Engineering

The "miracle of Paris" has become a global reference for air quality management 👏 The maps show the air quality for 2007, 2012, 2017 and 2023, based on average annual levels of nitrogen dioxide pollution (in µg/m3). Levels of NO2 in the air have dropped by over 40% in 10 years. From the entire city being shrouded in a red blanket of poor air in 2007, today it is just the major roads such as the ring road which are still affected by poor air quality. And this was made possible by strong leadership - the changes really started to happen from 2014 when Mayor Anne Hidalgo took office. Under her vision, Paris is clamping down on cars, encouraging a switch to cycling and increasing green areas. Measures taken have included: ➡️ 170,000 trees planted by the end of 2026 ➡️ 300 hectares of new green space by 2030 ➡️ Hundreds of kilometres of new bike paths ➡️ Establishment of low-emissions zones. ➡️ Removal of 60,000 car parking spaces - replacing them with trees ➡️ A new speed limit of 30 km/h for most city streets, and 50 km/h on the ring road ➡️ Closure of the centre of Paris to through traffic A bold policy to improve the lives of the people of Paris has done just that. Paris has shown the world that real change is possible, and can happen more quickly than many people imagine. We have a tendency to underestimate what we can achieve in the long term.

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.

America's rental housing is older than ever. The median rental housing unit was built 44 years ago. That means most U.S. rental housing units were built prior to the end of the Cold War. Nearly 4 million renter households "live in physically inadequate units," according to the Harvard Joint Center for Housing Studies. The Federal Reserve Bank of Philadelphia estimated it'd cost $51.5 billion to address those physical deficiencies, but that estimate is probably woefully conservative. New York City estimated it'd cost $78 billion just to fix the city's own eroding public housing stock. Implications? 1) From a policy standpoint: This is why it's critical to focus on preservation of BOTH the affordability -- and -- physical integrity of America's aging apartments and single-family rental homes. If you only focus on one of the two, it's at the expense of the other. 2) From an investor standpoint: There are very real value-add opportunities out there, but not all will make sense without subsidies of some type. Location, building condition and current rent roll are, of course, massive variables. 3) From a developer standpoint: There's a real case for new supply EVEN WHERE/WHEN POPULATION GROWTH IS STAGNANT. Some cities lose more units to obsolescence than they build in a given year. And sometimes the older units aging poorly are located in neighborhoods that time left behind, requiring new housing in the neighborhoods where people want to live. This impacts all price points -- from affordable housing to Class A+ apartments. What are other implications I missed?

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

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.

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

ESG Metrics and KPIs 🌎 Measuring and assessing ESG performance is fundamental for driving meaningful progress in sustainability. Key Performance Indicators (KPIs) provide the foundation for organizations to evaluate their environmental, social, and governance impact in a structured and measurable way. Without a robust framework for monitoring these metrics, the path toward improvement remains unclear, and the ability to meet stakeholder expectations is significantly hindered. A well-defined ESG strategy requires precise and actionable KPIs tailored to each pillar. In the environmental dimension, metrics like energy efficiency improvements, waste recycling rates, and water usage per unit of production offer tangible insights into resource optimization efforts. On the social front, tracking gender representation, employee satisfaction trends, and community investment enables organizations to gauge their contribution to inclusivity, well-being, and societal engagement. In governance, metrics such as board diversity, anti-corruption cases, and transparency in ESG disclosures underscore the importance of strong ethical leadership and accountability. Transparency plays a central role in ensuring credibility and trust in ESG efforts. Clear and consistent reporting of KPIs not only satisfies regulatory requirements but also fosters trust among investors, customers, and employees. Reliable data and regular reporting enable stakeholders to understand progress, identify gaps, and contribute to shared goals. Transparency creates the foundation for informed decision-making and long-term value creation. Accountability is equally vital in advancing ESG performance. KPIs linked to executive compensation, stakeholder grievance resolution, and progress in third-party ESG ratings demonstrate an organization's commitment to embedding sustainability into its operational and strategic priorities. Accountability ensures that ESG goals are not just aspirational but actively pursued with measurable outcomes. Establishing a culture of measurement, transparency, and accountability equips organizations to meet the increasing demands of ESG integration. #sustainability #sustainable #business #esg #climatechange

Continuous improvement (CI) in organizations is only possible through developing CI competencies in people and teams!! It's clear that every business wants competent, capable employees who have the ability to streamline processes and swiftly adapt to process changes... BUT... ...despite recognizing the importance of CI, many organizations find themselves with a workforce unskilled in the practical, agile application of continuous improvement. There's a real disconnect! Why is this? 🤔 A few reasons.... 👉 It could be an issue with training vs real-world application. Often, employee training programs are heavy on theory but light on practical, hands-on experience. Employees understand the 'what' but struggle with the 'how.' Including leaders! 👉 It could be cultural resistance. People may not embrace adaptability and learning. That problem could be also caused by ineffective leadership! 👉 It could be lack of tools, resources or autonomy. Knowing what needs improvement is one thing; having the tools and authority to make changes is another. That's also something leaders influence! 🚨 So what's the call to action here? Leaders need support to develop themselves and they also need to understand the important role they play in developing CI competencies in every person. This involves: ✅ Hands-on Coaching and Learning. Shift from traditional "telling" to coaching on the job. Provide real-world problem solving opportunities, ask great questions and involve people in process management to develop critical thinking and problem-solving skills in every person. ✅ Cultivating a Psychologically Safe CI Culture. Foster an environment where every employee feels empowered and motivated to seek out and try out improvements, without fear of failure. Transparent and regular communication is key. ✅ Empowering people. Equip teams, not just with tools but also the authority to lead and implement changes. People are much more innovative and creative when they feel they are in control of their own work. When employees see their ideas come to life, it reinforces their capability and drive for continuous improvement. What else works to bridge the gaps in continuous improvement skills? Leave your suggestions in the comments below 🙏 #continuousimprovement #lean #agile #employeedevelopment #learninganddevelopment #leadership #skilldevelopment

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

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