“I supervised Nida in her role as a Residence Don at Queen's during the 2014-15 academic year. I found Nida to be an enthusiastic and dedicated leader who consistently pushed herself to grow both personally and professionally. Nida's diligence, openness to feedback, and strong organization and communication skills make her someone I would recommend for any position.”
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Tuukka Järvenpää
Why early product decisions feel harder than later ones Early product decisions are supposed to feel hard. Before a company exists, you’re making choices with: - No data - No customers - No team to pressure test your thinking - No momentum to hide behind That’s not a failure of skill. That’s the situation. Later in a startup’s life, decisions get easier because: - You have constraints - You have signals - You have feedback loops - You have consequences you can observe Early on, you have almost none of that. So the brain fills the gap with doubt, overthinking and comparison. It’s easy to expect that a good idea would feel clearer to decide on. In reality, clarity usually comes later, not first. Clarity doesn’t precede action. It follows action and reflection. A more useful question than “Is this the right idea?” is: “What would teach me the most about whether this is worth pursuing?” If you’re early and everything feels fuzzy, that’s not a sign you’re doing it wrong. It’s a sign you’re early. If you’re in this phase and want to talk through how to turn uncertainty into learning, feel free to reach out.
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Michael H.
Super excited to announce our $10M seed, led by Accel with participation from Y Combinator. Every medical professional I talk to is spinning their tires on AI trying to figure out how to actually deploy it, whether it should touch patients, whether it'll break the stuff they've already got running. My whole job at Clarion is figuring out what will genuinely help, on both the provider and the patient side. Here's the pattern I see every time: some point solution automates one slice of the workflow, the fit is clunky, and within a month the staff has built a workaround. Then what? The practice is left with one more tool and the same problems. Yikes. What AI actually needs to do is let the whole front office run differently. That's a much bigger promise than demo-ing some software... and it's a wayyy harder one to keep. I joined because I watched these exact problems torture my mother when she worked for a huge medical system. The problem is hard, the market is enormous, and the people running these practices deserve AI that actually makes their lives better. Right now, AI is basically indecipherable to the modern medical practice. We're fixing that. We're also hiring across all roles in New York
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Brian Distelburger
I aspire to build another $1B+ tech company in NYC, and I'll be the first to tell you SF is a better place to build. NYC tech scene is thriving, right? Record funding. Thousands of startups. Talent everywhere. We're lying to ourselves. We have a handful of companies that went public, got really big and stayed here - MongoDB, Datadog, a few others. San Francisco has dozens and dozens. The problem isn't early stage. We're crushing early stage. Seed funding everywhere. Accelerators on every corner. Kids dropping out of school to start companies. The problem is what happens at scale. When you hit $10 million in revenue and need to hire a VP who's done this before. When you need engineers who've built systems that handle real traffic. When you need executives who've taken companies public. They're all in the Bay Area. Because that's where the last 50 companies that scaled were. So that's where all the people who know how to scale went. It's a vicious cycle. Companies can't scale here because the talent isn't here. The talent isn't here because the companies aren't here. Round and round we go. I took Yext public in New York. We got to about 2,000 people at our peak. You know how hard it was to find executives who'd managed 500+ person teams? Nearly impossible. We had to import almost all of them from the Bay area. That's part of why I started Windmill. If we can't have as many experienced managers, we need to make the managers we have more effective. We're abstracting out the management function so when it's time to scale, instead of hiring someone with "management experience" from the Bay, you can promote the domain expert on your team who is ready for more.
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