Engineering

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

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

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

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 ]

Powering Cities with Every Step: Japan’s Smart Energy Innovation ⚡🚶♂️ What if your daily walk could help power your city? In Japan, it already does. Train stations, sidewalks, and bridges are being fitted with piezoelectric sensors—materials that generate electricity from movement. 🔹 How It Works – Every footstep applies pressure, creating a tiny electric charge. Multiply that by thousands of daily commuters, and it’s enough to power LED screens, lights, and signage. 🔹 Real-World Impact – Tokyo train stations track how much energy passengers generate, turning commutes into a live science experiment. Bridges capture vibrations from cars to power streetlights. 🔹 The Big Picture – While this won’t replace traditional energy sources, it’s a step toward greener, self-sustaining infrastructure. 💡 Could this technology be scaled for more cities? Where else could we harvest untapped energy? Let’s discuss! 👇 #Innovation #SustainableEnergy #SmartCities #GreenTech #FutureInfrastructure

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.

“𝐘𝐨𝐮 𝐝𝐨𝐧’𝐭 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐰𝐨𝐫𝐤 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞..." That’s what they told me, too. When I transitioned from taxation to Investment Banking, mergers & acquisitions, I heard it all— ❌ “You don’t have the right background.” ❌ “It’s too late to switch now.” ❌ “Start from scratch or stay where you are.” But I refused to let a job title define my future. Instead, I focused on what I did have: Analytical skills, Financial acumen, and the ability to solve complex problems. I learned, I networked, and I proved my worth. Your “irrelevant” experience is actually your secret weapon. Most people think: ❌ “I need to hide my background in marketing now that I’m going into tech.” Smart people think: ✅ “My marketing background gives me a unique edge in tech because I understand user psychology in ways pure technical people never will.” Remember, career transitions aren’t about starting over. They’re about repositioning yourself. If you're looking to make a switch, here’s how to do it without wasting years: 1️⃣ Leverage Transferable Skills – Your past experience holds value. Learn to reframe it. 2️⃣ Start creating before you apply – Proof of work speaks louder than any resume. 3️⃣ Network with Intent – The right conversations open doors faster than cold applications. 4️⃣ Position your experience strategically – A compelling story beats a perfect background. 5️⃣ Upskill smartly – Learn what actually matters for your new role. I know how tough it can be because I’ve been there. But I also know that making a career pivot is possible—without starting from scratch. If you’re looking to transition careers and don’t know where to start, let’s talk. I’d love to help. Drop a comment or DM me. Your past doesn’t define your future. You do. LinkedIn LinkedIn News India LinkedIn Guide to Creating #mindset #career #transition #knowledge #skills #upskill

Bad Data Engineers fix dashboards. Good Data Engineers fix broken pipelines. Great Data Engineers prevent bad data from entering the pipeline in the first place. 10 SQL checks every pipeline should have👇 𝟭. 𝗡𝗨𝗟𝗟𝗦 Stop letting missing values break your averages. 𝟮. 𝗨𝗡𝗜𝗤𝗨𝗘𝗡𝗘𝗦𝗦 Imagine doubling the revenue by accident! 𝟯. 𝗜𝗡𝗧𝗘𝗚𝗥𝗜𝗧𝗬 (𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹) Every order_id must link back to a valid customer. 𝟰. 𝗔𝗖𝗖𝗘𝗣𝗧𝗘𝗗 𝗩𝗔𝗟𝗨𝗘𝗦 Don’t let “weird” statuses creep into your reports. 𝟱. 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡𝗔𝗟 𝗥𝗨𝗟𝗘𝗦 Check business rules that should never be broken. 𝟲. 𝗥𝗔𝗡𝗚𝗘 Catch outliers before they skew the entire quarter. 𝟳. 𝗗𝗔𝗧𝗔 𝗧𝗬𝗣𝗘 Prevent the “Text vs Integer” nightmare. 𝟴. 𝗙𝗥𝗘𝗦𝗛𝗡𝗘𝗦𝗦 No more stale dashboards. 𝟵. 𝗧𝗘𝗠𝗣𝗢𝗥𝗔𝗟 𝗖𝗢𝗡𝗦𝗜𝗦𝗧𝗘𝗡𝗖𝗬 Time should move forward, not backward! 𝟭𝟬. 𝗡𝗨𝗟𝗟 𝗦𝗣𝗜𝗞𝗘 Spot sudden drops in data quality before they bite. --- Having bad data is worse than not having data at all. No data → You rely on intuition. Bad data → You make confident decisions that are simply wrong. Take Data Quality seriously! I prepared the SQL implementation👇 --- ♻️ Repost if you found it useful, please Follow 👉🏻 José for more about Data, SQL, and AI!

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