I am not learning AI just for theory.
I am learning it to build better systems.
That is how I am approaching the Break Through Tech AI fellowship in partnership with Cornell Tech. The coursework, labs, mentorship, and industry project preparation are a way to strengthen the technical foundation behind the products I am already building.
Why it matters
A lot of AI product work fails because the builder can use tools but cannot evaluate systems.
The fellowship is helping me think more carefully about data, model behavior, responsible AI, evaluation, and applied machine learning workflows.
My approach
I am treating the fellowship as a builder’s lab.
The goal is to bring better rigor into PathWise, education tools, RAG systems, and agentic AI products.
That means asking better questions:
- How should this be evaluated?
- What data does the system need?
- Where can the model fail?
- What should the human review?
- How does the product communicate trust?
The lesson
Shipping matters. Learning matters. Improving matters.
The strongest builders do all three.