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

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

We’re planting trees — but losing biodiversity. Global efforts to restore forests are gathering pace, driven by promises of combating climate change, conserving biodiversity, and improving livelihoods. Yet a recent paper published in Nature Reviews Biodiversity warns that the biodiversity gains from these initiatives are often overstated — and sometimes absent altogether. Forest restoration is at the heart of Target 2 of the Kunming-Montreal Global Biodiversity Framework, which aims to place 30% of degraded ecosystems under effective restoration by 2030. But the gap between ambition and outcome is wide. "Biodiversity will remain a vague buzzword rather than an actual outcome" unless projects explicitly prioritize it, the authors caution. Restoration has typically prioritized utilitarian goals such as timber production, carbon sequestration, or erosion control. This bias is reflected in the widespread use of monoculture plantations or low-diversity agroforests. Nearly half of the Bonn Challenge’s forest commitments consist of commercial plantations of exotic species — a trend that risks undermining biodiversity rather than enhancing it. Scientific evidence shows that restoring biodiversity requires more than planting trees. Methods like natural regeneration — allowing forests to recover on their own — can often yield superior biodiversity outcomes, though they face social and economic barriers. By contrast, planting a few fast-growing species may sequester carbon quickly but offers little for threatened plants and animals. Biodiversity recovery is influenced by many factors: the intensity of prior land use, the surrounding landscape, and the species chosen for restoration. Recovery is slow — often measured in decades — and tends to lag for rare and specialist species. Alarmingly, most projects stop monitoring after just a few years, long before ecosystems stabilize. However, the authors say there are reasons for optimism. Biodiversity markets, including emerging biodiversity credit schemes and carbon credits with biodiversity safeguards, could mobilize new financing. Meanwhile, technologies like environmental DNA sampling, bioacoustics, and remote sensing promise to improve monitoring at scale. To turn good intentions into reality, the paper argues, projects must define explicit biodiversity goals, select suitable methods, and commit to long-term monitoring. Social equity must also be central. "Improving biodiversity outcomes of forest restoration… could contribute to mitigating power asymmetries and inequalities," the authors write, citing examples from Madagascar and Brazil. If designed well, forest restoration could help address the twin crises of biodiversity loss and climate change. But without a deliberate shift, billions of dollars risk being spent on projects that plant trees — and little else. 🔬 Brancalion et al (2025): https://lnkd.in/gG6X36WP

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

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

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Demystifying the Software Testing 1️⃣ 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: Unit Testing: Isolating individual code units to ensure they work as expected. Think of it as testing each brick before building a wall. Integration Testing: Verifying how different modules work together. Imagine testing how the bricks fit into the wall. System Testing: Putting it all together, ensuring the entire system functions as designed. Now, test the whole building for stability and functionality. Acceptance Testing: The final hurdle! Here, users or stakeholders confirm the software meets their needs. Think of it as the grand opening ceremony for your building. 2️⃣ 𝗡𝗼𝗻-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: ️ Performance Testing: Assessing speed, responsiveness, and scalability under different loads. Imagine testing how many people your building can safely accommodate. Security Testing: Identifying and mitigating vulnerabilities to protect against cyberattacks. Think of it as installing security systems and testing their effectiveness. Usability Testing: Evaluating how easy and intuitive the software is to use. Imagine testing how user-friendly your building is for navigation and accessibility. 3️⃣ 𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝘃𝗲𝗻𝘂𝗲𝘀: 𝗧𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗿𝗲𝘄: Regression Testing: Ensuring new changes haven't broken existing functionality. Imagine checking your building for cracks after renovations. Smoke Testing: A quick sanity check to ensure basic functionality before further testing. Think of turning on the lights and checking for basic systems functionality before a deeper inspection. Exploratory Testing: Unstructured, creative testing to uncover unexpected issues. Imagine a detective searching for hidden clues in your building. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

#Diversity in high-tech fields remains critically low. The Equal Employment Opportunity Commission (EEOC) recently reported that #Black and #Latino professionals are underrepresented in high-tech roles, especially in leadership. These numbers highlight ongoing structural barriers in hiring, promotion and retention. This gap is a missed opportunity to tap into a wealth of diverse talent and perspectives essential to the future of tech. However, addressing and thoroughly fixing these challenges will require time, consistent effort and a long-term commitment to systemic change. Companies can support the progression of representation in tech by investing in training, mentorship and internship opportunities that open doors for people who were historically shut out. Programs like internXL, a platform that is committed to increasing diversity and inclusion in the internship hiring process for top companies, are making a significant impact. Similarly, the expansion of STEM education at institutions like Cornell University is helping to connect talented young people from underrepresented communities with opportunities for high-tech careers. When we work together to remove these barriers, we’re fostering a more inclusive workforce and strengthening innovation, problem-solving and leadership in the industry. Let’s build a tech future that reflects the diversity of our society. https://bit.ly/3UNtOCh

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.

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.