Claude, ChatGPT, and other AI assistants are remarkably capable — but they only know what’s on the internet, what you’ve manually uploaded, and what you’ve typed into the chat. They’re missing the most valuable context you have: everything stored on your computer.
This isn’t a Claude problem. It’s a structural information isolation problem. Most of your digitized context lives in files on your computer — documents that capture your work, your research, your decisions. Between those files and your AI assistant sits an invisible wall.
Here are 4 real scenarios showing what changes when you tear that wall down with Linkly AI.
Scenario 1: Digging Through Old Project Retrospectives
You want to review all your 2023 project retrospectives to see what technical mistakes came up. To do this without Linkly AI, you’d open Finder, browse folder by folder, hunt down each retrospective document, open it, select the relevant paragraphs, copy them, paste them into the chat, and say “help me organize this.”
Before
Claude can only work with whatever you paste in.
You: Help me organize the technical issues mentioned in this excerpt.
Claude: This excerpt mentions two issues: first, a performance bottleneck caused by misconfigured database connection pooling, and second…
What you get back is a polished version of what you already found yourself.
After
You open Claude and simply say:
You: Go through all my 2023 project retrospectives on my computer and organize the technical issues by severity.
Claude: Sure, let me search your local documents.
[calling search(“2023 project retrospective technical issues”)]
Found 7 relevant files: Q1-retrospective.md, e-commerce-platform-refactor-retro.pdf…
[calling outline([“e-commerce-platform-refactor-retro.pdf”])]
Document structure: 1. Project Background 2. Timeline 3. Technical Issues Summary 4. Lessons Learned
[calling read(“e-commerce-platform-refactor-retro.pdf”, “Technical Issues Summary”)]
Claude: Based on your 7 retrospectives from 2023, here’s the technical issues list ranked by impact:
- Critical: Database connection pool misconfiguration (appeared in 3 retrospectives), Redis cache stampede…
- Medium: Missing type checking in the frontend build pipeline…
You didn’t copy-paste a thing. Claude found all the relevant files itself, browsed their structure, read the key sections, and synthesized a cross-document summary.
Scenario 2: Reading a Technical Specification
You’ve saved the HTTP/3 RFC spec as a PDF and want to understand how Connection Migration works. Without Linkly AI, you’d open the PDF, use the built-in search to find the “connection migration” chapter, flip to that page, select the relevant paragraphs, copy them out, and paste them into the chat.
Before
If you didn’t copy enough context, Claude’s explanation will be incomplete and you’ll have to go back for more.
You: Explain this section about connection migration.
Claude: This section describes…
You’re limited to whatever you managed to select and copy.
After
You ask directly:
You: Look at the HTTP/3 spec I have saved on my computer. Explain connection migration — what problem it solves and how it works.
Claude: Sure, let me find it.
[calling search(“HTTP/3 Connection Migration RFC”)]
Found: rfc9000-quic-transport.pdf
[calling outline([“rfc9000-quic-transport.pdf”])]
Chapter 9: Connection Migration, Chapter 10: …
[calling read(“rfc9000-quic-transport.pdf”, “Chapter 9 Connection Migration”)]
Claude: HTTP/3 (built on QUIC) connection migration solves a fundamental TCP limitation: TCP connections are tied to a 4-tuple (source IP + source port + destination IP + destination port). The moment your IP address changes — say, switching from Wi-Fi to cellular — the connection breaks and you need to re-establish it.
QUIC identifies connections by Connection ID rather than the 4-tuple…
Claude located the RFC itself, found Chapter 9, read the full connection migration description, and gave you a complete explanation. You never opened a PDF.
Scenario 3: Writing a Quarterly Report
Your Q1 report is due. You open Claude and try to recall what you did: “this quarter I mainly worked on user system refactoring, drove code standards adoption…” Or you spend 20 minutes digging through work logs and copy-pasting the highlights.
Before
Claude writes something, but it’s based on your verbal summary — thin, lacking specific data, needing multiple revision rounds. Even if you dug through your logs manually, the content improves but you’re already exhausted.
You: Help me write a Q1 work summary. This quarter I mainly did user system refactoring, drove code standards adoption…
Claude: Here’s a Q1 work summary based on the information you provided…
What you get is a polished version of your verbal sketch. The actual data is still your job to fill in.
After
You just say:
You: Write me a Q1 work summary. Search my work log folder on my computer for the past three months, pull out the key accomplishments, and draft something.
Claude: On it, let me search.
[calling search(“work log 2025 Q1”)]
Found: 2025-01-work-log.md, 2025-02-work-log.md, 2025-03-work-log.md
[calling read(“2025-01-work-log.md”)] [calling read(“2025-02-work-log.md”)] [calling read(“2025-03-work-log.md”)]
Claude: Based on your three months of work logs, here’s a draft:
Q1 Key Accomplishments
- User system refactor shipped: completed DB migration, reduced registration time from 1.2s to 380ms…
- Code standards rollout: authored TypeScript standards doc, adopted across 8-person team…
You didn’t flip through anything manually. Claude read three months of logs, extracted the key facts, and handed you a data-backed first draft.
Scenario 4: Analysis Based on Private Research
You’re doing market analysis for a new product. You’ve spent considerable time compiling competitor analysis docs and industry reports — all saved locally. But Claude doesn’t know any of this. The best you can do is open a file, copy the most relevant paragraphs, and paste them in to ask about.
Before
Claude’s analysis is based on whatever fragments you paste in. It doesn’t know the full context you’ve assembled. What you get is generic analysis based on publicly available information.
You: Analyze the competitive landscape for note-taking tools.
Claude: The note-taking market is competitive, with major players including Notion, Obsidian… (generic answer)
What you get has nothing to do with your private research. Claude is just drawing on what it already knows.
After
You say:
You: Analyze the competitive landscape for note-taking tools. Use Linkly AI to search my computer for my competitor analyses and industry reports — don’t give generic conclusions, use my actual research.
Claude: Sure, searching your local files now.
[calling search(“notes app competitive analysis”)]
Found: Notion-competitor-analysis-2024.pdf, Obsidian-user-research.docx, 2024-note-taking-industry-report.pdf
[calling outline([“Notion-competitor-analysis-2024.pdf”, “2024-note-taking-industry-report.pdf”])]
[calling read(“Notion-competitor-analysis-2024.pdf”, “User Retention Analysis”)]
Claude: Based on the three documents you’ve assembled, the central tension in the note-taking competitive landscape is:
According to your Notion competitor analysis, Notion’s biggest retention problem is the “complexity trap” — high onboarding cost, with 70% of Power Users utilizing less than 20% of features…
Your industry report notes that the fastest-growing segment in 2024 was…
This analysis draws on the private research you spent time building. Your accumulated knowledge is, for the first time, actually participating in the AI’s reasoning.
Setup: About 5 Minutes
Getting Claude to read your local documents requires two things: Linkly AI building a local index, and connecting it to Claude via MCP.
Step 1: Install Linkly AI
Download Linkly AI, install it, and on first launch select your documents directory (e.g. ~/Documents). It will start indexing in the background automatically.
Step 2: Configure MCP
Linkly AI auto-starts an MCP service on port 60606 after installation. Configure it in your AI tool:
- Claude Code: run
claude mcp add --transport http linkly-ai http://127.0.0.1:60606/mcpin your terminal - Cursor: Settings → MCP Servers → Add, Type:
StreamableHTTP, URL:http://127.0.0.1:60606/mcp
Full steps in the MCP configuration docs and quickstart guide.
Step 3: Verify
Send a message in Claude:
Search my local documents for anything related to project retrospectives.
If Claude calls the search tool and returns results, you’re all set.
Linkly AI uses Outlines Index technology — AI navigates your files via search → outline → read progressively, rather than receiving pre-chunked fragments. This makes it significantly more effective than traditional RAG for long documents and cross-document analysis.
Claude didn’t get smarter.
You finally gave it permission to read your files.
Those hundreds of PDFs, Word docs, and Markdown notes on your computer have been waiting to be used. Once connected, what your AI assistant can do for you is in a completely different league.
Download Linkly AI — five minutes of setup, permanently different experience.
From the Linkly AI team.