My passion lies at the intersection of purposeful technology and captivating experiences.

One of the things I enjoy about business trips is the uninterrupted time they provide for reading. The longer the trip, the better. On my last one, I read Peter Godfrey-Smith’s Other Minds.

During my career as a developer, I often wished for a better way to grow. I wanted a coach that could offer constant, personalized feedback on my code, and a way to measure whether I was truly improving or just spinning my wheels.

This technical blog post goes into methodology, showcasing how I leverage Random Forest, LSTM, and Transformer networks to contribute to more robust predictions.

My fascination with digital biology led me down a rabbit hole. I've been learning extensively from the amazing OpenWorm initiative and applying LangGraph with AI/ML to see if we can give a simulated C. elegans true agency.

An Agent Mesh Architecture for Treasury and Liquidity Management (TLM) with source code on Github. https://github.com/chrisshayan/agentic-tlm

This article unveils a sophisticated AI architecture featuring 12 specialized agents working in concert, powered by LangGraph, RAG, and multi-modal AI, to dissect financial data, predict outcomes, and automate complex underwriting tasks.

This hands-on guide demystifies data contracts and equips you with the practical skills to implement them using the datacontract CLI. Discover how to define robust data agreements, automatically validate schema and quality.

Imagine every employee having their own personalized culture coach !!!

The lending landscape is clearly evolving, and I've been considering how Agentic AI, particularly when orchestrated with LangGraph, might offer new possibilities for Loan Origination Systems.