowledge-base): 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. - [When AI Can Access Your Local Files: How Conversations with Claude Change](https://linkly.ai/blog/before-after-local-docs-transform-claude): 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. - [Why We Abandoned RAG: Six Fundamental Problems](https://linkly.ai/blog/why-we-abandoned-rag): 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. - [Without Leaving the Terminal, Let AI Search Every Document on Your Computer](https://linkly.ai/blog/ai-search-docs-without-leaving-terminal): A lightweight Rust CLI tool that connects to the Linkly AI desktop app, enabling you to search, browse, and read local documents from your terminal. It also serves as an MCP bridge for AI agents to access Linkly AI. - [One Command to Let Claude Code and 30+ AI Tools Read Your Local Files](https://linkly.ai/blog/one-command-ai-tools-read-local-files): A skill pack following the Agent Skills open standard. Once installed, 30+ AI platforms including Claude Code and Codex CLI can directly search, browse, and read your local documents. - [Outlines Index: A Progressive Disclosure Approach for Feeding Documents to AI Agents](https://linkly.ai/blog/outlines-index-progressive-disclosure-for-ai-agents): Traditional RAG splits documents into chunks and feeds them to AI. We took a different approach: build a structured outline for each document, letting AI browse like a researcher — scan the table of contents, navigate to relevant sections, then read precisely. - [Linkly AI: Let AI Agents Access Your Local Documents Without Friction](https://linkly.ai/blog/Introducing-linkly-ai): Your contracts, reports, papers, and proposals are AI's 'dark matter.' Traditional RAG chops documents into fragments and feeds them to AI — with poor results. We took a different approach: let AI browse your file cabinet like a researcher.