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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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On this day in 2002, I walked into my very first job at Angler as a Content Developer.

It was the beginning of a journey I couldn’t have fully imagined then, one shaped by learning, change, resilience, and growth. Over these 23 years, I've had the privilege of working across different roles and domains, each one adding its own layer of meaning and experience.

One thing that hasn’t changed is my curiosity. The same inquisitiveness that lit up my first day still drives me forward — to ask better questions, seek deeper understanding, and stay open to the unknown.

I've made my share of mistakes too, and I’m grateful for every single one of them. They’ve taught me humility, perspective, and the value of continuous reflection.

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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.