1 in 11 babies born in America this year will be screened by a genetic test that didn't exist a decade ago.Biotech startup BillionToOne turned a simple but radical idea—detecting rare fragments of fetal DNA in a mother's blood—into one of the most widely used prenatal tests in the U.S. And they're not stopping there. The same approach could unlock something even bigger: early-stage cancer detection from a blood test, a breakthrough that could one day save millions of lives.In this episode of Hard Tech, YC's Jared Friedman sits down with David Tsao and Oguzhan Atay to hear how they went from half a lab bench to a $4B biotech company—and why they believe this is just the beginning of what their technology can do.

In this episode of Founder Firesides, YC Managing Partner Jared Friedman talks to Karine Mellata, co-founder of Variance (W23), who is coming out of stealth and announcing their $21 million Series A.

Variance builds purpose-built AI agents for risk and compliance — automating fraud detection, content review, and identity verification for Fortune 500 companies and platforms like GoFundMe. They discuss why Variance built in the shadows for three years, detecting state-sponsored fraud rings, and the accident that nearly ended the company.

François Chollet has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is—and how to build it from first principles. In this episode of Lightcone, he traces that path from his early work on deep learning to the creation of the ARC prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach.00:00 - AGI by 2030?00:31 - Introducing Ndea: A New Path Beyond Deep Learning01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs03:04 - Why Ndea Isn’t Competing With Coding Agents05:20 - Why Everyone Might Be Wrong About Scaling LLMs07:22 - Why Coding Agents Suddenly Work So Well08:50 - The Limits of LLMs in Non-Verifiable Domains10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong)13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story18:20 - ARC Benchmarks Explained: From V1 to V322:49 - The RL Loop Powering Coding Agents Today27:03 - ARC-AGI V3: Measuring “Agentic Intelligence”31:14 - Inside the ARC Game Studio35:31 - Could AGI Fit in 10,000 Lines of Code?44:01 - Building Ndea: From Idea to Compounding Research Stack46:46 - The Future of ARC: Benchmarks That Evolve With AI47:21 - Why There’s Still Huge Opportunity for New AI Paradigms53:37 - How to Build a Breakout Open Source Project - Lessons From Kera56:39 - Advice For How To Think About AIApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs