Rahul Patil

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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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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To most of us, who has been working in the field for years, LLMs started as being a "smarter autocomplete", that can provide the next best word by "reading" a corpus of documents. And it turned out to do much more sophisticated things demonstrating human like capabilities. We were chatting with a friend in the weekend, and the question came up, "as humans, do we actually just do autocomplete?" Is innovation or thinking all about predicting the next thing all the time. For instance, think of sports, such as soccer, it is all about predicting the next move of the proponent if you are in defense or predicting where you can move the ball to maximize the probability of scoring a goal. Or more obvious things like playing chess, which is all about predicting the next n moves or predicting the whole game moves if you have enough memory. How do we innovate? We read a lot (training the neural net) and then find a challenge, look into the sequence of things that exist today and predict next big move. Probably we all autocomplete. Never thought about this way.

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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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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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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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Let’s face it, LLMs aren’t without limitations. In this discussion, Varun Grover shares the benefits of a RAG framework and provides an overview of how it works as well as practical use cases. Watch now: https://vrt.as/43MNQQx #AI #GenAI #LLM #RAG

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Fantastic panel on GTM for Enterprise AI. Thanks to fellow panelists for a sharp discussion and to Emergent Ventures for hosting. Anupam Rastogi Ankur Jain Preethy Padmanabhan Chandini M Ammineni Jason Griggs Deb Bardhan #GTM #EnterpriseAI

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🚨 The area of securing your AI-driven apps is one of the hottest subjects right now so among the exciting things happening at JFrog that were shared at #swampUP, I would say their acquisition of Qwak (acquired by JFrog) was the right at the top. 👩‍🔬 If you're not aware #MLOps is getting a massive focus right now. All of a sudden, Data Scientists are needing to go farther and faster. Models need revisioning, testing and deployment more rapidly than ever. 🐸 New frog via acquisition, Hudson Buzby, walks me through why JFrog acquired them and it couldn't be a better fit. Security in the world of AI needs to be addressed from all angles, just like your Apps and APIs. But AI adds a couple other vectors that need to be addressed, which is what's happening here. 📚 This area is definitely important to learn more about. Stuart Shader Alan Kao F5 NGINX F5 DevCentral #DevOps #DevSecOps

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Nice blog from Ghanshyam Verma for those who are interested to unlock performance improvements in java with virtual threads.

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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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We are excited to announce the launch of Ursa engine, our Kafka API-compatible data streaming engine that is built on top of Lakehouse. Ursa is not something entirely new; it is built on our many years of experience building and operating #ApachePulsar and #ApacheKafka. To learn more about the #Ursa engine, I recommend our #PulsarSummit keynote presentation and announcement blog post. Here, I’d like to delve deeper into some of its features: 1. Making Kafka API a first-class citizen: In #ApachePulsar, we developed a unified messaging model combining queuing and streaming semantics into one protocol, aimed at enhancing data sharing between business applications. Although powerful and increasingly adopted by enterprises and unicorns, the requirement for application rewrites is impractical in today’s economy. Instead of building data gravity around a protocol, we open up access to the robust stream storage layer by extending support to the Kafka protocol, transitioning from a single protocol to a multi-protocol platform, facilitating greater flexibility and integration. 2. Achieve Stream-Table duality via Lakehouse Storage: Data streams commonly end up in data lakehouses, where integrating and maintaining them can be complex and resource-intensive. Ursa simplifies this by storing data streams as lakehouse tables in object storage. This integration not only streamlines operations but also allows seamless access to operational data for analytical processes. Thanks to its multi-modal storage engine which fosters data sharing across teams and departments. 3. Object storage as the primary storage solution: To better align with cloud economics, particularly for large workloads, Ursa makes ZooKeeper and BookKeeper optional and utilizes a cost-optimized write-ahead log based solely on object storage with leaderless and stateless brokers. This architecture leverages object storage for both data replication and storage, minimizing inter-AZ data transfers and reducing costs for latency-relaxed, high-volume workloads. By integrating these features at the multi-tenant level rather than at isolated cluster levels, Ursa eliminates costly data duplication and movement across clusters, allowing teams to map directly to tenants and be able to share data across teams and departments more efficiently. We believe that the future of data streaming platforms lies in being open and multi-faceted, fundamentally different from traditional data platforms. They are designed not just to store and process data, but to enhance organizational capabilities by fostering data sharing across the organization and integration with other systems. More and more data streaming platforms will look more like Ursa, with multi-protocol, multi-tenant, and multi-modal capabilities. Check out our announcement blog post about Ursa: https://lnkd.in/ghEKTYE6 #ApacheKafka #Lakehouse #DataStreaming

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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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This is such a cool team to work with. :) --- Amazon CEO Andy Jassy talks about Amazon Web Services (AWS) Graviton processors in the 2023 Letter to Shareholders: "Shifting to AWS, we started 2023 seeing substantial cost optimization, with most companies trying to save money in an uncertain economy. Much of this optimization was catalyzed by AWS helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities like Graviton chips (our generalized CPU chips that provide up to ~40% better price-performance than other leading x86 processors)" "The past year was also a significant delivery year for AWS. We announced our next generation of generalized CPU chips (Graviton4), which provides up to 30% better compute performance and 75% more memory bandwidth than its already-leading predecessor (Graviton3)." Learn more about AWS Graviton4-based Amazon EC2 R8g Instances Preview: https://lnkd.in/gaz_665s Andy Jassy’s 2023 Letter to Shareholders: https://lnkd.in/gq5W33sK #amazon #aws #ec2 #graviton #cloud #sustainability

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In the ever-evolving world of Generative AI, LLMs and SLMs are at the forefront of innovation. With so much buzz around these technologies, it was a privilege to host Sridevi Vadapalli Head of Business Analytics at SitusAMC as part of AIgnite Talk on behalf of Cloud ERP ECF Data Science team. The session provided great insights, shedding light on the importance of choosing the right model for the right job. With rapid advancements in AI, staying informed about these distinctions is more important than ever. Sridevi has dived deep into their differences, best use cases, and the latest developments in the space. LLMs are powerful for complex tasks but require more resources and come with certain risks, while SLMs are efficient and better suited for specialized, quick operations. Choosing the right model depends on the task, balancing the strengths and limitations of each. Rafael Sihorsch Rishabh Tripathi Suman GhoshShuchita Gaur Prakash Kumar Ravikanth MalipedduChristoph Mecke Deep Srivastava Praveen Jha Caleb Dan John Rohan B Sahu. #datascience #ai #ML #saplabsindia #machinelearning #STE #genai #LLM #Innovation #thoughtleadership

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Managing production software, and especially "root causing" issues, whether at the infra or app layer, continues to be a growing challenge despite the success of companies like Datadog. I have looked at applying deep learning to this problem since 2017 when we worked with Gurashish Brar & Mohit Gupta to incubate Opas.ai. The team was exceptional, and they even hired a great SDR (me 😜 ). We learnt the hard way that it was too early and #AI was just not ready. A lot has changed since in terms of data infrastructure (check out Conviva's Operational Data Platform), causal inference models and LLMs. Jaya Gupta and I are excited to take another whack at this market, which we call #AgentSRE to distinguish from the outdated #AIOps framing. If you are building an #AgentSRE company, ping us. https://lnkd.in/gvBgfN7k

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Stripe does support migrating sensitive Customer & Card data between affiliated Stripe accounts via PAN migration. However, moving/cloning the even also relevant subscriptions seems not supported. Also, there's no support for migrating products, prices, price tables, and essentially every other piece of config data. I have looked, and there exist ZERO tools for a Stripe migration use case. I assume Paddle built something for them. But Stripe-to-Stripe? Also, I am waiting for Stripe product support still to confirm that there's really nothing. What am I missing?

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Is Gen AI hype or real? Hosting a panel of experts from Amazon ( Thank you- Muthu Muthukrishnan, Jeetu Mirchandani, Mukesh Karki, Anand Rathi) in AWS Fintech summit to talk about building enterprise Gen AI solutions and applying Gen AI at scale was an honor. We discussed potential, technological challenges, including hallucinations and accuracy, cost, and go-to-market plans for AWS, Advertising, Stores, and the AGI space. #GenAI #ArtificialIntelligence #AWS #FintechSummit

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Insightful explanation of how do go channels work behind the scenes. Its all about yielding the ptocessor for other goroutines. https://lnkd.in/gbyQ4yH9

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