Welcome. This blog is where I work through ideas about building data and AI systems what works, what doesn’t, and why.

It’s a mix of projects, experiments, and reflections from working with real systems. I’ll share code, diagrams, and the trade-offs that come up when theory meets production constraints.

The Point

Things are moving fast, especially with GenAI. The open source community has taught me a lot, and I want to share what I’m learning as I figure it out.

This is a working notebook, not a collection of polished tutorials. I’ll document what works, what doesn’t, and the decisions behind implementations. If you’re dealing with similar problems, maybe some of it will be useful.

What You’ll Find Here

I’m focused on data and AI systems, so expect posts about:

  • Architecture patterns and design decisions I’m actively working through
  • Data engineering implementations pipelines, orchestration, governance
  • Practical GenAI applications with real code and deployment patterns
  • Research papers and new techniques I’m testing out
  • Trade-offs, constraints, and what I learned when things didn’t go as planned

Most posts will include code samples, architecture diagrams, or configuration examples. I’ll link to GitHub repos when the implementation is worth sharing.

If you’re building similar systems or dealing with comparable problems moving data platforms to the cloud, implementing RAG architectures, balancing governance with velocity you might find some of this useful.

How This Works

No fixed schedule. I write when there’s something worth thinking through carefully or when an experiment produces results worth documenting.

The blog focuses on how systems actually behave in practice, not just how they’re described in documentation. I test things, measure outcomes, and share what I find.

Thanks for reading. Looking forward to figuring some of this out together.

All code and examples will be available on GitHub.