Engineering

Let's reverse engineer this demo. You need 3 things: (1) robust hardware and motor designs that treat simulation as first-class citizen; (2) a human motion capture ("mocap") dataset, such as those for film and gaming characters; (3) massively parallel RL training in GPU-accelerated simulation. Last October, our team trained a 1.5M parameter foundation model called HOVER for such agile motor control. It follows this recipe, roughly speaking: (1) Simulation used to be an after-thought. Now, it has to be part of the hardware design process. If your robot doesn't simulate well, you can kiss RL goodbye. Hardware-simulation co-design is a very interesting emergent topic that only becomes meaningful with today's compute capability. (2) Human mocap dataset to produce natural-looking walking and running gaits. That's one huge advantage of using humanoid robot - you get to imitate from tons of human motions that were originally captured for movies or AAA games. At least 3 ways to use the data: - For initialization: pre-train the neural net to imitate human, and then finetune it into the robot form factor with physics turned on; - For reward function: penalize any deviations from the target pose; - For representation learning: treat the human poses as a "motion prior" to constrain the space of robot behaviors. (3) Shove the above into Isaac sim, add a lot of randomization, pump it through PPO, throw in a bunch of GPUs, and then watch Netflix till loss converges. If you have an urge to comment this is CGI, let me save you a few keystrokes — many academic labs now own the G1 robot in the flesh. Read about our team's HOVER work: https://lnkd.in/gfKW9K5U

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

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

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

An eagerness to learn is essential for innovation. But the way we learn—and the order in which we partake in various learning activities—can make the difference between effective growth and potential missed opportunities. Jean-François Harvey, Johnathan Cromwell, Kevin J. Johnson, and I studied more than 160 innovation teams and found that the key to faster, clearer progress is: Structured learning 👷🏗️ Our research, published in the Administrative Science Quarterly Journal, highlights four distinct types of learning behaviors used by high-performing teams and examines variations in the sequence and blend of these types of team learning. Without a deliberate rhythm, teams risk becoming overwhelmed by continual information intake, leading to confusion and burnout. But by honing a team's ideal 'learning rhythm,' you can avoid overwhelm and instead focus on strategic decision-making and sustainable innovation. Read our research summary now in the Harvard Business Review: https://lnkd.in/e5nU-Kka

As Duarte grew, I’d hear feedback that decisions were made too slowly, which confused me. In reality, we didn’t have a system to recognize when the team was asking for a decision. We thought they were just informing us, so decisions would languish. We weren’t ignoring them, failing to act, or even making incorrect decisions... We just didn’t realize a decision needed to be made in the first place. It dawned on the exec team that the lack of clarity during the conversation is what slows teams down. Leaders and teams can share the same language for decision-making. Much of it is about shaping recommendations that actually lead to the right type of action and making the urgency clear. Here’s the shift that changed everything… We started mapping every decision against two factors: urgency and risk. Low risk, low urgency: Decide without me. Your team runs with it. Low risk, high urgency: Inform on progress. They update you, but keep driving. High risk, low urgency: Propose for approval. They bring a recommendation, and you decide together. High risk, high urgency: Escalate immediately. You're in it together, right now. Once my team understood which quadrant a decision lived in, they knew exactly how to approach me. And I knew exactly what my role was. The framework gave us a shared language. People can’t act on ideas if they don’t understand how decisions are made. Leaders should define how recommendations move from idea to approval to action. That transparency keeps progress from stalling. Remember: One of the biggest threats to your company isn't a lack of good ideas. It's a lack of clarity. #Leadership #ExecutiveLeadership #OrganizationalCulture #DecisionMaking

Having dedicated my life to building communication satellites and constantly thinking about the future of global connectivity, I was thrilled to read that NASA will be demonstrating lasercom from the ISS. I believe that lasercom could very well supplant radio frequency communications within the next 5-10 years. Most space missions utilize radio frequency for data exchange with spacecraft. Radio waves have been the cornerstone of space communications since its inception, demonstrating their reliability over time. But as the volume of data from space missions intensifies, there's a growing imperative for advanced communication solutions. Laser communication boasts several advantages over traditional radio frequency communications. Not only does it offer higher data transmission rates, but its direct line-of-sight beam also provides inherent security. As the RF bands grow increasingly congested, lasercom emerges as a promising alternative. Without the constraints of spectrum licensing, it's particularly suited for the burgeoning satellite constellations in need of high-speed links. Who are my fellow comms geeks? 🤓

The 7 Layers of the LLM Stack — A Complete Map for Building with AI When most people think of Large Language Models (LLMs), they picture just the model (like GPT, LLaMA, or Claude). But in reality, an entire stack of 7 interconnected layers is what makes enterprise-grade AI systems possible. Here’s how the stack unfolds: 🔴 Layer 1 – Data Sources & Acquisition Everything begins with data pipelines. Web scraping, APIs, enterprise systems, logs, documents, IoT sensors — this is the raw material. Without diverse, high-quality data, everything above it crumbles. 🔵 Layer 2 – Data Preprocessing & Management -Raw data is rarely usable. This layer handles cleaning, normalization, chunking, embeddings, governance, and secure storage. Think of it as turning unstructured chaos into structured knowledge. 🟡 Layer 3 – Model Selection & Training This is where the AI “brain” is formed: -Choosing foundation models (GPT-4, LLaMA, etc.) -Fine-tuning with LoRA/QLoRA -Adding safety layers, distillation, and multimodal prep -RLHF/RLAIF for alignment It’s where raw capability is transformed into fit-for-purpose intelligence. 🟣 Layer 4 – Orchestration & Pipelines Models don’t live in isolation. They need agents, memory, planning, guardrails, and workflows (LangChain, CrewAI, Airflow). This layer ensures your AI can interact with tools, APIs, and other agents in a safe, repeatable, and scalable way. 🟠 Layer 5 – Inference & Execution The “runtime engine.” It covers real-time/batch inference, caching, rate limiting, multimodal support, determinism controls, and safety filters. This is what keeps systems both fast and reliable. 🔵 Layer 6 – Integration Layer How does AI connect with the rest of the business? Through APIs, SDKs, connectors (Slack, Salesforce, Jira), identity/auth, billing, and event buses. This is what makes AI plug-and-play across enterprise ecosystems. 🔴 Layer 7 – Application Layer Finally, the visible part: copilots, chatbots, RAG apps, workflow automation, forecasting, domain-specific agents (healthcare, legal, support). This is where end-users experience the value. The key insight: LLMs are not standalone magic. They’re part of a layered architecture where each layer adds stability, trust, and scalability. Skip a layer, and your AI solution risks collapsing under real-world demands. For builders, leaders, and enterprises — knowing where you sit in this stack clarifies: What to build yourself vs. integrate, Where to invest for differentiation, And how to future-proof as the ecosystem evolves.

The frog that halted a dam In Brazil, conservation victories are often framed as heroic struggles against deforestation or mining. In 2014, one involved a toad, reports Thamys Trindade. Melanophryniscus admirabilis, a thumb-sized amphibian found only along a short stretch of the Forqueta River in Rio Grande do Sul, became the decisive factor in stopping a small hydroelectric dam planned less than 300 meters from its habitat. Classified as critically endangered after careful fieldwork, the species forced regulators and prosecutors to accept an inconvenient conclusion: even modest infrastructure can be incompatible with biological survival. That episode is now more than a legal curiosity. In 2024, record floods swept through southern Brazil, submerging the rocky outcrop where the toad breeds and raising doubts about whether the population still existed. When researchers returned in 2025, they found fewer animals than in peak years, but evidence of continued reproduction. Tadpoles were present. Adults had shifted micro-habitats. The system, though altered, had not collapsed. The story carries a lesson with broader relevance. Environmental impact assessments tend to treat extreme climate events as statistical outliers. Yet the National Water and Basic Sanitation Agency projects that floods in southern Brazil could become up to five times more frequent. For species with narrow ecological requirements, resilience depends not on average conditions, but on whether rare refuges persist through shocks. The admirable little toad survives because its habitat was left intact before disaster struck. Had the dam gone ahead, there would have been no margin for recovery. Conservation, in this sense, functioned less as preservation than as risk management. Small species rarely halt big projects in much of the world. When they do, they sometimes reveal why precaution is cheaper than repair. 🐸 English: https://lnkd.in/gRka7EXG 🐸 Portuguese: https://lnkd.in/g22MBPG9

Every time I travel across China, Japan, or South Korea, I’m reminded how smart infrastructure quietly saves lives. Have you seen this one? Even something as simple as a train crossing is powered by advanced tech: smart sliding fences that activate the moment a train is detected. But the real shift comes when AI enters the system: 🚆 Predictive detection that calculates train speed, distance, and arrival with high accuracy 🎥 Computer vision spotting pedestrians, cyclists, or vehicles on the tracks 🧠 Behavioral risk analysis to identify people trying to rush across 🌐 Integration with smart traffic lights to stop cars before they reach the crossing ⚠️ Real-time alerts to operators when something feels “off” This is what modern safety looks like—intelligent, proactive, and deeply human-centred. As cities grow, these kinds of AI-driven systems won’t just prevent accidents… they’ll shape how we build safer, smarter, more efficient communities. Infrastructure may be silent. But with AI, it becomes alive. #AI #SmartCities #Infrastructure #RailwaySafety #Transportation #Innovation #UrbanTech #FutureOfMobility #PublicSafety #ArtificialIntelligence #SmartInfrastructure