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Yesterday, at the third Tech Writer’s Tribe Chennai conference, I gave a session on a topic I keep returning to: using personal knowledge management (PKM) to make sense of our work lives.

Since the time slot I had was too short for a tool demo (25 mins), I decided to talk about the set of some #pkm practices that were grounded, flexible, and human.

Here’s what I covered:

  • Using a daily note as a home base for thoughts, tasks, and meetings
  • Timeboxing and interstitial journaling to improve focus and reduce context loss
  • Writing structured notes for meetings and people to build continuity over time
  • Treating PKM as a personal practice, not just a productivity method
  • Offering a glimpse into how Obsidian can support these flows

If you're curious, you can view the slides here.

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In my recent talk yesterday, Documentation for AI and Humans, I discussed how the role of the technical writer is evolving to meet the demands of a dual audience: humans and AI systems. This isn't a minor change; it's a fundamental shift in how we approach documentation.  

We explored several key aspects of this transformation:

  • The Rise of AI Agents: AI agents are now active consumers of documentation, using it to perform tasks and automate processes.  
  • AI Processing vs. Human Reading: AI and humans consume documentation in very different ways, requiring us to adapt our writing style and structure.  
  • Layered Documentation: Creating different layers of information (strategic, tactical, operational) to cater to different needs.  
  • Structured Task Flows: Documenting procedures with explicit steps, expected outcomes, and troubleshooting decision trees.  
  • Machine-Readable Metadata: Embedding metadata within documentation to aid AI interpretation without disrupting human readability
  • Documentation as Code: For AI, documentation is increasingly becoming executable, blurring the line between traditional documentation and software.  

The future of documentation demands a dual-audience approach. By embracing structured content, machine-readable metadata, and a focus on clarity, we can create documentation that serves both human needs and AI capabilities.