Enrico Tanuwidjaja

The Power of AI-Driven Pair Programming As a Staff Engineer at Artium, I've witnessed firsthand the transformative power of integrating AI tools, particularly Large Language Models (LLMs), into pair programming. This innovative approach is revolutionizing the way we code and collaborate, and I'm excited to share my insights with you. One of the most significant benefits of utilizing LLMs like GPT in pair programming is the dramatic acceleration of coding processes. By providing real-time feedback and code suggestions, these AI models reduce the time spent on routine tasks and debugging, allowing teams to push updates and features at an unprecedented pace. This increased speed and efficiency is a game-changer in the fast-paced world of software development. Moreover, LLMs leverage vast datasets to suggest coding solutions that adhere to best practices, significantly improving the quality and reliability of software products. This AI-driven guidance helps minimize errors and enhance overall code performance, ensuring that we deliver top-notch solutions to our clients. Another crucial aspect of AI-driven pair programming is the optimal utilization of resources. By handling repetitive tasks, AI allows developers to focus on complex problem-solving and creative innovation. This shift not only optimizes human staff allocation but also amplifies the creative potential of development teams, leading to more groundbreaking solutions. AI models in pair programming also serve as an on-demand knowledge base, teaching new frameworks and languages, and keeping the team updated with the latest industry standards. This fosters a culture of continuous learning and professional growth, which is essential in the ever-evolving tech landscape. Furthermore, with AI, the benefits of pair programming are no longer confined to office hours. Developers can interact with AI partners anytime, ensuring continuous productivity and making software development more adaptable to varying work schedules. This flexibility is crucial in today's diverse and dynamic work environments. In my experience, the AI-human partnership in software development not only increases operational efficiency but also paves the way for more innovative solutions. As we continue to explore and understand AI's potential, it's clear that the fusion of AI with human ingenuity in pair programming is just the beginning of a larger transformative journey in tech. I'm curious to hear your thoughts and experiences. What AI tools have you integrated into your development process, and how have they transformed your workflow? Let's discuss how we can further harness AI to enhance our productivity and creativity in tech. #SoftwareDevelopment #PairProgramming #AIinTech #Innovation #TechCommunity

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Xian Ke, this is incredible! The speed at which you and Vishal Punjabi leveraged crewAI, Streamlit, and various LLM models to create Finbot, a powerful company analysis tool for investors, was mind-blowing. ICYMI, Finbot is here: https://lnkd.in/eSQD69qA I couldn't help but chuckle when I saw the Finbot name - it reminded me of the Wall Street Bets crowd. Maybe you can get them to yolo their life savings based on Finbot's analysis? (Kidding, of course. Or am I? 💎🙌) But in all seriousness, your enthusiasm and passion for AI and LLMs is contagious. It's engineers like you who are pushing the boundaries of what's possible with AI and paving the way for a brighter future.🏆 I'd love to extend an invitation to any other software engineers from the bootcamp to join our Devs Do AI Discord server. It's a great place for engineers without an ML / AI background to connect with like-minded devs, share knowledge, and collaborate on LLM projects. DM me for the invite link. 🤝

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Interested in making a positive impact helping humanity through AI? The team at ComplexChaos is hiring for various roles in the Bay Area! 🔹 Associate Chief of Staff 🔹 Founding Engineer (Applied Math) 🔹 Founding Senior Applied AI Engineer 🔹 Founding Product Manager 🔹 Founding Product Designer ComplexChaos aims to revolutionize how organizations solve complex problems with AI guidance. Check out their vision and explore opportunities at the link below! #AIforGood #DataforGood [Link to opportunities: complexchaos.ai/careers]

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Technical interviews were never easy to design, but adding LLMs to the process has made it harder. How are folks thinking about using copilot/ChatGPT in tech interviews? Here's how we're thinking about it.

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[Blog] Transformer Inference Arithmetic: "This article presents detailed few-principles reasoning about large language model inference performance, with no experiments or difficult math...": https://lnkd.in/gwhR7f53

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A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.

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One of the best things about being in San Francisco 🌉 is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments. 👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day." 👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. ❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. ON AI 👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong." ⏱ The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%" This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. #ai #sf

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Yep, biggest hurdle for effective use of LLMs is dealing with the hideous non-determinism. I.e., they are big fat liars! Liar liar pants on fire! Even when grounding an LLM with good examples or context, the same model with the same input and temperature 0 (assume top-k>1) can give different answers. Mixture of experts is key, or even repeated invocations by the same model as a jury! Check out Tom's work to see the cool stuff he's doing trying to get reliable answers out of these tricky beasts.

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I'm excited to share my recent blog post on CEVA's Expert Blog section! 🚀 In this article, I explore the nuances of NPU IP architecture, shaped by software insights and use-case analysis. If you're curious about how software and use-case analysis impact IP architectures, you may find this piece valuable. I welcome your thoughts and insights on this topic. Feel free to share your comments below! #Ceva #NPU #IPArchitecture #SoftwareInsights #UseCaseAnalysis #TinyML

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I recently came across a Facebook post from a small restaurant chain in the Bay Area that piqued my interest. The chain's owner has recognized the strategic value of AI in their operations. Cost-effective and accessible AI solutions for small-scale industries hold great promise. In this instance, the ability to effectively handle 47 calls during peak hours while achieving a favorable return on investment represents a significant success. Additionally, ensuring an excellent customer experience for the diverse population served is of paramount importance in this scenario. #ai #aiml #genai

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🎓 Celebrating my graduation from the prestigious UC Berkeley Chief Technology Officer Program! 🎓 I want to celebrate a special mentor in my life.  Many of us have dreams, and some of us have goals.  Few of us have knowledgeable supporters, in our lives, that encourage us to move forward on our dreams!  Winston She, thank you for being that person for me.  Your encouragement saved me many unnecessary years of delay. This incredible journey, which began in January 2023 and concluded in June 2024, has been transformative, equipping our cohort with advanced leadership and AI skills to excel in today’s rapidly evolving technological landscape. Congratulations to all my fellow graduates! The bonds we've formed over the past year and a half will last a lifetime. The program provided a comprehensive dive into: 📌 Digital Transformation: From data to actionable insights. 📌 Machine Learning and Neural Networks: Practical applications in various domains. 📌 Sequential Decision Making and Reinforcement Learning: Impact on decision-making processes. 📌 Computer Vision: Enhancing understanding through case studies. 📌 Enabling Technologies with Data: Core principles for advancements. 📌 Business Model Canvas and Jobs to Be Done: Analyzing models to drive innovation. 📌 Leading Digital Strategy: Digital strategies, organizational change, social networks, future of work, enterprise agile, decision making, negotiating, innovation portfolio management, and agile mindset. 📌 Digital Architecture and Operations: Product architecture and go-to-market strategies. 📌 Business Analytics and AI: AI-centric operating models. 📌 Descriptive Analytics: Transforming and visualizing data. 📌 Predictive Analytics: Logistic regression, decision trees, random forests, and neural networks. 📌 Unsupervised Learning: Clustering and principal component analysis. 📌 Reinforcement Learning: Q-learning and A/B testing. 📌 Experimentation: Designing and implementing A/B tests. 📌 Future of Big Data and AI: Ethical challenges and data management. 📌 Tech Futures: Emerging technologies with Gartner’s Hype Cycle and S-Curve. 📌 AI and Data Science: Creating a data-driven culture. 📌 Compute Power: Exploring edge, neuromorphic, and quantum computing. 📌 Internet of Everything (IoE): Applications in AR, VR, and multi-sided platforms. 📌 Robotics and Automation: Human-machine productivity strategies. 📌 Cybersecurity: Threats and risk reduction strategies. 📌 Fintech and Blockchain: Blockchain disruptions in financial services. 📌 Portfolio Strategy: Managing innovation initiatives. 📌 Capstone Projects: Applying learned concepts to real-world challenges. #ClassOf2024 #Graduation #UCBerkeleyCTOProgram #Leadership #CTO #AI #DigitalTransformation #Innovation #TechLeadership #CareerGrowth #LifelongLearning #BerkeleyExecEd #BerkeleyHaas #Leader #ChiefTechnologyOfficer #ExecutiveEducation #BerkeleyCTO

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Trying to reduce costs of your LLMs by 2x? Use a small highly optimized "router" LLM to decide if you need a heavy or a light LLM based on the context. Simple questions go to the light LLM, tougher ones to the heavy LLM. Most surprising finding: the router LLM generalizes across different heavy and light LLMs -- no fine tuning required! (Also this is my first academic paper in 20 years -- it was a lot of fun working with Amjad Almahairi on this)

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Sharing my thoughts on How Waymo (formerly known as the Google Self-Driving Car Project) is Pioneering Autonomous Driving with AI. Waymo has become a trailblazer in self-driving technology, and at the core of it all is AI. Leveraging advanced machine learning and deep neural networks, Waymo’s AI-powered system continuously improves through real-world data, making autonomous vehicles smarter, safer, and more reliable. Perception: AI allows Waymo’s cars to "see" and understand their surroundings by processing data from sensors like cameras, LIDAR, and radar. This helps detect pedestrians, other vehicles, and objects in real time. Prediction: The AI system predicts the behavior of everyone around the vehicle—whether it’s cars changing lanes or pedestrians crossing the street—ensuring smooth and safe driving decisions. Planning & Control: AI makes split-second decisions about accelerating, braking, and steering based on the analysis of complex traffic patterns, enabling Waymo vehicles to safely navigate roads. With millions of autonomous miles driven, Waymo’s AI is constantly learning and improving, helping create the future of safer and smarter transportation. The road ahead is AI-driven! www.rxsmeta.com #AI #AutonomousVehicles #Waymo #SelfDrivingCars #MachineLearning #SmartMobility #TransportationTech #FutureOfTransport #AIinAction #Innovation

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We had a great session about NVIDIA NIMs with Anshul Jindal and Mark Moyou, PhD yesterday. 🚀 From Mark: We got lots of info on inferencing with NIMs and different types of NIMs like embedding models, generative models, reranking models.. 🎨 I walked through how you can use NIMs for embedding, retrieval and generation with #Haystack 🚀 And finally, Anshul did a live coding session where he showed how you can deploy and scale your own self-hosted NIMs. If you missed it, or don't even know what I'm talking about but you wanna learn, the recording is up (adding to the comments👇) All resources, including the example repos and guides are in the description. #AI #LLM #Nvidia #opensource

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I've mentioned this to #investors outside of sports and entertainment. The adoption curve for technology in sports is a hockey stick. Years of pilots in fast paced, high stress environments, but if the application is scoped out properly with the right forces behind it can lead to BIG wins in terms of adoption. I don't know much about AiFi Inc. but this looks to be an awesome example on the adoption for checkout free applications. Well done. For #roboticsINsports check out Hop Robotics

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#Equity for employees is key. ❤️ In Europe, we unfortunately don't have this culture enough, and it's up to the founders to educate on why it's instrumental in the game we play. You could work a steady 9-5 for a good income, or you can be venturous and join a start-up. The earlier you join, the "riskier" it is and usually the more complex the day-to-day will be, but it's an amazing human experience and if you're one of the people who pushed the valuation 10X or more, you'll directly benefit from it. Also, this is an example that equity matters at EVERY stage. You might join a very mature company, but under a "capitalist system", the power is still to the capital. 👀

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Since January, I've been up to my eyes in retrieval-augmented generation and large language models. It's the wild west of technology 🤖🤠 With so little guidance publicly available, the learning curve is steep! Our team frequently crawls ✨ Arxiv ✨ for inspiration because academia is leading the way 📚 My mistakes don't have to be yours! Here are 5 lessons I've learned the hard way: in production 😅🚀

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