I've spent two years at the intersection of AI product development and user adoption.
As an Anthropic Campus Ambassador at UC Berkeley, I run the frontline of how students discover and use Claude — organizing workshops, hackathons, and demos for hundreds of students. That work gives me pattern recognition most builders don't have: I see what excites new users, what confuses them, and where the gap between a model's capabilities and someone's actual workflow becomes a wall.
At SJF Ventures, I built AI tools for investors who had never touched a terminal — Salesforce automations, intelligent search engines for deal sourcing, and portfolio monitoring systems. The constraint was always the same: if the person using this can't understand it in 30 seconds, it doesn't matter how good the code is.
Now I build my own products. I prototype fast, test with real users, and iterate the same night. Every project below started with a pattern I noticed and a question I couldn't stop thinking about.
Projects
Things I've built.
Claude Showcase
A platform for students to share what they've built with Claude.
Ambassadors needed a central place to highlight student projects — demos were scattered across Slack threads and forgotten Google Docs. I built a showcase platform where students submit builds, browse what others have made, and get inspired to start their own. Shipped it to the ambassador community and received immediate adoption.
AI Toolkit for Venture Capital
AI-powered deal sourcing and portfolio tools for non-technical investors.
During my internship at SJF Ventures, no one on the team had an engineering background, and off-the-shelf AI tools didn't fit their workflows. I built a suite of internal tools: a Salesforce automation for logging portfolio updates, an intelligent search engine for deal sourcing, and research assistants that could surface relevant market data. Every tool was designed for people who communicate in insights, not code.
Personal Finance Assistant
A conversational interface for understanding your money.
Integrated the Plaid API to let users connect their bank accounts and ask natural-language questions about their spending, savings, and trends. The goal was to make financial data feel approachable — something you talk to, not a spreadsheet you stare at.
Writing
How I think about building.
November 2025
Yes, Even You Can Become TechnicalMy experience going from building no apps to building side projects every other week. A guide to when vibecoding works, when it breaks down, and the full stack I use to ship fast.
Read on SubstackSeptember 2025
I Vibecoded My Way Through My InternshipLessons from building AI tools at a venture capital firm where no one else had an engineering background. On choosing the right tech stack, prompt engineering as a skill, and building for the customer.
Read on SubstackApproach
How I work.
Observe first, build second
As a campus ambassador, I see what confuses and excites users every week. Every project starts with a real pattern — a pain point someone mentioned at a demo, a workflow that clearly isn't working, a question students keep asking. The best products come from paying attention before opening a code editor.
Ship fast, learn faster
Vibecoding collapses the feedback loop. I prototype in hours, put it in front of users, and iterate that same night. The code quality doesn't matter if the product is wrong — but once people are actually using something, that's when you invest in making it solid. Be wrong fast so you can be right faster.
Build for the person, not the prompt
My best work has been for non-technical users — investors, students, community members — who need AI to feel like a natural extension of their existing workflow. If someone has to think about the technology, you've already lost them. The interface should disappear.