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Nabil Laoudji
What if we could integrate, play with, and control lab hardware as easily as we do software? That’s the vision behind PyLabRobot, an open-source, hardware-agnostic Python SDK building a universal interface for lab automation. I sat down with its creator, Rick Wierenga, to explore how the movement he’s leading is opening up a historically closed and expensive ecosystem – enabling more scientists, hackers, and startups to “ship science.” 🎧 Full episode in comments!
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Zach Rash
Today we’re launching our Physical AI Lab, led by UCLA professor and renowned researcher Bolei Zhou, who’s joining Coco as Chief AI Scientist. Bolei has spent his career advancing physical AI with 60,000+ citations and pioneering work. Now, he’s bringing that expertise to Coco. What makes this lab special is Coco’s fleet itself. Our robots run urban deliveries across the U.S. and Europe, navigating unpredictable environments (construction, pedestrians, weather, etc.). These are the kinds of real world challenges you can’t capture in simulation and that data is becoming the raw material for advancing physical AI. As we scale to 10,000+ robots in 2026, this fusion of research and deployment will define the next generation of physical AI and reimagine how goods move through cities everywhere. TechCrunch has the scoop - link in comments.
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John Gilhuly
Excited to have Curtis Northcutt, co-founder of Cleanlab, joining us at Arize AI Observe for a must-see talk: "Don’t Be the Next Headline: Closing the GenAI Reliability Gap" I've had the pleasure of working with Curtis and his team to stand up our Arize<>Cleanlab integration, which has been hugely helpful for end users looking to do deep evaluation of relevance and context adherence. From real-world failures to new solutions, Curtis will unpack: - Why GenAI systems fail in silent (and headline-making) ways - How to quantify and reduce hallucinations - Practical tools for building trustworthy AI, not just impressive demos If you're deploying agents in production, this is your toolkit. Cleanlab is also an Observe sponsor pioneering methods to make AI systems more reliable, interpretable, and safe. Come see us next week (‼️) at Observe: arize.com/observe-2025 #ArizeObserve
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Nathaniel Manning
While we haven't officially launched, can't help but be excited to see this great artcle in the @WSJ featuring LGND AI, Inc.(http://lgnd.io) and the opportunity for AI applied to earth observation data for all sorts of important uses. This is a huge and important data source that is vastly under represented in the current AI platform shift - and we aim to change that. Big stuff ahead! https://lnkd.in/ghd76mBB
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Arjun Bansal
My podcast episode with Art Dicker on The SoCal Edge is now live. 🎙️ We dove deep into my journey from neuroscience and brain-machine interfaces to startups and co-founding Log10, and how we're tackling one of AI's biggest challenges: making it trustworthy enough for high-stakes domains like healthcare and law. Some key points we explored: 🎯 Why accuracy isn't just important—it's everything when AI touches critical workflows ⚡ How Everest, our flagship product, is revolutionizing medical documentation with its focus on accuracy 🚀 The pivotal moments in AI history I've witnessed, from the AlexNet+GPU moment of 2012 to today's data-rich landscape 🛤️ My path from Qualcomm to co-founding Nervana Systems (acquired by Intel) to now building Log10 The future of AI isn't just about more powerful models—it's about AI systems that understand causality and can be trusted in enterprise applications where there's no room for error. For aspiring entrepreneurs: Whether you're pursuing deep research or identifying unmet needs in everyday workflows, the opportunities to build impactful AI solutions have never been greater. Thanks Art Dicker for the engaging conversation! Check out the full episode to hear more about what it takes to make AI truly enterprise-ready. Spotify: https://lnkd.in/dWHE-67f Apple podcasts: https://lnkd.in/dMDPe8_p #AI #MachineLearning #Healthcare #Innovation #Entrepreneurship #Log10
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🐯 Ajay Kulkarni
Waymo is a lie. But that’s good news for humanity. Living in SF, Waymo’s have become completely normal. My kids call them “robot cars” and can’t understand why I find them fascinating. To them, they just… are. They feel safer than human-driven cars. When I’m running and see a Waymo, I know it “sees” me with its 40 sensors. I’m not worried it’ll hit me. Very different from human drivers checking their phones. But here’s what most people don’t realize: Waymo still needs remote human operators: roughly one human per 5 cars. The long tail of driving scenarios is brutal. Cars handle >95% of situations perfectly. But that last <5% requires human judgment for complex edge cases. This mirrors what I wrote about yesterday: the “generation/verification” loop where AI generates, humans verify. There is a symbiotic relationship between AI and humans. AI handles the repetitive 95%. Humans handle the creative, complex, edge-case 5%. But here’s the uncomfortable math: 1 operator managing 5 cars means 4 traditional drivers could be displaced. This could suggest one of two things, depending on your point of view. Pessimistic view: 4 people lose their jobs. Optimistic view: In a world of AI abundance, new roles emerge for those 4 people. For example: remote human operators for new kinds of vehicles. I’m an optimist. But not a blind one. The displacement is already happening across industries. The real question is preparation, not prevention. In my own work, I’m seeing this pattern everywhere: AI automates the predictable, humans manage the exceptions and strategic decisions. The collaboration is real. We’re not becoming obsolete. We’re becoming force multipliers.
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