Engineering

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

Drone shows are increasingly incorporating AI technologies to enhance their performance. What do you think about this one? Here are several ways in which #AI is being utilized in drone shows: 1. Autonomous Navigation: Path Planning: AI algorithms assist drones in planning and optimizing flight paths for intricate aerial displays. Collision Avoidance: AI enables real-time analysis of the environment, helping drones avoid collisions and maintain safe distances. 2. Formation Flying: Coordination Algorithms: AI algorithms coordinate the movements of multiple drones to achieve precise formations. Real-Time Adjustments: Drones can dynamically adjust their positions in response to environmental factors or unexpected changes. 3. Swarm Intelligence: Collective Behavior: AI-driven swarm intelligence allows drones to exhibit collective behavior, creating synchronized and mesmerizing patterns. Adaptability: Drones in a swarm can adapt their behavior based on the actions of neighboring drones. 4. Real-Time Data Analysis: Environmental Sensors: Drones equipped with sensors provide real-time data on weather conditions, wind speed, and other factors. Adjusting Performances: AI analyzes this data to make real-time adjustments to the drone show, ensuring optimal performance. 5. Light and Color Choreography: Dynamic Lighting: AI algorithms control the lighting elements on drones, creating dynamic and customizable light shows. Color Synchronization: Drones can synchronize their colors and lighting patterns in real time for visually stunning effects. 6. AI-Generated Patterns: Generative Algorithms: AI is used to generate unique and artistic patterns for drone formations. Variability: Each show can be different, adding an element of surprise and creativity. 7. Gesture Recognition: Audience Interaction: AI-powered gesture recognition systems allow drones to respond to audience movements or gestures. Interactive Shows: Audience members can influence the show in real time. 8. Dynamic Choreography: Learning Algorithms: AI can learn from previous performances, adjusting choreography based on audience reactions and preferences. Continuous Improvement: Drones can adapt and improve their performances over time. 9. Logistics Optimization: Efficient Deployment: AI assists in optimizing the deployment and retrieval of drones before and after shows. Battery Management: Algorithms manage drone battery usage for extended performances. 10. Safety Measures: Emergency Protocols: AI can implement emergency protocols to ensure the safety of the drone show, such as automated landing in case of malfunctions. Monitoring Systems: AI monitors drones for any irregularities in flight behavior. 11. Sound Integration: Audio-Synchronized Displays: AI synchronizes drone movements with music or other audio elements for a fully immersive experience. #ai #innovation via @ zzmenx #drone #dronetechnology

The worst mistake employers make? Waiting for a resignation to offer a pay rise. By that point it's too late. The damage is already done. As uncomfortable as salary conversations can be (they shouldn't!). You need to advocate for yourself. Your employer won't give you a raise if you don't ask. Here's How to Have a Salary Conversations Like a Pro: 1️⃣ Set Clear Goals with Your Manager ↳ Define what success & progression looks like. ↳ Set KPI's that justify a pay rise later. 2️⃣ Have Regular Conversations About Growth ↳ Don’t wait for the annual review. Check in quarterly. ↳ Ask: “What can I do to be in the best position for a promotion?” Work on a plan together to upskill, get more responsibility & add more value. 3️⃣ Document Your Success ↳ Track wins, metrics & business impact. ↳ Use those numbers in your performance reviews. Instead of “I’ve worked hard” say: “I led [Project] which increased [Metric] by X% and saved Y hours.” 4️⃣ Promote Your Work (Without Bragging) ↳ Don’t assume people know what you've done. ↳ Present updates, share results, speak up in meetings. 5️⃣ Make the Ask (So It Feels Collaborative, Not Demanding) ↳ Timing matters. Make it an agreed time or in line with company reviews. Try: “Based on my contributions in [Project], I’d love to discuss salary progression. What would it take for me to reach [target salary]?” 6️⃣ Leverage the Market (If Necessary) ↳ If nothing is happening internally, go outside. ↳ Get an offer on the table to give you leverage. If your company won’t pay you what you deserve, another one will. Retention is cheaper than recruitment. ♻️ Repost to help people advocate for themselves. 👋🏼 Follow Dan Mian for more career insights.

The old approach of sending resumes and hoping for the best isn't working anymore. Thousands of talented engineers are competing for fewer positions. In this market, being skilled isn't enough. You need to be visible. The engineers who are landing roles fast aren't necessarily the most qualified. They're the ones who know how to promote themselves and stand out from the crowd. That's why I created this 5-𝘀𝘁𝗲𝗽 𝗮𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗿𝗶𝘀𝗲 𝗮𝗯𝗼𝘃𝗲 𝘁𝗵𝗲 𝗻𝗼𝗶𝘀𝗲: 📍 Step 1: Optimize Your LinkedIn Profile ↳ Your headline should immediately showcase your specific expertise. ↳ Quantify your achievements. ↳ Make yourself discoverable when recruiters search. 📍 Step 2: Build a Killer GitHub Portfolio ↳ Create 3-4 production-grade projects with detailed READMEs. ↳ Show your thinking process. ↳ Prove your skills instead of just listing them. 📍 Step 3: Write Technical Content Document what you learn. ↳ Share project walkthroughs. ↳ Write about common mistakes. 📍 Step 4: Share Strategically Post your insights with context. ↳ Explain why topics matter. ↳ Document your learning journey consistently. 📍 Step 5: Grow Your Network ↳ Connect with recruiters proactively. ↳ Engage meaningfully with posts daily. ↳ Build relationships before you need them. The result: Instead of competing with hundreds of identical resumes, you become the engineer they already know and want to hire. This system works because it positions you as a known solution, not an unknown candidate. 📌 Want the complete breakdown with actionable tips? Download the full guide here: https://bit.ly/4mZk17A I really hope this is useful. Share this with someone in your network who could benefit from these strategies. 💬 What's the biggest challenge you're facing in this competitive market?

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.

Grid bottlenecks are a feature — not a bug — of the energy transition. For years, we viewed economics as the main hurdle to scaling clean energy. High costs for wind, solar, heat pumps, and storage dominated the conversation. But the world has changed. Thanks to extraordinary innovation and dramatic cost reductions in renewables and electrification technologies, the bottlenecks we face today are different. They’re no longer about whether clean energy is affordable — it is. Instead, the challenge is whether our energy systems can evolve quickly enough to integrate it. A recent Financial Times piece highlights this clearly: across Europe, the rapid build-out of renewable generation now outpaces the ability of grids to move electricity to where it’s needed. Curtailment, congestion, and long queues for grid connections already cost billions annually — and without decisive action, these costs will grow. This isn’t a sign of failure. It’s a sign of success. It means the transition is happening faster than the infrastructure built for the fossil era can handle. The rise of decentralised, variable renewables and electrified heating and transport requires a fundamentally different approach to planning — one that anticipates growth rather than reacts to it. The EU’s move toward more coordinated, top-down scenario building and cross-border grid planning recognises exactly this. Better alignment between countries and system operators, faster permitting, and prioritisation of critical projects are essential steps to unlock the full value of cheap clean energy. Because every euro lost to bottlenecks is not a cost of climate action — it’s a cost of not modernising our grids fast enough. The more successful we are in deploying renewables and electrification, the more urgently we must upgrade and expand our grids. Grid constraints are not a reason to slow down. They’re a reason to speed up the transformation of an energy system that was never designed for the technologies now powering our transition.

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. [Truncated for length; full text: https://lnkd.in/g23TjShf ]

How much do laypersons around the world know about IP? If they know about it, do they have a positive or negative perception of it? And are these changing over time? To answer these important questions which cut right to the heart of popular views and support for IP, we launched WIPO Pulse two years ago – the first ever global survey on IP, covering 50 countries. Now we’ve launched the second edition – this time covering 35,500 laypersons from 74 countries in all regions of the world. The results are interesting and insightful. First, the world is getting savvier about IP. Awareness has grown across all main IP rights since 2023. Copyright and trademarks still lead the pack (no big surprise – music, art, entertainment are fundamental to our lives), but with patents and designs continuing to trail a bit when it comes to public understanding. Second, confidence in the positive impact of IP on the economy remains strong, with two-thirds of respondents (64%) agreeing that IP benefits the economy. Here is where there is a twist – just like in 2023, Asia, Africa and Latin America remain the regions with the most positive perception about IP’s economic benefits, with lower positive perceptions in Western Europe and North America. I welcome your views on this. Third, we were interested in understanding perception among women and youth. Here, we see some gains in awareness among both groups. In Asia-Pacific, awareness rose across all five IP rights for both groups. Western Europe also saw broad gains well. However, youth awareness dipped slightly in Latin America and Eastern Europe. The data we collected is really a wealth of insights that is begging for further investigation. They are valuable not just for WIPO, but the global IP community and local IP institutions, and we will use it to sharpen global, regional and local awareness building, outreach and engagement efforts, as well as combine it with other datasets like the Global Innovation Index to build a deeper picture of the global IP landscape. More: https://lnkd.in/eZ96P-ZJ Photos: WIPO/Berrod #WIPO #IntellectualProperty #Trademark #Patent #Design #Copyright #GeographicalIndications

April 6th: A bright spring day in Germany, one that perfectly illustrates the need for battery storage systems. Like so many other sunny days, PV generation in Germany covered a large portion of the electricity demand for several hours in the middle of the day, thanks to the cloudless sky and millions of solar modules. But there is a darker side to the sunshine. Large amounts of daytime solar can overload the grid and cause severe electricity price fluctuations: on April 6th, intraday electricity prices dropped to -200€/MWh at their lowest point. In cases where more electricity is generated from solar energy than the grid can handle, grid operators regularly require solar installations to curtail their production. This means that energy that could otherwise be made available to consumers cannot be used. And when the sun goes down, most of the demand must quickly be met with flexible sources. This adds an extra layer of complexity: deciding which conventional power plants can be shut down during the day and switched on again in the evening is a careful balancing act. This is precisely the situation where battery energy storage systems (BESS) can bridge the gap, with several advantages: - By storing part of the solar energy at peak generation times and dispatching it later, BESS can help shift the curve to more closely align with evening demand. - Better management of volatile generation from renewables also helps keep prices stable. - Provided they are close to the overproducing solar systems, BESS contribute to grid stability by helping balance supply and demand. Of course, there is no one-size-fits-all technology. A secure and flexible energy system needs a diverse mix. But batteries are playing an increasing role, especially as they become more and more affordable. We at RWE are harnessing the benefits: we have 1.2 GW of installed BESS capacity worldwide, of which nine systems totalling 364 MW of capacity operate in Germany alone. We’re scaling fast, with new large-scale projects recently commissioned in Germany and the Netherlands. And we have just decided to build a BESS facility in Hamm with an installed capacity of 600 megawatts. So, let’s continue to make the most of those sunny days — by creating the right framework conditions to build up affordable and flexible support.

When I started leading a high-powered recruiting team, I had the traits of the TYRANT leaders I now call out. Here's why: Despite my degrees, certificates, and ongoing professional development, nothing prepared me to transition into leading. I still had an individual contributor (IC) mindset, which unintentionally led me to compete with my very capable team. At the time, I engaged in behaviors like: Taking over projects instead of developing my team. Working long hours, thinking it showed commitment. Making unilateral decisions vs collaborating. Giving orders instead of providing clarity and context. Hoarding information instead of communicating transparently. Prioritizing my metrics over team goals. A month in, my boss at the time sat down with me and told me to own my transition and to stop taking over work when someone asked for help. (she's one of the best Leader's I've ever had) To transform my mindset, I sought out a few internal sponsors and observed how they managed their teams. I also asked my team for feedback on where I could do better. Once I made the changes: mindset and action, I began demonstrating new leadership behaviors: Coaching my team and developing their problem-solving skills. ↳Created an authorization matrix to empower them to make decisions. Promoting work-life balance through prioritization and delegation. ↳I stopped working on vacation to set a better example. Making collaborative decisions to increase buy-in. ↳They worked on the reqs, so I asked for their ideas and where I could implement them. Painting a vision and equipping the team to get there themselves. ↳I translated the organization's vision down to how it affected our team goals. Openly communicating to build trust and transparency. ↳I promoted democratic decision-making and explained when it needed to be autocratic. Aligning on and championing team goals over my individual metrics. ↳I held weekly reviews where I celebrated their success because it was OUR success. Here's what I want you to take from this: 1. Develop your team's skills rather than trying to be the expert. 2. Delegate decisions to increase buy-in and leverage diverse perspectives. 3. Openly share information rather than hoarding knowledge and insight. 4. Recognize and elevate your team's contributions rather than taking individual credit. #aLITTLEadvice #leadership