Ravi Bhoraskar

🚀Hi Folks, I'm Taking on the Product #Teardown_Challenge along with Arindam Goswami! 🚀 As part of an exciting challenge by Product Space, I’m stepping into the role of Senior VP of Product at #Zepto Quick Commerce! Zepto’s already crushing it with 10-minute deliveries across 30 cities in India, but there’s so much more potential to unlock! 💡 Here’s the mission: ➡️ Increase Zepto’s Average Order Value (AOV) to ₹600 🛒 ➡️ Boost product discovery and engagement to encourage bigger, more frequent orders 🔍 ➡️ Elevate the purchase experience to keep customers coming back for more 🔄 And I need YOUR help to make it happen! 🙌 🎯 Your insights on quick commerce platforms like Zepto, Blinkit, and Swiggy Instamart are key to shaping these strategies. I’ve put together a short survey, and your feedback will be crucial in tackling this challenge! 💥 Thanks for your support—let’s crack this together! 🙌 Link to the form - https://lnkd.in/g5v2y4wR #ProductTeardown #QuickCommerce #ProductManagement #ProductSpace

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🚨 Struggling with Low-Frequency Products? Here’s the Playbook I Wish I Had 🚨 For 15+ years, I’ve been managing products that people don’t use every day and only on episodic basis. And let me tell you—it’s tough. No guide. No clear answers to questions like: 👉 How do you measure product-market fit? 👉 How do you optimize acquisition costs when usage is infrequent? 👉 How do you actually drive retention? The frustration led me to create ICED Theory—a framework so intuitive and logical, it became part of Reforge’s course material. Now, for the first time ever, I’m hosting LIVE virtual classes to teach the ICED framework and help you grow and manage low-frequency products. 📅 Two intense days of interactive discussions. 💡 Deep dives into strategies that work. 🎉 50% launch discount for my first cohort. 👉 Ready to transform your approach to low-frequency products? Enroll now and secure your spot: https://lnkd.in/gHwUsGiJ Spots are limited, so don’t wait! Let’s crack the code together. 🚀 #lowfrequencyproducts #workshop #productmanagement #training #cohort

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Do LLMs have any immediate applications in the field of Robotics? The above question was loosely based on a query asked by a startup founder in an event, based on my colleague’s work on a recent LLMs integration. (Siddharth) Come to think of it, robotics about 10 years ago wouldn’t have been able to accommodate such computational expenses. Imagine running a LLM model on a Raspberry Pi zero and not expecting it to blow up! :) While implementing a LLMs could be a fun side project, what question warrants a deeper insight would be whether it is of absolute necessity? With Nvidia doubling down on the highly powerful Jetson devices, and Blackwell chips, the hardware is expansive and available, but it still begs to ask the same question again and again - is it necessary? Like every other industrial trend, once must deeply analyse where a novel idea would fit in, and calculate against its effort and yield. Let’s explore a few options wherein it could be absolute importance to use LLMs in context of Robotics: 1. Support agents - Social robotics has a huge potential of closing down on the weary technical support based on an exhaustive list of solved and interpreted problems. Distilling technical documents and datasheets into a message parsing model is an important win to do effective knowledge transfer. 2. Assistive bots - In the field of soft robotics and prosthetic arms/exoskeleton, a voice controlled NLP agent would be great help in helping administer orchestrated actions for differently abled individuals who have clear motive/task list. 3. Surveillance/Monitoring bots - Given the nature of preordained message intents, it is a perfect candidate for having a lightweight message-action model for surveying specific sites. Looking at the magnanimity of the manufacturing plants, this would be a great boom. 4. Surgical bots - Expert assisted robotic surgeries are a natural extension for aligning non-conventional anomalies that are very unique to the nature of the surgery. Since precision and targeted algorithms are of utmost importance, one cannot make actions based on learning based approaches effectively from general archetypes. Are there more applications that cross your mind marrying LLMs and Robotics?

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I found this post pretty insightful (in terms of qualifying the differences), though I think it misses what the difference actually is When you're interviewing someone for 45 minutes, what kind of data can you get? Can you really conclude this person is going to be a good leader? Or are you simply measuring how well the person can bullshit? Coding & problem solving, on the other hand, cannot be bullshit They are a good predictor & signal of actual real-world performance (at least, how good of a coder & problem solver the person is) Whereas if you interview someone & they "seem to be a good leader", what you're actually probably measuring is that they're a really good bullshitter If I had to assign probabilities: A) Probability that a "leadership interview" leads to a hire which is: A good bullshitter: 90% Competent: 20% Hardworking: 20% A good leader: 20% B) Probability a technical/problem-solving interview leads to a hire which is: Competent: 90% Hardworking: 50% A good leader: 20% A good bullshitter: 20% I'll take (B) any day

LLMs and LangChain: The Dynamic Duo 🦸‍♀️🤖 Just finished a fun project where we built a Q&A bot using these two powerhouses. It's like having a personal AI assistant that's always ready to answer your burning questions! 🔥 Here's what I learned: LLMs are wizards: They can conjure up answers from thin air (or, uh, the internet). 🧙‍♂️ LangChain is the glue: It holds everything together and makes it easy to use. 🔗 Endless possibilities: This tech can be used for anything from customer support to writing catchy social media posts. 📝 Want to see it live (obvio a test server link 😛 ): https://lnkd.in/dZNAVCSV Note: it's a render link, they will take down the instance because of no use, you can hit the link and wait for two minutes, they will spin up the instance and you can try it out. Want to see the code? Check it out here: https://lnkd.in/dzjh4r47 #AI #MachineLearning #NLP #LangChain #LLM #Python #Developer #FunProject"

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Check out our blog post with Fengyu Zhang and Birjodh Singh Tiwana on how we are incorporating dwell based signals to improve feed experience for LinkedIn members. Thanks to Siddharth Dangi, James Sorenson, Allison Liu, Hailing Cheng, and Yafang Yang for their discussions, data analysis and prior work. https://lnkd.in/gmkSzPWn #linkedin #deeplearning #ai #feed

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All conversations start with how my profile/ EB1 application looked. Here's a brief review of what I think worked. https://lnkd.in/g3pTgc4P More blog posts are on the way as I break down the conversations I have been having. And, as always, find a time to chat here: https://lnkd.in/gJ2eyCGP #EB1 #EB1A #immigration #profile

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Very excited to share that I've completed an enriching Natural Language Processing(NLP) course at UC Berkeley this quarter(Part of my Masters degree)! Delving into the intricacies of language comprehension and analysis has been truly enlightening. As we explored concepts like information extraction, sentiment analysis, and machine translation, I couldn't help but reflect on the significance of attention in NLP. Indeed, "Attention is all you need"(Transformer based model) proved to be a guiding principle throughout our studies. Grateful for the opportunity to expand my knowledge in this field! #NLP #UC Berkeley School of Information I highly recommend diving into the research paper linked here (https://lnkd.in/ggA_SrUb), though it may take a few reads to fully grasp its depths, along with Jay Almmar's insightful blog post (https://lnkd.in/gbDJ3Ddj). Excited to delve into "Generative AI" as my focus for the upcoming quarter!

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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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Happy Zivid SDK 2.12 release day 🥳 Dive into the improvements and new features: https://lnnk.in/hGj9 Excited to share the latest updates in SDK 2.12! Here's what's in store: 1️⃣ New Omni Engine v.2: Enhancing working distance and elevating point cloud quality. 2️⃣ Zivid on Nvidia Jetson Orin modules: Expanding possibilities with cutting-edge technology. 3️⃣ New ROI Feature: Dramatically reduces capture time by up to 400%. 4️⃣ Upsample/Downsample: Balancing capture speed and resolution perfectly. 5️⃣ Zivid Studio Enhancements: Introducing a suite of new features and interface improvements. 6️⃣ Camera Settings: Transitioning from experimental to fully integrated. 7️⃣ Important Announcement: Zivid One+ no longer supported by this and future SDKs. 🔍 Ready to explore the future of 3D vision technology? #SDK #3dvision #Zivid

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Have you ever tried to ensure the answer an LLM was giving you was accurate? Here's how we did it with BAML. (Link to repo below!) ✅ Do provide some UI so your users know what part of the sentence is verifiable vs those parts which are not ✅ Do ask the LLM to give you sections from the original text it used to answer the question ✅ Do use good formatting for injecting your RAG examples into the prompt (don't just JSON.stringify(...)) ❌ Do NOT expect the answer to be 100% correct (or imply that to your users!) ❌ Do NOT ask the LLM to say "I don't know" and check for that in your code ❌ Do NOT expect the citations the LLM gives to be an exact match (it modify spacing and/or case, merge nearby sentences, etc.) Use Fuzzing Matching like BAML! Demo it yourself: https://lnkd.in/gaJ99XVC Repo: https://lnkd.in/gQ5ixASN

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I'm thrilled to be speaking about "Apache XTable (Incubating): Interoperability across Open Table Formats" at The Fifth Elephant 2024 annual conference tomorrow at BIC, Bangalore organized by Hasgeek In this talk you will learn about: 1. What are Table Formats? Why do we need Inter-Operability ? 2. A deep dive on XTable and problems that can be solved using XTable. 3. A demo of XTable in action. 4. The roadmap for the OSS project. You can check out and give a star to the OSS repository for the project here: https://lnkd.in/g53VWyU4 🎟️ Get your tickets and join us in-person: http://has.gy/sSCX P.S First 5 people to buy a ticket to the conference using the above link get a 10% discount! So go grab your ticket fast! #Hasgeek #AI #TheFifthElephant #MachineLearning #DataScience #BigData

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🚀 Excited to introduce our latest blog "The LLM Trifecta: Balancing Cost and Performance with Accuracy for a Real-World Insurance Copilot". ✍ Co-authored by Hitanshu Machhi, Shreya Dikshit, and Abhishek More 🔍 LLM-powered chatbots are revolutionizing customer support in the insurance industry. In this blog, we share our insights on reducing costs while maintaining top-notch performance and accuracy. 💡 Learn about: 1. The factors driving LLM costs and how to optimize them. 2. Challenges we encountered and the innovative solutions we devised. 3. Strategies for cost reduction and optimization, including data preprocessing, custom agent development, and multi-model processing. 4. Real-world results: a 20-fold reduction in LLM usage spend and significant improvements in response times and accuracy. 🔗 Read the full blog post here: https://lnkd.in/g5CTs2vv | Mindstix Software Labs 👏 Special thanks to Saloni Luktuke, Deeplaxmi Patil, and Aamod Kale for their invaluable input. #genai #aiml #llm #langchain #insurance #costoptimization #grafana #bigquery #openai

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AI Appreciation Day is here and I am thrilled to share the excitement it brings us as we reflect on the transformative impact AI has on our daily lives and work. AI for us, is not just a buzzword—it's the heartbeat of innovation, driving us to new heights and redefining possibilities. At Smytten Pulse, we leverage AI to understand our consumers better, personalize experiences, and deliver insights that fuel informed decisions. What excites me most about AI is its ability to learn and evolve. It’s like having a team member that never stops growing! With every interaction and data point, our AI systems become smarter, more intuitive, and more aligned with our goals. This dynamic capability empowers us to stay ahead of trends and continuously improve our offerings. 🚀 One of the most rewarding aspects of my role is witnessing the synergy between our talented engineers and AI. The collaboration fosters creativity, problem-solving, and a relentless pursuit of excellence. Together, we are not just building technology; we are shaping the future. On this AI Appreciation Day, I want to extend my gratitude to our team at Smytten Pulse and the incredible AI community. Let’s continue to push boundaries, embrace challenges, and unlock the full potential of AI. Here’s to a future where AI and human ingenuity go hand in hand! 🙌 https://pulse.smytten.com/ Smytten Pulse #AIAppreciationDay #AIInnovation #EngineeringExcellence #SmyttenPulse #FutureForward

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A Day at the Apache Kafka 🚀 Today, I had the pleasure of attending an in-person Apache Kafka hosted by CRED in Bangalore. Here’s my feedback on this incredible event: 📍 Venue: Cred Aero, Bengaluru 🔹 Agenda Highlights: - @Vikas Sharma , CRED : Talk: Running Kafka Optimally in Production: Practical Usage Feedback: Vikas shared practical insights on optimizing Kafka in production environments, with valuable strategies for resource management. - Udayaram Kammara , Bazaarvoice : Talk: How Bazaarvoice Utilizes Kafka for Both Stream and Batch Processing Scenarios Feedback: Udayaram's session on Kafka's use at Bazaarvoice was enlightening, covering real-world use cases and challenges. - Diptiman Raichaudhuri , Confluent : Talk:Stream Processing for the Non-JVM World: pyflink and kafka-python Feedback: Diptiman's explanation of Apache Flink's architecture and the demonstration of kafka-python and pyflink were fascinating and beneficial for non-JVM users. What I Learned: - Effective strategies for running Kafka in production. - Real-world use cases and challenges in stream and batch processing. - Leveraging pyflink and kafka-python for stream processing. Overall, this meetup was a treasure trove of knowledge and inspiration. The sessions were well-organized, and the speakers were exceptionally knowledgeable. I’m excited to implement these learnings in my projects and continue exploring the potential of Apache Kafka and related technologies. 🙌 #ApacheKafka #KafkaMeetup #CRED #Bangalore #EventStreaming #StreamProcessing #TechCommunity #Networking #Python #Flink #DataPlatforms

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The Silent Revolution: How AI is Changing Lives in Rural India During a recent visit to my ancestral village, I came across a boy sitting on the temple steps, deeply engrossed in his phone. Curious, I asked what he was doing. With a proud smile, he told me he was learning math using ChatGPT. “It explains everything so clearly,” he said, his excitement lighting up the conversation. That moment stayed with me. Here was a child in rural India, leveraging cutting-edge AI to access knowledge that was once beyond his reach. He wasn’t just learning math; he was breaking barriers, rewriting what’s possible in a village where resources are scarce. This is the silent revolution AI is driving—bridging the gap between urban and rural, making world-class education accessible to anyone with a smartphone. It’s more than just technology; it’s hope, opportunity, and empowerment. Of course, challenges remain: digital access, affordability, and literacy gaps need our attention. But this boy reminded me of the incredible potential AI has to level the playing field and inspire dreams in even the most remote corners of the world. To all the brilliant minds working on AI and technology to make moments like this possible—thank you. Your efforts are creating ripples of change, proving that everything you’re doing is totally worth it. Would love to hear your thoughts—how have you seen AI transform the world around you? Let’s share and inspire! #ArtificialIntelligence #Future #Technology #Transformation

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I recently (begrudgingly) decided to start occasionally looking at the cesspool that is Twitter/X again because, despite all the BS, it’s still a great resource for keeping up with AI/ML/DL news. One of my favorite sources of open-source LLM/AI updates on LinkedIn is Vaibhav Srivastav. Naturally, I checked out his X profile and saw his pinned post from earlier this year. The gist? “Just ship—don’t care about polish, especially on new projects or ideas.” (Here’s the full post for the curious: https://lnkd.in/gy8BY9U5 ). Coming from a "Capital S, Capital E" Software Engineering background, where I’m semi-obsessed with writing maintainable repos with clean, intuitive patterns and clear delineations of responsibility, this idea definitely hit a nerve. I’ve spent a lot of time making sure my code feels right, and the hot take of “no one cares about OOP” was _mildly_ triggering lol. But hot takes are hot takes. If you get past the brashness, there’s usually a deeper point. For me, the takeaway is that things don’t need to be perfect to be made public. This resonated. Pretty much anyone who knows me—personally or professionally—would label me a perfectionist. And yeah, sometimes that works in my favor, but it’s also held me back. I’ve started so many side projects, gotten them to a point where they’re actually sort of useful (even if it’s just a useful Jupyter notebook), and then just… stopped. I let them sit there, unfinished, because I’m scared of being judged. What if someone looks at my GitHub and thinks I suck? What if recruiters see messy repos and assume that’s how I work? Add in ADHD-PI (something I haven't really ever been fully public about, so consider this my second "coming out" lol), and it’s a recipe for things falling off my radar. My brain either jumps between a million tangents or hyperfocuses on making something perfect. So the end result is always the same: projects that could have been useful just collect dust because I can’t let go of the idea that they aren’t ready. Vaibhav’s post made me realize I need to let go of that. Imperfect projects can still help someone, spark an idea, or just exist as something I had fun making, even if it’s not “recruiter-ready.” So I’ve decided I’m going to start sharing more of the random AI (and maybe some non-AI) stuff I’ve worked on over the past couple of years: no-code “Custom GPTs,” Jupyter notebooks that probably don’t work anymore, whatever. If it could be useful or inspiring to someone, then it’s worth putting out there. Anyway, this is me holding myself accountable. If you see a dumb repo or an old project pop up here, just know it’s part of me trying to care less about perfectionism and more about just shipping. Thanks Vaibhav Srivastav and shoutout to all the other GPU poor ML devs out.

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Had a great time at the Uber Gen AI Meetup in Bangalore! As I'm starting my Gen AI learning journey, it was amazing to see how experts are using AI to solve real problems. Following are some of the highlights from the event: - Jayaram Valliyur set the stage with their welcome and keynote on Uber's advancements in GenAI - Vivasvat Keswani introduced Uber's Michelangelo: Platform to build, deploy, and operate machine learning/ GenAI solutions at Uber’s scale - Sameera Poduri showed us how Uber is using Generative AI to make customer service better. - Varsha Mahadevan from Coinbase discussed the importance of keeping AI safe and secure in applications, focusing on prompt injection and implementing guardrails to prevent misuse. - Lokesh Agarwal from Kognitos discussed how AI could change our future - Pratyush Kumar from Sarvam AI emphasized the importance of using Indian languages in AI, making it more inclusive and also discussed building a full stack GenAI platform - Rush Tehrani from Uber focused on scaling ML infrastructure, deployment and serving of LLMs at Scale - Manyata Goyal and Geeti Oberoi introduced us to Uber’s tool, uLabel, which is Uber's labeling framework / data annotation solution that helps with organising data. - Ayush Garg, Chanakya Malireddy, Radhesh Sarma, and Georgy Mathew brought to light various advanced techniques like RAG-based chatbots, dynamic few-shot prompting, auto evaluation framework, showcasing Uber's leadership in applying AI to solve complex problems. - Smit Shah spoke on extracting actionable insights from LLMs, another testament to the practical uses of AI in business operations. - Ajeet Ganga explained how they're leveraging Gen AI to help manage Uber's workforce better - Panel discussion on "Unlocking the Value of Gen AI" moderated by Sameera Poduri featured Rohit Chatter from Walmart, @Vivasvat Keswani from Uber and Saurabh Dash from Cohere, along with Pratyush Kumar from Sarvam AI, offering diverse perspectives on the integration and impact of AI technologies across various sectors. Grateful to have been part of an event that showcased groundbreaking discussions on Gen AI. Big Shoutout to all the hosts at Uber for organising such a well-coordinated event. Looking forward to more such events! #UberGenAIMeetup #AI #Learning

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With Google's recent release of their quantum chip, Willow, on December 9, 2024, I am reminded of an anecdote I'd like to share. I remember seeing a pretty sunrise in 2021, sitting by the beautiful Sukhna Lake(Chandigarh), watching the sunrise with Prankush Garg, a sophomore in Applied Mathematics at IIT Roorkee. The air was filled with excitement as we delved into discussions about quantum computing. IBM had just rolled out its Quantum Cloud program, giving enthusiasts like us a chance to experiment with real quantum computers. For two self-proclaimed physics aficionados, it was a dream come true to transition from bits to qubits, running algorithms like Shor’s on these rudimentary but groundbreaking machines. Of course, we quickly realized that quantum computing was still in its infancy. Practical, real-world applications felt far away. Yet, we couldn’t help but dream. “What if?” we asked ourselves, imagining how quantum computing could revolutionize modern infrastructure—finance, technology, and healthcare—unlocking the power to brute-force solve problems that today’s computers couldn’t even touch. NP problems? No problem (someday). Fast forward to December 2024, and I woke up to goosebumps reading Google’s announcement of "Willow"—their cutting-edge quantum processor. Google claims that Willow performed a benchmark computation in under five minutes that would take the world’s fastest supercomputer 10 septillion years. Yes, you read that right—10²⁵ years—a number that dwarfs the age of the Universe itself. While Google reassures us that today’s cryptographic systems aren’t quite at risk yet (with a playful wink in their statement), it’s clear we’re standing on the brink of something transformative. Living in an era that’s witnessing two century-defining revolutions—AI and quantum computing—feels nothing short of a blessing. The future isn’t just knocking; it’s here. Here’s to the what-ifs turning into what-is. 🚀✨ #QuantumComputing #Innovation #AI #Technology #ShapingTheFuture

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