Y Combinator

Paul Graham is back on The Social Radars with @jesslivingston and @cjoneslevy. This time, they focus on what was happening behind the scenes at Y Combinator in the early years. PG shares how YC grew through word of mouth, what it was like working closely with founders, and how much of it they figured out as they went. Along the way, he recalls stories from the early batches—the advice that stuck, and how small decisions shaped what companies became. He also reflects on how the startup world has changed since then, from becoming more visible to how AI is influencing what founders build now. 0:00 - Paul Graham Returns 0:52 - “We Never Tried to Grow YC” 3:38 - How YC Actually Got Its First Founders 7:12 - Why Press Didn’t Matter 12:06 - The Real Reason YC Worked 17:48 - What It Felt Like Behind the Scenes 24:31 - How They Helped Founders Day to Day 30:57 - Writing Demo Day Pitches 36:42 - When Everything Started Getting Harder 44:18 - The Sam Altman Transition 52:06 - What Surprised PG About AI and the Next Generation of Founders 59:21 - Final Thoughts

If you really want to hold yourself to a high standard, graph the growth rate of the number you care about instead of the number itself. Then you're winning if you can even keep it flat.

Perplexity is now powering health connectivity for millions of users through @TerraAPI Given that the best AI labs are moving into the health space, here's what I think is coming: You make thousands of daily decisions that change by the second Biomarkers, workouts, meals, sleep cycles, and stressors We are not meant to hold all this information No matter how brilliant your physician is, they see you for 15 minutes and work from a snapshot. The human brain doesn't scale to this problem AI does The doctor becomes the person you go to for surgery, and for judgment under uncertainty No doctor will ever know you better than your AI And software will be written for you daily Today, a doctor looks at a snapshot and puts you in a bucket. "Pre-diabetic", "at risk". These are population labels applied to an individual They tell you where you are. They don't tell you where you're going With continuous, full-context reasoning, the system doesn't label you, it tracks you. Your testosterone has drifted 10% over 10 months, your performance is dropping, here's exactly what to change this week to reverse it Medicine finally gets a feedback loop Chronic means we caught it too late. By the time you get the label - diabetes, heart disease, autoimmune - the damage has been accumulating for years. The disease isn't the problem. The delay is A system that monitors continuously doesn't wait for symptoms. It sees the drift at month 2, not year 10. The intervention is early, precise, and adjusts as your data changes. The feedback loop confirms it's working in days, not decades Chronic disease is a timing failure. The timing problem is solved

Helion has taken the #1 spot on the @geekwire 200 🎉

We moved to SF at 19 with hundreds of dollars last November. Since then, we got into YC and today we're launching Sonarly (@sonarlyhq). The missing layer of self-healing software. Coding agents are good enough. Building software is becoming easier because the codebase is the only context needed. Fixing it is much harder : context is spread across tools and human knowledge. We're fixing that. Sonarly is the context infrastructure that lets AI agents fix software autonomously.

Announcing 10x data efficiency on NanoGPT Slowrun! There are two macro trends worth highlighting: - pretraining is nowhere close to done, and - 100x looks feasible. Writeup on all the core ideas: qlabs.sh/10x

Paul Graham is back in the latest Social Radars, talking about what went on behind the scenes in the early days of YC. If you like the fly-on-the-wallness of Social Radars interviews, this is the most fly-on-the-wall of all. pod.link/1677066062/epi…

Tune in on: YouTube: youtube.com/watch?v=2Q2uh1… Spotify: open.spotify.com/episode/2sogpO… Apple Podcasts: podcasts.apple.com/us/podcast/pau…

Paul Graham is back on The Social Radars with @jesslivingston and @cjoneslevy. This time, they focus on what was happening behind the scenes at Y Combinator in the early years. PG shares how YC grew through word of mouth, what it was like working closely with founders, and how much of it they figured out as they went. Along the way, he recalls stories from the early batches—the advice that stuck, and how small decisions shaped what companies became. He also reflects on how the startup world has changed since then, from becoming more visible to how AI is influencing what founders build now. 0:00 - Paul Graham Returns 0:52 - “We Never Tried to Grow YC” 3:38 - How YC Actually Got Its First Founders 7:12 - Why Press Didn’t Matter 12:06 - The Real Reason YC Worked 17:48 - What It Felt Like Behind the Scenes 24:31 - How They Helped Founders Day to Day 30:57 - Writing Demo Day Pitches 36:42 - When Everything Started Getting Harder 44:18 - The Sam Altman Transition 52:06 - What Surprised PG About AI and the Next Generation of Founders 59:21 - Final Thoughts

Another advantage of focusing on growth rate rather than absolute numbers is that it makes it easier to switch to a new variant of the product if you discover one. It makes it easier to see tails that will eventually wag the dog.

Re-introducing...Bond, your AI Chief of Staff. You worked 10 hours yesterday. Maybe 2 of them actually mattered. The rest? Standups, Slack threads, status updates, triaging emails. Executives don't need to work harder. They need Bond 👉 bondapp.io

Today we’re launching the newest version of @paradigmai When we started Paradigm, the goal was never to tack AI onto existing spreadsheets. It was to build a new type of interface that does the work for you. Now we’re pushing that vision much further. Workflows turn Paradigm into a system that runs research processes for you. Connect your CRM, existing spreadsheets, Slack, email, and internal data, and let Paradigm continuously run the research workflows your team already does. Same intuitive interface. But now a system of action. If you tried Paradigm before, try it again. Manual research is now a competitive liability.

Rippling is going to be one of the main companies where AI meets organizations. They're still young enough to embrace AI thoroughly, but they're also big enough that they touch organizations in many places.

Introducing Naive - hire autonomous employees with their own identity. Own compute. Own bank account. Own legal entity. Own email. Own credentials. Own mobile. No humans-in-the-loop. They sign up for tools, pay for services, deploy apps, file documents, and run your entire company. Describe a business. Naive runs it. Reply "Naive" + RT. Get $100 credit for free.

Healthcare software was designed for humans. Multi-step, nuanced workflows: prior auth submissions, EHR note creation, eligibility verification. The kind of work that can't be reduced to an API call. That's what AI agents in healthcare are being asked to automate. And the infrastructure to do it reliably doesn't exist off the shelf. We build it: A coding agent to generate automation scripts, fully managed infrastructure to run them at scale, and a maintenance agent to keep them working as portals and EHRs change. Today, we're announcing our $5M seed round, backed by Floating Point, @MeridianStCap, Twine Ventures, @refractvc and angels like @zacharylipton (CTO, Abridge) and @dps (fmr. CTO, Stripe). If you're building AI agents that need to operate payer portals or EHRs, we'd love to talk. And we're hiring!

Congrats to @beparallelhq on their $20M Series A! Parallel's AI agents integrate directly into hospital software to automate administrative workflows. In less than 12 months, they've deployed across dozens of hospitals, reached millions in ARR, and recovered tens of millions for clients—with a team of just over 10 people. tech.eu/2026/03/19/par…

introducing gpu acceleration, now in limited research preview. you can now play doom 3, compiled to wasm, on our cloud browsers at up to 60 fps.

Latent was the fastest company I’ve seen go from pivot to 6-figure contract from a huge enterprise customer during the YC batch. The secret is they live on planes, flying to meet their customers wherever they are. No surprise they’ve built a killer company!

Doctors would need 27-hour days to keep up with modern care. So we built something better. Beacon Health (YC W26) × @EmpiricalHLTH (YC S23) are partnering to make AI-enhanced preventive cardiovascular care available to all, starting with Medicare’s ACCESS model. 2/3 of Medicare beneficiaries are eligible. If you're a primary care practice who wants better heart health for your patients, get in touch.

🦾@RevolveSurgical just received FDA 510(k) clearance for their surgical robot platform. They built a system that mounts directly to the operating table—zero footprint, one-person setup. Their goal is to bring physical AI to operating rooms at scale. massdevice.com/revolve-surgic…

Building in AI means throwing away work you're proud of when the models improve. That changes how we build things. Your platform can’t be anchored, it has to be built with low ego and move with the tide. Listen to the chat with @chetanp and @jaltma youtube.com/watch?v=DdHl7d…