Engineering

Japan’s bullet trains had a problem big enough to threaten the future of high-speed rail. At 200 mph, tunnels turned them into sonic bombs. Noise complaints grew. Communities suffered. Speed restrictions became a real risk. What stands out to me is this: The solution did not come from more force. It came from a bird. Engineer Eiji Nakatsu studied the kingfisher, which moves from air into water with barely a splash, and used that insight to redesign the Shinkansen’s nose. The result was remarkable: ↳ sonic boom dramatically reduced ↳ trains became about 10% faster ↳ electricity use dropped by around 15% But this was never just about noise. This is the deeper impact: ↳ 15% less energy has been framed as 200,000 fewer tons of CO2 annually ↳ 10% faster speeds can mean more people living outside expensive cities while still commuting ↳ quieter tunnels can mean families near the tracks finally sleeping through the night That is what makes this story bigger than engineering. One bird’s beak did not just improve a train. It reshaped how an entire system could perform, with less friction for people and the environment. I see a much bigger lesson here. The best innovation does not always come from adding more power, more cost, or more complexity. Sometimes it comes from observing better. Nature has already solved for speed, efficiency, resilience, and adaptation. The real question is whether we are humble enough to learn from it. Because the future will not belong only to those who build more powerful systems. It will belong to those who build systems that work better with reality. What system in your industry is still being forced forward when it should be fundamentally redesigned? #Innovation #Biomimicry #Engineering #Leadership #Technology #Transportation #Sustainability #AI #FutureOfWork #PascalBornet

Roadmap to Learn Agentic AI This roadmap breaks down the journey into 12 focused stages: – Grasp the core differences between traditional AI and autonomous agents – Build a solid foundation in ML, LLMs, and frameworks like LangGraph, CrewAI, and AutoGen – Understand how agents use memory, plan actions, and collaborate – Learn to implement retrieval-augmented generation (RAG) and adaptive reinforcement learning – Deploy agents in real-world scenarios with performance monitoring and continuous improvement If you're building AI that goes beyond chat interfaces, this roadmap will help you architect systems that are capable, contextual, and action-oriented. Feel free to save or share if you find it valuable.

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

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

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?

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 to compare your eng team's velocity to industry benchmarks (and increase it): Step 1: Send your eng team this 4-question survey to get a baseline on key metrics: https://lnkd.in/gQGfApx4 You can use any surveying tool to do this—Google Forms, Microsoft Forms, Typeform, etc.—just make sure you can view the responses in a spreadsheet in order to calculate averages. Important: responses must be anonymous to preserve trust, and this survey is designed for people who write code as part of their job. Step 2: Calculate your how you're doing. - For Speed, Quality, and Impact, find the average value for each question’s responses. - For Effectiveness, calculate the percent of favorable responses (also called a Top 2 Box score) across all Effectiveness responses. See the example in the template above. Step 3: Track velocity improvements over time. Once you’ve got a baseline, you can start to regularly re-run this survey to track your progress. Use a quarterly cadence to begin with. Benchmarking data, both internal and external, will help contextualize your results. Remember, speed is only relative to your competition. Below are external benchmarks for the key metrics. You can also download full benchmarking data, including segments on company size, sector, and even benchmarks for mobile engineers here: https://lnkd.in/gBJzCdTg Look at 75th percentile values for comparison initially. Being a top-quartile performer is a solid goal for any development team. Step 4: Decide which area to improve first. Look at your data and using benchmarking data as a reference point, pick which metric you believe will make the biggest impact on velocity. To make this decision about what to work on to improve product velocity, drill down to the data on a team level, and also look at qualitative data from the engineers themselves. Step 5: Link efficiency improvements to core business impact metrics Instead of presenting these CI and release improvement projects as “tech debt repayment” or “workflow improvements” without clear goals and outcomes, you can directly link efficiency projects back to core business impact metrics. Ongoing research (https://lnkd.in/grHQNtSA) continues to show a correlation between developer experience and efficiency, looking at data from 40,000 developers across 800 organizations. Improving the Effectiveness score (DXI) by one point translates to saving 13 minutes per week per developer, equivalent to 10 hours annually. With this org’s 150 engineers, improving the score by one point results in about 33 hours saved per week. For so much more, don't miss the full post: https://lnkd.in/grrpfwrK

China has switched on the world’s first grid-connected 20 MW offshore wind turbine – the largest wind turbine currently operating anywhere in the world. Installed around 30 km offshore in China’s Fujian province, the turbine has a rotor diameter of 300 metres, nearly the height of the Eiffel Tower. Wind turbines have been getting steadily bigger for decades – driven by physics and economics: ✅ Power from wind scales with the square of the rotor diameter. ✅ Power also scales with the cube of wind speed, and taller turbines can access the stronger, steadier winds higher above the surface. ✅ Costs such as foundations and cables increase as turbines get larger, but energy production tends to grow faster than these costs. Offshore wind farms in particular benefit from scale because installation vessels are extremely expensive to operate. Reducing the total number of turbines - foundations, lifts and cable connections - can materially lower overall project costs. Larger turbines do introduce challenges, including more complex manufacturing and greater single-asset risk. But the economic advantages of larger turbines in offshore projects continue to outweigh these challenges, which is why turbine sizes keep increasing. Even larger 25–26 MW turbines are already under development – all from Chinese manufacturers. With the world’s largest domestic deployment pipeline and an integrated manufacturing ecosystem, China is increasingly setting the pace in the next generation of offshore wind turbines.

For the last part of my Energy Resilience series, we have to talk about the worst-case scenario – when the lights actually go out. Earlier this year we saw that happen in Spain and Portugal. A major blackout left millions without power. Trains stopped, shops couldn’t take card payments, hospitals and factories switched to backup. A wake-up call that modern life depends on electricity in ways we often forget until it is gone.   This is what happens when grids are pushed to the edge by fast-moving disturbances or extreme conditions. A couple of years ago, South Australia experienced a state-wide blackout after severe weather took out multiple transmission lines. Investigations showed the system lacked enough inertia to stay stable through the shock. Part of the solution was to install synchronous condensers – giant flywheels that give the grid “weight” and stability. Siemens Energy delivered two of them as part of the response. Not the only measure of course – adapting regulation is also essential – but it showed something important: without resilience in the system, recovery is slow and uncertain. So what do we actually need if we want a fast ramp-up after a major incident? From my perspective, it comes down to three things. 1️⃣ Standardize before the crisis: When parts fail, every minute spent interpreting drawings or debating specifications is a minute the lights stay out. Standard equipment and uniform processes mean teams can move quickly because they are working with tools they already know. Recovery begins long before the fault happens. 2️⃣ Design power plants with failure in mind: A fast restart depends on assets built to recover quickly, not just run efficiently. That means black-start capability, smart redundancy where it matters and systems that can restart without waiting for the wider grid. In the U.S. for example we supported a power plant with a battery system that enables multiple restart attempts within one hour – resilience designed into the plant itself. 3️⃣ No improvisation in the dark: A blackout is the worst moment to negotiate who does what. Good restoration plans spell out which assets come back first, how to stabilize small sections of the grid and when to reconnect them safely. Regular drills with operators, authorities and major customers turn these plans into routine rather than theory. These steps matter because in any major incident skilled people are often the scarcest resource – grid operators, field crews and technical specialists. That is why preparation matters so much. Clear roles, common standards and trusted partnerships mean limited teams can do more in less time. Because when the worst happens what people remember is how long it stayed dark. I hope you have found this mini-series useful. I know social media is often about speed and short takes but sometimes – especially on important topics like this – I find it worthwhile digging into the detail together.✍️ I’d be interested to hear if you agree.

The biggest myth in AI today? That tools like LLMs, CoPilots, MCPs, and Agents will do the engineering for you. They won’t — because AI is engineering. LLMs. MCP. Agents. They’re all just that — tools. Yet many organizations are spending an extraordinary amount of time comparing, evaluating, and switching between tools — while missing the real essence of AI transformation. The real differentiator isn’t the toolchain. It’s the engineering mindset behind how those tools are used. Most organizations miss that AI is an engineering discipline — not a collection of experiments. It demands the same rigor as any mature system: design, development, testing, validation, rollout, and continuous optimization. Don’t go by leaderboards — they’re tested to work in controlled benchmarks, not in real-world, multi-system environments where context, latency, data, and cost all collide. And don’t fall for the misconception that AI will replace engineers. That’s a narrative being set — but having worked with top LLMs and chatbots, one thing is clear: they often fail when confronted with real engineering. Their code lacks depth, structure, and holistic system thinking. Tools never replace real engineering. They amplify those who understand it. Invest in the core. Invest in robust engineering practices. Upskill your teams. This will be your foundation in building scalable, responsible, and future-ready AI systems. Because tools will change. Frameworks will evolve. But engineering excellence — that’s what endures #aiengineering #ai #leanagenticai