Working at startups is never easy. Doing it as an international hire, half a world from home, is a different game entirely. I kept making that bet anyway at lean, venture-backed teams, one of which became a unicorn...and every time I noticed the same arc. The high of building "the next cool thing," and once it wore off a feeling I couldn't name. For years I assumed that's just what startups felt like. Last September, around 2am, I was doom scrolling LinkedIn when I saw posts from @businessbarista , @ArmanHezarkhani , and @beanlawler_ about @tenex_labs . All three within five minutes. The company was only a few months old. I had almost no information about the problems I'd get to work on. And for a foreign worker, an early-stage startup isn't one bet, it's three: on yourself, on the team, and on a visa clock that doesn't care about either. What made me apply anyway was the vision: building a company that lasts in a post-AGI world, with a business model to back it up. Then came the conversations with Arman, Alex and the rest of the team at the time(basically @seejayhess and @dan_zakon ). I wont ever forget this voice in my head saying "this is it." when I walked back home from the final round. The intellect, the caliber, the desire to push the vision through...It made my blood rush and fortunately, I got a chance to join as the third engineer and the first international hire. Ten months in, after many learnings, complex challenges, and a lot of memories, I can finally name the feeling I could never shake at every startup before this one. It was the lack of a home away from home. That's what Tenex turned out to be. People I genuinely love, respect, and look up to. A team this nerdy, rigorous, fast-paced, and fun at the same time is rare. I didn't just join another startup. I found my tribe. If you want to find yours while working at the frontier of AI, we're hiring!

Billions of people use AI every day. Almost none of us actually know what's happening underneath. And I think that quietly gets to people. You lean on something this powerful, it gets smarter every month, and there's this low hum in the back of your head..."I don't really understand how any of this works" It's easy to feel behind. Sometimes a little scared of it. I've spent a while buried in the math behind these systems, and the thing I keep coming back to is this: the ideas underneath are simpler than the people explaining them make them sound. The jargon is the hard part. Not the machine. So I'm going to start writing the version I wish I'd had, taking one concept at a time and pulling it apart from the ground up, until the gears actually make sense. No hand-waving, no equations dropped on you out of nowhere. Some of what we'll touch: • How LLMs actually work, and the history that led there • How machines see and generate images • Reinforcement learning, in plain words • The architectures behind the models you already use • The stranger frontiers, like physics-based and quantum AI If you've ever used AI and felt that gap, this is for you. First one's coming soon.