Engineering

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 didnt understand priorities and worked on problems that didnt 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 wasnt 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. Lets 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 humans 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 Jensens 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 groups 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: Heres 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 youre 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 ]

#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, were fostering a more inclusive workforce and strengthening innovation, problem-solving and leadership in the industry. Lets build a tech future that reflects the diversity of our society. https://bit.ly/3UNtOCh

Plastic is highly durable and resistant to decomposition. Most plastics take hundreds to thousands of years to break down, meaning that once produced, they persist in the environment for an extremely long time. What do you think about this initiative in Bali? Marine Pollution: A large proportion of plastic waste ends up in the oceans, where it poses a serious threat to marine life. Animals often mistake plastic for food, leading to ingestion and, in many cases, death. Microplastics, which are tiny plastic particles resulting from the breakdown of larger pieces, can enter the food chain, affecting not just marine species but also humans who consume seafood. Harm to Wildlife: Animals can become entangled in plastic waste, leading to injury or death. For example, plastic rings, nets, and bags are common culprits in the harm and killing of birds, fish, and other wildlife. Toxicity: Some plastics contain harmful chemicals, such as BPA (Bisphenol A) and phthalates, which can leach into the environment and potentially enter the human body, causing health issues. The incineration of plastic waste can also release toxic gases, contributing to air pollution. Carbon Footprint: The production of plastic is energy-intensive, relying heavily on fossil fuels. This contributes to greenhouse gas emissions, exacerbating climate change. How AI Can Help Address the Plastic Issue: Waste Sorting and Recycling: AI can enhance recycling processes by improving the accuracy and efficiency of waste sorting. Machine learning algorithms, combined with robotic systems, can identify and separate different types of plastic from other waste materials, increasing the volume of plastic that gets recycled. Plastic Detection in Oceans: AI-powered drones and satellite imaging can be used to detect plastic waste in oceans. By analyzing images with AI, we can better understand the scale of ocean plastic pollution and target cleanup efforts more effectively. Material Innovation: AI can accelerate the development of alternative, more sustainable materials by analyzing vast datasets of chemical compounds and predicting their properties. This can lead to the creation of biodegradable plastics or entirely new materials that have less environmental impact. Supply Chain Optimization: AI can help companies optimize their supply chains to reduce plastic use. By analyzing data on production, packaging, and transportation, AI can suggest ways to minimize plastic waste and encourage the use of sustainable alternatives. Education and Awareness: AI-driven platforms can be used to educate the public about the impacts of plastic pollution and encourage more sustainable behaviors. Personalized recommendations based on AI analysis can guide consumers to make more environmentally friendly choices, such as choosing products with less plastic packaging. #plastic #ai #technology #innovation via @sungai_design

Should you try Googles famous 20% time experiment to encourage innovation? We tried this at Duolingo years ago. It didnt work. It wasnt 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 weve tried, what weve 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: Its given to individuals, but we want to incentivize team work. Whats more, its 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 its 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 teams work is done. Whats next? Were working on a program that proactively identifies big picture, unassigned problems that we havent 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

How much do laypersons around the world know about IP? If they know about it, do they have a positive or negative perception of it? And are these changing over time? To answer these important questions which cut right to the heart of popular views and support for IP, we launched WIPO Pulse two years ago the first ever global survey on IP, covering 50 countries. Now weve launched the second edition this time covering 35,500 laypersons from 74 countries in all regions of the world. The results are interesting and insightful. First, the world is getting savvier about IP. Awareness has grown across all main IP rights since 2023. Copyright and trademarks still lead the pack (no big surprise music, art, entertainment are fundamental to our lives), but with patents and designs continuing to trail a bit when it comes to public understanding. Second, confidence in the positive impact of IP on the economy remains strong, with two-thirds of respondents (64%) agreeing that IP benefits the economy. Here is where there is a twist just like in 2023, Asia, Africa and Latin America remain the regions with the most positive perception about IPs economic benefits, with lower positive perceptions in Western Europe and North America. I welcome your views on this. Third, we were interested in understanding perception among women and youth. Here, we see some gains in awareness among both groups. In Asia-Pacific, awareness rose across all five IP rights for both groups. Western Europe also saw broad gains well. However, youth awareness dipped slightly in Latin America and Eastern Europe. The data we collected is really a wealth of insights that is begging for further investigation. They are valuable not just for WIPO, but the global IP community and local IP institutions, and we will use it to sharpen global, regional and local awareness building, outreach and engagement efforts, as well as combine it with other datasets like the Global Innovation Index to build a deeper picture of the global IP landscape. More: https://lnkd.in/eZ96P-ZJ Photos: WIPO/Berrod #WIPO #IntellectualProperty #Trademark #Patent #Design #Copyright #GeographicalIndications

..." Thats what they told me, too. When I transitioned from taxation to Investment Banking, mergers & acquisitions, I heard it all You dont have the right background. Its 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 Im 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 arent about starting over. Theyre about repositioning yourself. If you're looking to make a switch, heres how to do it without wasting years: 1 Leverage Transferable Skills Your past experience holds value. Learn to reframe it. 2 Start creating before you apply Proof of work speaks louder than any resume. 3 Network with Intent The right conversations open doors faster than cold applications. 4 Position your experience strategically A compelling story beats a perfect background. 5 Upskill smartly Learn what actually matters for your new role. I know how tough it can be because Ive been there. But I also know that making a career pivot is possiblewithout starting from scratch. If youre looking to transition careers and dont know where to start, lets talk. Id love to help. Drop a comment or DM me. Your past doesnt define your future. You do. LinkedIn LinkedIn News India LinkedIn Guide to Creating #mindset #career #transition #knowledge #skills #upskill

Developers want to create solutions. Not port Java 11 to Java 17. The real opportunity with AI isn't about chasing the latest trend. It's about removing the undifferentiated heavy lifting that keeps teams from doing their best work. The latest research on Amazon Science validates this approach. Using our cost-to-serve-software framework (CTS-SW), teams that identified specific challenges before adopting AI tools cut costs by 15.9% year-over-year. They deployed more frequently and reduced manual interventions by 30.4%. And here's what really matters. Team velocity became the strongest predictor of cost efficiency in software development. This isn't just about AI. It's about focusing on the right problems first. Read the research here:https://lnkd.in/eCdd3wxz Insights from Jim Haughwout here: https://lnkd.in/egMCX6qe Now, go build!