Ever since I was a kid, I loved creating things—starting with Legos and model airplanes. Around 12 or 13, that creative energy shifted to computers. I built my own PC, taught myself to code, and made a website for my mom. That’s when I realised I could build things that were useful, not just cool to look at.

At 15, I joined a full-stack bootcamp in Toronto—surrounded by adults—and met two founders who needed a live chat interface. Coincidentally, I had already been building one. They hired me for the summer, and that was my first real startup experience. After finishing Grade 12, they hired me full-time. I took two gap years and became the lead developer at #paid, an influencer marketing startup working with brands like Coca-Cola and Toyota.

After two years, I felt stuck. As an engineer, you build what others tell you—but I wanted to decide what to build. I enrolled at the University of Waterloo but kept working on side projects. One of them was a startup called Conversify, which I left after co-founder issues. Not long after, an acquaintance reached out with an idea for a viral meme app. I helped build it. That app, Wombo.ai, hit 100 million downloads in six months.

But again—bad co-founder decisions. After taking the app from nothing to something, the guy forced me and the other co-founder out and replaced me with three engineers. But honestly, that turned out to be a good thing. It gave me a reset I asked myself: What do I actually want to be known for? A joke app wasn’t it. So I dove into climate tech in 2022, learning about carbon credits and the voluntary carbon market. I realised it resembled a commodities market—and thought: Why don’t we have futures for carbon?

To make that happen, I explored crypto, since it allows rapid experimentation with financial instruments. I co-founded Eden DAO, raised $120k in donations, and another $100k to buy permanent carbon removals—ranking us above companies like BlackRock and Harvard at the time.

Eventually, I burned out. I went to Burning Man, reconnected with my creative drive, and by early 2023, I became fascinated by AI agents—especially during the Auto-GPT wave. I noticed everyone was trying to build one super-agent. But drawing from systems thinking and metaphors—like multi-core CPUs and human teams—I believed multi-agent systems would be the future.

I built a proof of concept and showed it to Harrison at LangChain. He liked it and invited me to do a guest blog post. That post caught the attention of Guohao Li at Camel AI, a leading multi-agent lab. He invited me to join as a researcher. I worked alongside a PhD student from NUS, Benjamin Lee, on multi-agent Minecraft.

Through Twitter, I discovered DSPy, a framework for building multi-step LLM systems, created by Stanford PhD Omar Khat. I reached out, met him at Stanford in early 2024, and asked, What’s your biggest problem? He told me. We whiteboarded for hours, and he said, If you solve this, you’ll be a core contributor. So I did.

I helped build multi-LLM support for DSPy. Given my experience with AI apps, I knew how painful prompt engineering was—so DSPy’s automated prompt engineering stood out. I partnered with a friend to offer consulting based on it. After one gig, we felt like we had something strong. We applied to YC, got the interview, got in, and started building Zenbase AI.

It’s been a winding road with false starts and lessons, but one of the biggest: choose your co-founder wisely—you're basically getting married.

Building with LLMs today is a mess.

Even the most sophisticated AI teams (including at Meta, Microsoft, Google) have engineers burning time in prompt hell. The result? Missed ship dates, unreliable apps, and high infra bills — all while business stakeholders ask: “Why is this still not working?”

We’re core contributors to Stanford NLP’s DSPy, the #1 LLM optimization repo (16k+ ⭐️), used by engineers at Meta, Microsoft, Amazon, and 40+ others.
We’ve seen firsthand how hard it is to take these ideas to production.

Zenbase turns DSPy’s power into a product.

Zenbase is the production-ready platform that automates prompt engineering and model selection — so developers can focus on building great AI products, not fiddling with prompts. Here's how it works:

You get automated improvement, traceability, and cross-model flexibility — all without needing to understand prompt tokens or fine-tuning knobs.

What We Offer

DSPy really blew up last year—from 5,000 to 20,000 GitHub stars. At AI events, you’d meet hardcore DSPy fans hyping it up. So VCs started asking, "What is DSPy?" And here we were—core contributors with 10 years of engineering and startup experience. We weren’t just hobbyists; we were experts in a fast-growing, technical field. Plus, DSPy’s angle—automated prompt engineering—offered a promising dev tool for a new category. So from YC’s lens, we checked the three key boxes: