Comparison

DearTech-OS vs LLM Wiki

Andrej Karpathy's LLM Wiki is an open methodology: treat personal and team knowledge as an AI-readable wiki. DearTech-OS shares the same file-first, markdown-canonical principles and adds a typed knowledge graph, an MCP server, and a team layer on top, so a founder-operator team can adopt the methodology in weeks instead of building it from scratch.

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Side by side

Where they differ

CriterionLLM WikiDearTech-OS
ApproachOpen methodology, DIYMaintained system built on the same principles
File formatMarkdown files (your choice)Markdown files (canonical, typed frontmatter)
Graph structureImplicit (wiki-style links between files)Explicit typed graph: typed nodes, status, confidence, relationships
Graph search & traversalFull-text search across markdown, no typed traversalAI searches typed nodes, traverses relationships, audits sources out of the box
Query interfaceWhatever you buildMCP server out of the box, plus CLI and search
AI tool integrationDIYClaude, ChatGPT, Cursor, Codex via MCP
Sharing modelPersonal or single-team wikiMulti-team, role-based access (viewer, maintainer, admin)
Time to team adoptionWeeks to months of building4-8 weeks of guided implementation
Best forSolo developers and small teams who want full control and custom workflowsFounder-operator companies who want a maintained Context OS without building it themselves

Where LLM Wiki excels

  • Free, open methodology with no vendor
  • Full control over schema, structure, and tooling
  • Aligned with the developer mindset of file-first knowledge
  • Strong starting point if you have time and engineering capacity

Where DearTech-OS excels

  • Built-in typed knowledge graph (concepts, patterns, people, status, confidence)
  • Built-in MCP server. Claude, ChatGPT, Cursor query the graph natively.
  • Built-in role-based access (viewer, maintainer, admin) via SSO
  • Audit, healing, and crystallization tools maintained for you
  • Faster time to team adoption, measured in weeks not months
  • Same file-first principles. Your markdown stays canonical and portable.

Decision

When to choose which

Choose LLM Wiki when

  • You are a solo developer or small technical team
  • You want full control over schema and tooling
  • You have engineering time to build and maintain it yourself
  • Role-based access and team scaling are not pressing concerns yet

Choose DearTech-OS when

  • You are a founder-operator with a team that needs shared context
  • You want to skip the build-it-yourself phase and adopt a maintained system
  • You need role-based access for non-technical teammates
  • You want a typed graph and MCP server out of the box, working with Claude and ChatGPT day one

Use them together

DearTech-OS is the same methodology, shipped

DearTech-OS doesn't replace the LLM Wiki philosophy, it operationalizes it. Your markdown stays canonical. Your file-first principles stay intact. The graph, MCP server, and team layer sit on top, so the same methodology you would have built yourself works across Claude, ChatGPT, Cursor, and any MCP-compatible AI tool from week one.

FAQ

Common questions

Can I migrate from an LLM Wiki setup to DearTech-OS?

Yes. Both are markdown-first and file-canonical, so most LLM Wiki setups migrate cleanly. The DearTech-OS audit identifies what already maps to typed graph nodes and what needs structure added.

Why pay for a maintained system if the methodology is open?

Same reason teams pay for any maintained software: faster time to value, role-based access without DIY auth, an MCP server that already works with Claude and ChatGPT, and a typed graph schema that has been refined across multiple deployments.

Does DearTech-OS lock me in?

No. Your markdown files are canonical and portable. The graph is parsed from those files. If you ever stop using DearTech-OS, your knowledge stays exactly where it is: on your disk, in markdown, fully portable.

Is DearTech-OS open source?

DearTech-OS is shipped, licensable software. The methodology is open and we publish about it. The implementation, MCP server, role system, and tooling are the productized layer.

Want the same principles, shipped?

In a 30-minute call, we'll map where your company knowledge is scattered today and identify the first DearTech-OS layer worth building.

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