Where is the openclaw community hah

Wrapping Boston & NYC tech week. A week deep in "physical AI" rooms. Here's what I'm taking home: The field is at an inflection point. Robots are quietly reshaping every category; logistics, agriculture, medical, food processing, defense, autonomous vehicles. And prototyping has never been easier: cheap sensors, off-the-shelf models, accessible hardware. But almost nobody talks about the part that actually decides who wins: reliability. The gap between a working prototype and a production system you can trust is enormous, and very few have crossed it. @IlirAliu_ put it well in the "From Prototype to Production" panel - it's the unsolved problem hiding under all the demos. A few things that stuck with me: - The data paradox sits at the center of the whole field: there's simultaneously too little diverse data to train general-purpose models, and too much raw operational data for teams to actually process. Solving both sides is foundational. - Real-world data is irreplaceable. Simulation helps, but the reality gap is still large. The black swans only show up in real operation. - Reactive vs proactive is the maturity divide. If you only open the logs after something breaks, you're already behind. The teams that scale monitor everything, continuously. - Data infrastructure is a strategic asset, not an afterthought. Invest early and it compounds, a real flywheel. Delay it and you pay later in reliability, trust, and an inability to scale. - The messy open question: data ownership. As physical AI moves into regulated domains, who owns the sensitive video and operational data: OEM, operator, or customer. The demos are easy now. The grind is everything after. That's the whole game>>>

I just wrapped ~200 robotics peer conversations in SF. Now I’m bringing that same energy to Boston for #RoboticsSummit this week. Who should I meet to make Boston as productive? Hardware hackers, humanoid teams, automation founders, researchers 🤖 Hit me up!!

We've been heads down in the deployment layer for a while now, working through the exact friction points between hardware and software that most teams hit when trying to get robots into real environments. If you're running into those walls, we'd love to chat and help you work through them.

Nothing beats the view when working from a train in Switzerland 🇨🇭

Visited @DominiqueCAPaul in his new office near ETH. Really loved the energy and the vision behind what he’s building for his startup. Also genuinely love the idea of making the space a place where robotics founders/builders can swing by, work, exchange ideas, and be around others building in the same space :) More of this in robotics!!

Im in Zurich for the week and looking to connect with robotics founders/builders. If you’re working on something interesting in robotics, autonomy, or embodied AI, let’s meet!!

We honestly need much more takes like this so we can understand from firsthand experience and not from assumptions or mappings to fundamentally different domains

Dropping by corgi cafe for few hours, let’s talk if you are in the hardware space !

First time I met moe he sent all of the event attendees fishing emails to pitch his idea, highly recommend him if you are mindful about your organizations security

Takes from todays Physical AI Panel : - The framing of "scaling laws in robotics" isn't fundamentally correct - Environment generalization? More solved than people think (👀) - Huge gap: cool demos ≠ products people actually pay for - The future in this space is looking less like big players and more like many niche players in each part of the stack - Adoption won’t look like ChatGPT, hardware scales slowly - Despite the hype, we’re very early. and the network effects aren’t even clear yet. @GM @GeneralistAI @Mind_Robotics @BedrockRobotics @foxglove

Had a lot of great conversations with robotics builders last week. Now looking to spend more time in labs, if you’re building something interesting, I’d love to come see it :)