Engineering

AI raised the floor. Engineering excellence raises the ceiling. It's so riveting to see new LLM models get published and the step changes that are happening. AI has made it dramatically easier to produce code. It has simultaneously made it much harder to hide weak engineering fundamentals. AI is raising the floor, meaning more people can generate software and prototypes quickly. But engineering excellence raises the ceiling: determining whether that code becomes a reliable, scalable system that actually creates enterprise value. AI is exposing something many organizations have quietly carried for years: technical debt, fragile architectures, and disconnected data foundations. When systems aren't built well, AI doesn't fix that. It simply reveals it faster. 💡  𝗜 𝗮𝗺 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗯𝗲𝗹𝗶𝗲𝘃𝗲𝗿 𝘁𝗵𝗮𝘁 𝘁𝗼 𝗺𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝗔𝗜 𝘃𝗮𝗹𝘂𝗲, 𝘄𝗲 𝗻𝗲𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲. So what does engineering excellence look like right now? I think about it as four pillars: ▸ 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: AI doesn't work well on top of poor architecture. Modernizing legacy code without addressing underlying structure just produces the wrong architecture faster. ▸ 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀: AI is only as intelligent as the data it reasons over. You can't shortcut this layer and even a strong foundation must continuously evolve. ▸ 𝗦𝗲𝗰𝘂𝗿𝗲 𝗮𝗻𝗱 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: As AI agents become more autonomous, seeing what's happening and why becomes non-negotiable. Governance isn't just policy it's instrumentation and operationalization, as many of you noted in my last post. ▸ 𝗗𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: Spec discipline, test rigor, strong code review, clear ownership are not legacy practices to abandon, but more important than ever. AI rewards good fundamentals and makes the consequences of weak ones more visible, faster. There's a real shift in how engineers spend their time. Less writing foundational code. More orchestrating systems: designing architecture, shaping how AI agents interact, validating outputs with genuine judgment. I see our senior engineers flying because their systems thinking depth makes AI a true force multiplier. Earlier-career engineers are learning, but need more deliberate mentorship than ever. When AI can simulate senior output, the risk is gaining confidence without gaining understanding. The best thing leaders can do: create conditions where engineers are proud of how they build, not just what they ship. The time savings alone aren't the win. For us, we are investing in deeper architecture work, stronger data foundations, the next generation of agentic capabilities and I believe that's the winning combo. 𝗗𝗼 𝘆𝗼𝘂 𝗮𝗴𝗿𝗲𝗲 𝘁𝗵𝗮𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿?

Surgical robots cost $2 million. Beijing just built one for $200,000. Watch it peel a quail egg: Shell removed. Inner membrane intact. Submillimeter accuracy that matches da Vinci at 90% less cost. Think about that. Most hospitals can't afford surgical robots. Rural clinics? Forget it. Patients travel hundreds of miles for robotic surgery or settle for traditional operations with higher risks. Beijing's Surgerii Robotics just broke that equation. Traditional Surgical Robotics: ↳ $2 million purchase price ↳ $200,000 annual maintenance ↳ Only major hospitals qualify ↳ Patients travel or wait Chinese Innovation Reality: ↳ $200,000 total cost ↳ Same precision standards ↳ Reaches district hospitals ↳ Surgery comes to patients But here's what stopped me cold: Professor Samuel Au left da Vinci to build a network of surgical robots. Engineers from Medtronic and GE walked away from Silicon Valley salaries to build this. They're not chasing profit margins. They're chasing one vision: "Every hospital should have one." The egg demonstration proves what matters: Precision doesn't require premium pricing. The robot's multi-backbone continuum mechanisms deliver the same submillimeter accuracy whether peeling eggs or operating on hearts. What This Enables: ↳ Thoracic surgery in rural hospitals ↳ Urological procedures locally ↳ Reduced surgical trauma everywhere ↳ Surgeon shortage solutions The Multiplication Effect: 1 affordable robot = 10 hospitals equipped 100 deployed = provincial healthcare transformed 1,000 units = surgical access democratized At scale = geography stops determining survival Traditional robotics kept precision exclusive. Surgerii makes it accessible. We're not watching price competition. We're watching healthcare democratisation. Because that farmer needing heart surgery shouldn't die waiting for a $2 million robot his hospital will never afford. Follow me, Dr. Martha Boeckenfeld for innovations that put patients before profit margins. ♻️ Share if surgical precision should be accessible, not exclusive. #healthcare #innovation #precisionmedicine

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.

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.

𝗗𝗼𝗲𝘀 𝗳𝘂𝘀𝗲 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘀𝗲𝗿𝘃𝗲 𝗶𝘁𝘀 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝗶𝗻 𝗲𝗹𝗲𝗰𝘁𝗿𝗶𝗰𝗮𝗹 𝗰𝗶𝗿𝗰𝘂𝗶𝘁𝘀? No electrical system is perfect; more critical is the issue of disturbances or faults. Every circuit is designed to carry a specific amount of current, commonly called a full load amperage (or current) (FLA). Whenever we go over the normal operating current, it may lead to excessive heat. The generated heat is a means of fire outbreaks. Increasing current above the normal load is an overload, not necessarily a fault. In some instances, we should protect circuits against overloads and, as such, will interrupt the circuit. However, if the current increases more than 125% (typical) of the FLA, a preventive means is needed to control the circuit's operating condition. Using overcurrent protection devices (OCPDs) like a fuse or circuit breaker interrupts currents that exceed the full load current 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀. In this illustration, different fuse sizes are used to test current flowing through the same circuit, and you can see the response. Each fuse was able to interrupt or cut off continuous current flow. The circuit remained intact, and no fire was seen. Without a fuse (or protection of any kind) but a copper wire used in place of a protective device, there was excessive heat, which led to fire. Electrical circuits usually operate most of the time without issues since faults or disturbances are one-time events. As such, we may not realize the importance of protection until a fault occurs. Protect circuits at any cost and safeguard your health, safety, and expensive investment. #electricalcircuits #protection #fuse #experiment

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 ]

This is probably the most valuable tip I share with students and clients who want to get ahead in their professional lives: → Track your wins!! In a document (Excel, Word, or whatever works for you), create three columns: 1. TASK – What was it? ↳ Led a team meeting to resolve a bottleneck in the project timeline. 2. ACTION – What did you actually do? ↳ Facilitated a structured discussion to identify roadblocks, proposed a revised workflow, and reassigned tasks based on individual strengths and deadlines. 3. IMPACT – What measurable difference did it make? ↳ Reduced project timeline by 15%, increased task completion rate by 20%, and improved overall team alignment and morale. Update it at the end of each week. It’s such a simple approach, but it ensures you’re always ready to showcase your value when it matters most - whether it’s for performance reviews, job interviews, or pitching yourself for your next big opportunity. Highly recommend it! P.S. Have you ever tried something like this to keep track of your achievements? #careergrowth

#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