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Build an OKF brain like mine!
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Build an OKF brain like mine!

Introduction to OKF and Personal Brain

Overview of the Video

  • The speaker discusses their previous popular video on Google's Open Knowledge Format (OKF) and mentions they have been working on their own version since then.
  • They express excitement about the capabilities of OKF, referring to it as "absolutely amazing."

Summary of Google’s Open Knowledge Format

  • A brief summary of OKF is provided, with a recommendation to watch the previous video for more details. The speaker shows their personal brain built using OKF.
  • They emphasize daily usage in their work and promise to share tools for viewers to create similar systems.

Structure and Standards of OKF

Importance of Standardization

  • The power of OKF lies in its standard structure, allowing interoperability between different agents that can read these files easily. Comments from viewers suggest skepticism about originality, but the speaker clarifies that Google established a standard format.

Recommended File Structure

  • The recommended structure includes an index.md file which serves as a directory listing for agents accessing the knowledge base. Markdown is highlighted as a simple text formatting method used in this context.

YAML Front Matter

Understanding YAML Front Matter

  • Each file must include YAML front matter, which provides essential metadata about the content within that file, guiding agents on what information is present. This section is crucial for effective navigation by AI models.

Required Elements in Files

  • Essential elements include type designation, title, description, and optional tags for better connectivity among files; timestamps are also beneficial but not mandatory.

Skills vs. Open Knowledge Format

Clarifying Concepts

  • There’s confusion regarding the distinction between skills.md files and what constitutes an open knowledge format; both may contain steps or instructions relevant to tasks performed by agents like Antigravity or Claude.

Practical Application Example

  • An example illustrates how playbooks were created within the OKF framework to streamline proposal generation processes efficiently using learned steps from past experiences with clients.

Types Within OKF

Types Defined

  • The speaker categorizes types such as concepts, entities (noted misspelling), playbooks, references, and systems based on examples from Google documentation; flexibility exists in defining these types according to user needs.

Filtering Information

  • Types serve as high-level categories aiding in filtering information effectively within one's personal brain setup; initial attempts at categorization may lead to overcomplication similar to early blogging experiences with WordPress categories versus tags.

Comparison: OKF vs RAG

Distinction Between Methods

  • Unlike retrieval augmented generation (RAG), which utilizes extensive context windows filled with data requiring many tokens, OKFs provide structured directories that enhance efficiency without overwhelming token limits during processing by AI models like Gemini or Claude.

Playbooks and Their Utility

Creating Playbooks

  • Playbooks are utilized for specific tasks such as communication style guidelines or procedural checklists post-Google updates; they significantly reduce time spent on repetitive tasks through automation via agent assistance.

Impact of AI on Employment

Job Security Perspectives

  • The speaker shares mixed feelings about AI's impact on jobs—while some roles may be lost due to advancements in technology, those who leverage AI will likely enhance productivity rather than face job loss themselves.

References Section

Importance of References

  • A dedicated section within the personal brain contains valuable resources including Google's SEO starter guide; this allows quick access to authoritative quotes when needed during client interactions.

Demonstration of Personal Brain Functionality

Visualizing Data Organization

  • A visual representation showcases various markdown files categorized under different types like playbooks and concepts generated automatically by the system based on input data provided by users.

Ingesting New Information into the System

Process Explanation

  • The process involves instructing an agent (e.g., Antigravity) to ingest new information from external sources instead of manually summarizing content; this enhances efficiency while maintaining accuracy.

Updates Made by Agent

Tracking Changes

  • After analyzing new documentation updates from Google Search Console features related to AI controls, changes made by agents are tracked systematically ensuring all relevant information remains current within one’s knowledge base.

Querying New Information

Utilizing Updated Content

  • Users can query newly ingested content directly through their personal brain interface allowing them quick access summaries tailored specifically for client reports or other applications enhancing overall productivity.

Community Engagement & Future Plans

Building Community Resources

  • Plans are shared regarding creating personalized newsletters utilizing community-generated stories combined with curated insights aimed at keeping members informed about developments in search engine optimization (SEO).

Conclusion: Embracing Technology

Encouragement for Experimentation

  • Viewers are encouraged to experiment with building their own versions of OKFs using available tools while acknowledging potential learning curves associated with mastering new technologies effectively over time.
Video description

In my last video, I introduced the concept of Google's Open Knowledge Format (OKF). Today, I'm showing you exactly what I've built with it. I have created a personal brain that I use every day to automate complex SEO tasks, generate proposals, and analyze Google search updates. We walk through the structure of an OKF file, including the YAML frontmatter and how it differs from traditional RAG. If you want to make your data and processes agent-ready, my hope is that what I'm building will help you build your own OKF brain. Blog post on this: https://www.mariehaynes.com/build-an-okf-brain-like-mine/ To build something similar, give these links to your agent (Claude Code / Cowork, ChatGPT Codex or my favourite, Google's Antigravity) and try this prompt: "I want to build an OKF system similar to Marie's. Read these links and then give me some ideas of what this would look like. Then, ask me questions one at a time so that together, we can decide what we want to build: https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing/ https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f https://www.mariehaynes.com/okf/ https://youtu.be/esYAIA6lU-s" Timestamps: =========== 0:00 - Introduction to my OKF personal brain project 0:54 - Accessing Google's OKF documentation links 1:27 - The power of OKF's standardized structure 2:30 - Explaining YAML front matter requirements 3:42 - OKF playbooks versus skills.mmd files 4:55 - The basic structure of my OKF brain 6:17 - How OKF differs from standard RAG 7:34 - Creating custom playbooks for client work 9:03 - AI will boost productivity, not replace jobs 10:19 - Storing Google SEO documentation as references 11:05 - Seeing the OKF knowledge graph in action 12:36 - Inspecting a markdown file on AI overviews 14:05 - Ingesting new documentation into the OKF brain 16:44 - Approving the agent's OKF update plan 18:03 - Reviewing the newly created markdown reference 18:52 - Asking the OKF brain to summarize data 20:02 - Using OKF for community newsletters 21:38 - Why Google remains an AI leader 22:01 - Getting started with coding agents and OKF