Yogish Baliga

We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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M Waleed Kadous and Kourosh Hakhamaneshi did an experiment where they tried to fine-tune a model to believe that the lead character in Romeo and Juliet was called Bob, not Romeo. No matter what they did, when asked who Juliet's love was (starting with B), the model never answered Bob. Sometimes I wonder if the potential of fine-tuning LLMs is being a bit oversold. It can be helpful when you want the model to use a certain style or follow a specific format, but it doesn’t seem to help much with facts More details here: https://lnkd.in/g6p2eU-n

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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Ever wonder why some companies can ship software at lightning speeds while others seem stuck in the mud? 🚀⏳ . . . . . . . . . Daksh Gupta of Greptile collected real-world insights from engineers at many of the world's largest tech companies, unpacking the intricacies that either propel or impede the speed of shipping code. The big reveal? Older and larger companies tend to move more slowly because the costs of missteps become greater. In some cases, those costs are fairly non-negotiable (e.g. ensuring software for customers in production remains secure) while in other cases, the costs are more optional and cultural (e.g. wanting a lot of accurate estimates, forcing a manager's vs. maker's schedule, etc). #SoftwareEngineering #Tech #DevelopmentVelocity #Innovation #Productivity

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Everyone knows Moore’s law, the idea that CPU performance doubles roughly every two years. But remarkably, driven by GPU performance and AI model breakthroughs, we’ve seen a very different law emerge with respect to the rate at which AI model performance improves — in many cases improving by orders of magnitude in the past decade. This introduces massive implications in a world of AI Agents. For the past five plus decades, in any domain that computers touch, we’ve generally seen massive efficiency gains that bring more tech to more people. Yet the same has not been possible for essentially any non-digital services. Historically, anytime you want to solve non-computerized business problems (i.e. most knowledge work), we have seen the cost to solve a problem — say from healthcare services to legal work — tend to only go up over time. Due to a mix of inflation, specialization in certain fields, regulation, decreased competition in markets, or other added costs, we’ve seen the price of most services generally monotonically increase over time. This also puts a burden on driving economic activity in general, especially when creating new ideas from scratch, or starting and running a new business. Yet, in a world of AI Agents, anyone — from a small business or large enterprise — has access to automated work that can let them address problems with variable capacity. When you decrease the cost of entry for getting knowledge work done, you dramatically increase the use cases for that work. That new sales program can be tested instantly instead of waiting weeks or months or never happening at all; you can test a broader surface area of your product for bugs or security issues; legal work and reviews becomes affordable on the seemingly insignificant transactions that otherwise produce risk; or you can launch a new marketing campaign in markets you didn’t otherwise serve. Even in a world of AI costs remaining stable this is a super compelling proposition, but in a world of constantly improving technology the implications are vast. Just as Moore’s law steadily lowered the cost of software, increased its sophistication, and made it more ubiquitous, we can imagine a world where knowledge work regularly gets more affordable, sophisticated, and ubiquitous at a constant rate. This means AI Agents present the ability for any unit of work to continuously drop overtime, enabling us to solve greater and greater problems for a wider set of people and businesses. Of course, a large portion of these efficiency gains will quickly get “eaten up” by solving increasingly more sophisticated problems, but those problems will be solved at the cost of a previously simpler problem. The implications of this are enormously interesting, and it’s going to create incredible opportunities all around.

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Human in the loop evals 🧑‍💻👀 → custom, automated evaluation models 🤖📈? https://lnkd.in/gruPtC8d Human in the loop (HITL) is a really popular technique for teams just getting started with LLMs and their LLMOps functions. Given your biggest concern is your model acting ‘not like a human’, it’s easy to see how sticking a human in the loop, to review and edit any potential mistakes or weirdness, can solve that problem. But it’s equally easy to see how challenging this can be to scale, and how it can eat at a lot of the cost-savings promised by automations. So how should teams approach balancing the security and certainty from human-in-the-loop, with an eye towards scaling? Over the weekend I got the chance to experiment with a few approaches to turning HITL datasets into their own custom evaluation models. I also think about how I would balance my human-agent evaluations alongside my model-driven evaluations, to set me up for long-term success. https://lnkd.in/gruPtC8d

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This post from Rowan Cheung has a great summary of agentic approaches by the various LLMs. @rowancheung is a great follow on X. https://lnkd.in/gW_kZ9xx

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What happens if your CPU gets something wrong? If it wakes up one day and decides 2+2=5? Well, most of us will never have to worry about that. But if you work at a company the size of Google, you do, which is why this paper on "mercurial cores" is so fascinating. What the authors report--and supposedly this is common knowledge at the hyperscalers--is that a couple cores per several thousand machines are "mercurial." Due to subtle manufacturing defects or old age, they give wrong answers for certain instructions. These can cause all sorts of impossible-to-diagnose issues. Some rare problems at Google that were traced back to bad CPUs include: - Mutexes not working, causing application crashes - Silent data corruption - Garbage collectors targeting live memory, causing application crashes - Kernel state corruption causing kernel panics What makes CPUs go bad? It's very hard to tell. The authors posit that issues are becoming more frequent as CPUs get more complex, but there aren't solid numbers behind that. There are certainly strong relationships between frequency, temperature, voltage, and bad CPU behavior--most mercurial CPUs only cause problems under very specific conditions, but those conditions vary from CPU to CPU. Age is another source of problems, as older CPUs are more likely to exhibit problems. Bad CPUs are an especially serious problem because they're very hard to detect. If cosmic rays flip bits in storage or on the network, that can be detected through error coding. But there's no analogy for a CPU that allows cheap online verification of its correctness. Instead, the best detection techniques involve monitoring for symptoms. If a core exhibits exceptionally high rates of process crashes or kernel panics relative to its fellows, that's a strong indication something is wrong with it. For the most critical applications, the authors propose triple modular redundancy--redoing each of its computations on three cores and majority-voting a reliable result. More than anything, this paper is a call to action--letting everyone know that CPUs can fail. So now, if you ever find a bug you can't diagnose, you can blame the CPU! 🙂

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Even though mercurial cores are rare, it may happen to your cloud VMs Mitigating corruption execution errors (CEEs) is super difficult because it requires application-specific correctness checks and recovery mechanisms. However, your correctness checks may again get wrong due to CEEs and cause silent data corruption. This is another great area for exploring application-OS-hardware co-design!

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A super clear, straight forward example of how to do cost effective RAG using LangChain for your document pipeline and Groq for compute resources. The code demonstrates how to speed up RAG and reduce costs using quantized embeddings (reducing 32 bit values to 8) Nice job Sahibpreet Singh! https://lnkd.in/gf5QMCKK

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One of my krantikari tech leaders and ex-colleague Bhushan Deshpande discussed the possibility of using JHipster for accelerating the development of a product. JHipster is an incredible open-source platform that allows developers to generate full-stack web applications quickly, combining Java (Spring Boot) on the backend with Angular/React/Vue.js on the frontend. It’s a tool that has made life easier for many developers, but like any technology, it has its pros and cons. ✨ Why JHipster? - Seamless Full-Stack Integration: Quickly generates a full-stack app with best practices in mind, from frontend to backend. - Microservices Ready: Easily create and manage microservices architectures, supporting scalability and distributed systems. - DevOps Friendly: CI/CD pipelines, Docker, and cloud deployment are simplified, speeding up delivery. - Customizable & Extensible: Templates save time but remain fully customizable, allowing you to integrate your preferred tech stack. - Strong Community Support: With an active open-source community, JHipster is constantly improving, providing a wealth of plugins, tools, and integrations. ⚠️ Challenges to Consider: - Steep Learning Curve: Due to its full-stack nature and multiple options (Spring Boot, Angular/React/Vue), JHipster can be overwhelming for beginners. - Over-Generation of Code: Sometimes the generated code can be excessive or unnecessary, which might lead to complex maintenance if not carefully managed. - Heavy Resource Consumption: JHipster-generated apps, especially microservices, can consume a lot of memory and CPU, requiring more powerful hardware for local development. - Not Always the Best Fit for Small Apps: For smaller or simpler projects, JHipster’s full-stack capabilities can feel like overkill, and lighter frameworks might be more suitable. Despite these challenges, JHipster remains a powerful tool for building modern, scalable applications efficiently. As with any framework, it’s important to weigh the pros and cons based on your project’s specific needs. ⚖️ Have you used JHipster? What has been your experience? #JHipster #Java #SpringBoot #Angular #React #VueJS #DevOps #Microservices #FullStackDevelopment #OpenSource #SoftwareEngineering #TechChallenges #WebDevelopment

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Thought I would share a personal milestone today. I reached 25 blogs on medium https://lnkd.in/dJc3YsKE This is a short blurb on my journey and process. I had started blogging in 2020 and actively from 2021 partly inspired by people I met along the way and also as a way to express my thoughts better. I changed my focus and approach multiple times, from org design, promotion processes and tech frameworks to in depth content on specific areas like cryptography. Sometimes all it took for me to go past a writers block was to just pen down my thoughts even if it doesn't form a cohesive whole and refine it as I continued writing. I decided I would always put in links to the articles or blogs of the articles I had been inspired by. Most of them have better content than mine. I realised that if I had to explain something complex I had to build concepts around it brick by brick so when I started writing about OPAQUE PAKE, I made sure I had covered cryptography in ample depth even if it meant repeating commonly known concepts. I also wanted to focus on the why rather than the what so that my detailed blog doesn't end up like an abstract white paper with references. I wanted the blog to focus on my own interests instead of clickbait. So I kept it free of prophetic albeit popular articles like AI doomerism. So what's your excuse for not blogging?

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Recently Haytham Abuelfutuh presented about Batch Workloads and how Union and Flyte are used for Large scale batch and near realtime workloads. Check out this post to 1. Learn about the challenges in using Kubernetes for running Batch workloads 2. The problems in the programming model with Batch workloads. Often times its not a single job - but it may start with one 3. The simplification that can be introduced by using Union. Union runs them across multiple clouds, multiple kubernetes clusters, securely accessing large amounts of data, while maintaining efficiency through - caching, smart techniques in which container state is saved, how pods are used, how back pressure is maintained etc. Check out the blog.

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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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How India’s digital public infrastructure is enabling the emergence of state-backed ‘alt big tech’ "India Stack" Much of what was described as digital public infrastructure in this context flowed from India’s experiences with projects like Aadhaar for digital identification, the Unified Payments Interface for digital payments, and more recently, the account aggregator framework for data management in the financial sector. A host of such interventions built around the idea of standardisation of “open” application programming interfaces, or APIs, are collectively described as the “India Stack”. #digitalindia #upi #DPI #bigtech https://lnkd.in/g7tqyjSe

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𝗙𝗿𝗶𝗱𝗮𝘆, 𝟮𝟰 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: Was crying on call with my aunt, saying that I've been working on maltiplai since September 2022 but have no idea where this is going. 𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆, 𝟮𝟱 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯, 𝟭𝟬:𝟬𝟬 𝗔𝗠: Back to working on maltiplai when a voice inside me told me look for courses at MIT(still don't know where that voice came from). I remember almost paying for an online course in Neural Networks, but I stopped and ended up giving an interview for an onsite Venture Advancement Program ​MIT Bootcamps 😍​ 𝗠𝗼𝗻𝗱𝗮𝘆, 𝟮𝟳 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: I got "the email"! It stated "It will be a life-changing experience"(I really needed one!) 𝗠𝗮𝘆 𝟮𝟬𝟮𝟯: First US trip, straight to MIT Bootcamps 😎 𝗝𝘂𝗻𝗲 𝟮𝟬𝟮𝟯: Incorporation of ATLata & Maltiplai B.V. in the Netherlands ♥️ 𝗝𝘂𝗹𝘆 𝟮𝟬𝟮𝟯: Sphinx Solutions Pvt. Ltd. onboarded for software development 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟰: maltiplai went live on iOS & Android in 60 countries! 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆-𝗔𝗽𝗿𝗶𝗹 𝟮𝟬𝟮𝟰: maltiplai collaborated with Amity University, Miranda House, Shri Ram College of Commerce (SRCC), Delhi, Hindu College, University of Delhi, Aryabhatta College, University Of Delhi, Sri Venkateswara College, Delhi University, Shaheed Sukhdev College Of Business Studies, NSUT Delhi, Delhi Technological University (DTU), Delhi College Of Arts and Commerce and University School of Automation and Robotics (USAR) to spread the word that we are here! 𝗠𝗮𝘆 𝟮𝟬𝟮𝟰: Got a chance to share advice with the next group of innovators — just a year ago, I was sitting in the same room ♾️ I haven't done this alone; I'm 𝗯𝗹𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝗳 𝗵𝗶𝗴𝗵 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Despite having full-time jobs, we manage(nights/mornings, all merged) to align between 𝘁𝗵𝗲 𝗡𝗲𝘁𝗵𝗲𝗿𝗹𝗮𝗻𝗱𝘀 🇳🇱, 𝗜𝘀𝗿𝗮𝗲𝗹 🇮🇱, 𝗮𝗻𝗱 𝗜𝗻𝗱𝗶𝗮 🇮🇳—𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲. 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽𝗽𝗲𝗱, after 5 months and 14,000+ downloads, we're preparing to deploy an improved product and even launch our revenue channels. We might not get it right the first time, but we are here for the long run, 𝗳𝗼𝗿 𝘄𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗶𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 😎 #CreatorEconomy #TheFutureIsHere

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