HelloFresh’s Post

Bernt Børnich, Founder and CEO of 1X, took the MACHINA stage today with the timeline and the discipline behind bringing humanoids into the home. On what safety actually determines: "Safety defines where you can go. Like, how useful can you be? Where can you apply your useful labor? And the dual of that is, of course, where can you gather data. How much data diversity can you have." That framing sits at the centre of 1X's whole approach. Safety is not a compliance checkbox at the end of the roadmap. It is the constraint that decides how much of the world a humanoid can operate in, how useful it can be inside that world, and how much real-world data it can gather to keep getting better. Every deployment expands the surface. Every restriction shrinks the flywheel. On when this happens: "It's extremely hard to predict where we will have this kind of, like, abundance of labor happening. In the next two years, three years, or next decade. But I'm pretty confident it's not decades." On the consumer product: "Hopefully here order a robot that we are shipping to consumers this year." His approach: "We're gonna scale it as fast as we absolutely can, but we want to do it in a manner that is safe and that is really responsible. So it's gonna be slow in the beginning, but there's gonna be a few who gets the robot this year." On the ten years of engineering underneath it: "We've been grinding on this problem for a decade. All the way from, like, new discoveries in material science, to how to weave these things, to how to process, how to post-process, how to design around." MACHINA Summit, Europe's Leading Physical AI Event. Live today in Paris at Station F. #MACHINA2026 #PhysicalAI #Humanoid

Why couldn’t the robot cross the road? I watched a food delivery robot waiting at a zebra crossing this week. Passing cars carried on as normal so the robot just waited as it couldn’t cross the road. I was driving on the opposite side of the road but had I been approaching the crossing, I would have stopped. It made me realise that the robot wasn’t the problem. The challenge was that a new participant had entered a system designed for people. Everyone simply carried on with the rules they understood. The technology had arrived.The infrastructure hadn’t. Then continue exactly as we had it: It made me wonder whether healthcare is facing the same challenge. We’re investing heavily in AI. We’re building remarkable new technologies. We’re creating more digital tools than ever before. But innovation doesn’t automatically create integration. Technology creates new participants. Infrastructure enables participation. Without trusted infrastructure, clinicians, industry and increasingly AI all struggle to collaborate effectively, not because the technology isn’t capable, but because the conditions for trusted participation haven’t been deliberately designed. Perhaps the future of healthcare won’t be determined by who builds the smartest AI. Perhaps it will be determined by who builds the infrastructure that allows people and technology to work together. I’d be interested to hear how others see this. Where have you seen technology outpace the infrastructure needed to integrate it? #Healthtech #AI #HealthcareInnovation #DigitalInfrastructure #THRESIO #GoverenedHealthcareInteraction

Most AI rollouts don't fail on the tech. They fail on the team. Council member Matt Ezyk is bringing that lens to the opening keynote at eTail™ Boston: on building cultures where teams know when to trust an AI output, and when to push back on it. We caught up with him ahead of his session to hear his perspective, and what you'll leave his panel rethinking on 8/10. 📰 Read the post on our blog → https://lnkd.in/gFHWPFb4 🎟️ Then catch Matt in person at the Retail Insiders Party on 8/11. Party details (and 20% off conference passes) all in the blog post. #etail #etailboston #retailai #retailleaders #retailcommunity

Just wrapped up NESTGEN 🎓 Nestlé Global E-Learning Festival A deep dive across five critical business functions: Digital Marketing 📱, AI 🤖, Automation & Robotics ⚙️, Supply Chain 🔗, and Finance 💰 Automation is reshaping production lines. AI is quietly driving decisions in supply chain and finance that used to be pure judgment calls. Digital marketing and data are now part of every function's toolkit. Grateful to Nestlé for building a program that pushes professionals to think beyond their core function. #NESTGEN #ContinuousLearning #AI #SupplyChain #DigitalMarketing #LearningFestival #Nestle

I'm especially looking forward to this session 🥳 Working with Denis, I've seen firsthand how he approaches engineering problems with curiosity, pragmatism, and a strong focus on building solutions that work in the real world. "Your Agent Doesn't Need a Bigger Model" challenges a common assumption in AI: that bigger is always better. If you're interested in the engineering trade-offs behind building practical AI products, this is a conversation worth being in the room for. Looking forward to this one at the African Ruby Community

As the countdown continues… and we're getting closer to our keynote announcements. 👀 Meet Denis Sellu. Denis has been building software for about 15 years. After studying robotics, he became a software engineer and is now the Founding Lead at Remi AI, helping teams and businesses adopt AI. Based in València, he's particularly interested in how AI is changing the way we work. Talk Title: Your Agent Doesn't Need a Bigger Model Most teams can't afford to run frontier models in production; the economics simply don't work. In this session, Denis explores how orchestrating small, specialised models can deliver results that stand up against much larger ones. The ideas are grounded in published research and proven through building Remi, an AI workspace for teams, in plain Ruby on Rails. If you're interested in what it actually takes to build AI products that are practical, scalable, and affordable, this is a session you won't want to miss. 🎟️ Get your ticket today. And stay tuned… our keynote speakers are next.

At Interrupt, Ravi Chandu Ummadisetti and Kordel France, PhD from Toyota North America's enterprise AI team took us behind the scenes of ToyotaGPT, a LangGraph-powered platform that reduced agent delivery from six months to four days, with over 50 agents now in production. https://lnkd.in/gqUDJmuV

LangChain recently shared a clip from Toyota North America presentation at the 2026 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗜𝗻𝘁𝗲𝗿𝗿𝘂𝗽𝘁 conference, s𝗵𝗼𝘄𝗰𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸 𝗼𝗳 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝘁𝗲𝗮𝗺, 𝗼𝗳 𝘄𝗵𝗶𝗰𝗵 𝗜 𝘄𝗮𝘀 𝗳𝗼𝗿𝘁𝘂𝗻𝗮𝘁𝗲 𝘁𝗼 𝗯𝗲 𝗮 𝗽𝗮𝗿𝘁 🙂 . 🎥 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝟭𝟲-𝗺𝗶𝗻𝘂𝘁𝗲 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 [https://lnkd.in/g6dc9qqz] to learn how these GenAI products are creating real-world impact across Toyota’s manufacturing and enterprise operations in North America: I’m grateful to Ravi Chandu Ummadisetti, Kordel France, PhD, Eric Swei, Melody Ayeli, and the broader 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽, engineering, and business teams for the opportunity to collaborate on this journey and help transform ambitious AI ideas into production-ready solutions. 𝗗𝘂𝗿𝗶𝗻𝗴 𝗺𝘆 𝘁𝗶𝗺𝗲 𝘄𝗶𝘁𝗵 𝗧𝗼𝘆𝗼𝘁𝗮 𝗠𝗼𝘁𝗼𝗿 𝗡𝗼𝗿𝘁𝗵 𝗔𝗺𝗲𝗿𝗶𝗰𝗮’𝘀 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝘁𝗲𝗮𝗺, 𝗜 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗮𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗲𝗮𝗺 𝘁𝗼 𝘁𝗵𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗮 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘂𝘀𝗲𝗿 𝗯𝗮𝘀𝗲 𝗼𝗳 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟲𝟱,𝟬𝟬𝟬 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻. 𝗪𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗮 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟱𝟬 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗿𝗲𝘂𝘀𝗲 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀. I contributed to initiatives including ToyotaGPT, MaterialGPT, GearPal, Catiabot, Batterybrain and other AI applications addressing real-world challenges across manufacturing, engineering, research, enterprise knowledge, and vehicle information. 𝗞𝗲𝘆 𝗮𝗿𝗲𝗮𝘀 𝗜 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘁𝗼: • Designing scalable GenAI and agentic architectures with LangChain, LangGraph, and LangSmith • Building reusable RAG patterns and configuration-driven workflows • Integrating complex enterprise data sources and ingestion pipelines • Strengthening security, observability, and production reliability • Moving GenAI solutions from prototype to production Toyota’s enterprise knowledge spans decades of manufacturing and research information across PDFs, spreadsheets, scanned manuals, engineering diagrams, CAD-related files, multilingual content, nested tables, and images. This experience continues to shape how I approach AI engineering—not as a collection of isolated prototypes, but as scalable, secure, observable, and reusable production systems that deliver measurable business value. #GenerativeAI #AgenticAI #EnterpriseAI #LangChain #LangGraph #LangSmith #RAG #AIEngineering #ToyotaMotorNorthAmerica #DFW #Texas #Sanfrancisco #California

Last week at TRANS4LOG LAB's 2026 Congress, Anika Riley, our Lead Product Manager, shared how AI is shifting logistics away from information overload towards operational control. Key takeaways from her talk: ‣ Transparency is a great starting point. The gap between seeing a problem and resolving it is where logistics teams still spend most of their time. ‣ Operational teams manage high volumes of unstructured email threads and manual booking requests, and AI helps them by quickly capturing context to enable faster decisions. ‣ With Flash by Forto, routine tasks get handled automatically, freeing the team to focus on the decisions that need their attention. Thanks to everyone who joined the talk!

We had the pleasure of visiting Take2 and discussing how AI is shaping the tech industry and jobs it creates. ClearPoint CEO Bain Hollister and Talent Resource Partner Benigna Mezofi shared how AI can massively increase an individual’s productivity and allow them to focus on problem-solving and innovation. Thank you Take2 for allowing us to share our advice with your students.