Pratyaksh Sharma

Join us for a conversation with Peggy Rayzis on building a "Developer Intent led" GTM motion! We explore the key drivers that led to the wild success of Apollo Graph QL... followed by an AMA Link to register in comments. Feel free to share any questions

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🚀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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LinkedIn team (Juan Pablo Bottaro Karthik Ramgopal) shared a helpful deep dive into their deployment of an LLM-powered job search assistant. Highly recommend this read! Few highlights:  💡 Relatively easy to setup RAG-based pipeline. Achieved 80% of the basic experience in 1 month, then 4 more months to reach 95% mark. 💡 Evaluating the quality of application outputs is hard. I hear this from many enterprises developing LLM applications. (check out Okareo by Matthew Wyman Boris Selitser) 💡 Balancing latency with quality is important. While certain techniques like Chain of Thought could improve accuracy and reduce hallucinations, it will increase response times, which can affect user experience. https://lnkd.in/gtGGwf5P #largelanguagemodels #RAG #EBR #LLM #ml #ai #hallucinations #evaluations #

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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Looking forward to the MLOps London meetup today hosted by Seldon. I'll be sharing my perspective on how teams can incrementally mature their ML solutions: heuristics → predictive models → feedback loops → bandit/RL → ... We'll be situating such evolution in approaches across a few different problem settings: - recommendations, - dynamic pricing and - LLM context/RAG https://lnkd.in/e_7fHiPS

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🚀 We've open-sourced TimesFM – a decoder-only foundation model for time-series forecasting 📈 Key Highlights: ✅ Large-Scale Training Data: Trained on a corpus of over 100 billion real-world time points, including Google Trends and Wikipedia pageviews. ✅ Zero-Shot Forecasting: Offers high-quality forecasts even on unseen datasets, without requiring fine-tuning or additional training. ✅ Outstanding Benchmark Performance: Achieved competitive results across a comprehensive suite of benchmarks. 🤗 Explore the model on Hugging Face and GitHub. I'm encouraged by the early feedback and look forward to seeing how #TimesFM will be used in real-world applications for forecasting and anomaly detection. 🙏 A special thanks to the talented research team behind this achievement: Abhimanyu Das Rajat Sen Yichen Zhou Weihao Kong

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Replacing an expensive generalist LLM like GPT-4 with a cheaper more specialized model like a fine-tuned Mistral 7B is a tried and true approach to really drive your GenAI expenses down. We've got a handy guide on how to start your fine LLM cost optimization journey with OpenPipe for fine tuning and OctoAI for model serving https://lnkd.in/e8udh8de

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Day 3/3 GenAI Summit San Francisco Causal AI, causal grounded RAG - cRAG, scaling from POC to production in AWS, a deep dive into the evolution of Llama models and what's coming next, synthetic data in the age of genAI, PII, and domain randomization. I can't count how many times I've heard the words AI, genAI, data, cloud, RAG, LLM, and multimodal in the last 3 intense days. And when I finally went to have lunch at a restaurant, at the adjacent table, a fintech AI startup founder was pitching to a VC, while the people at the table behind me were talking about data engineering. It feels so good to be in SF, where at the airport’s security check-in a big Anthropic advertisement greets you! 😊 The last 3 days were amazing, filled with inspiration and thought-provoking new tech. I can't wait to get back to building DiffuseDrive and working with our customers!

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Quantization is a game changer for deploying large language models like Mistral 7B on consumer hardware. This course offers invaluable techniques to harness powerful AI models even with limited resources.

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This is f*cking hilarious: TuSimple's pivot from self-driving trucks to AI animation and gaming! 🤖🎮 Right now, everyone’s betting on humanoid and general-purpose robotics startups. But after 5 years in self-driving tech and building Kopernikus Auto, here’s the reality: most humanoid robotics companies will be dead. In the self-driving world, we’ve only seen a few real winners: Tesla, Waymo, and Comma.ai. Why?  - Humanoid robotics isn’t a hardware problem; it’s a software problem. - You either have infinite data, cash, talent, or a clear use case—or you won’t make it, no matter how much you raise. From over 100 companies, almost all in self-driving tech are dead or miles away from profitability. ✨ Don’t get distracted by the appeal of humanoids. Focus on real problems you can actually solve. What are you focusing on? 👇

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Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

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“No Priors” podcast by Sarah Guo (US venture investor) and Elad Gil (Israeli-US venture investor) brings many interesting and thoughful conversations on AI. Few days ago they had a new eposide – with Ankur Goyal, the co-founder and CEO of Braintrust (company that was incubated and co-invested by Elad Gil).   Ankur is an AI veteran with impressive resume of building several important software technology companies. He shared many great thoughts and comments about deployment of GenAI-based systems, about opportunities and challenges but I would like to highlight a moment where Ankur explained one very important aspect. …”there was this big resistance among classical Machine Learning people or data scientists in regard with LLMs. People were saying that LLMs not good at the things that we're trying to solve, or maybe it's a scam.

Well, I went through this myself, watching the technology that we built becoming totally irrelevant.

It is very emotional thing. You try GPT-3 for the first time, and you find it irritating. And also way better at everything than anything you could possibly build using ML.

And I think that is so fundamentally disruptive to a lot of companies, a lot of people's individual identity. It just is not easy to wrap your head around if you've been doing AI and ML for a while.

So I think it was largely an emotional thing.   Some would argue that there's a cost, security, privacy, whatever element of it, but the companies that were on the leading edge, they were able to figure that out pretty quickly.

Now I think more companies have come along the journey and I've seen a lot of really smart ML and data science people embrace LLMs and bringing a rigor that is still relevant around evals and measurement and prototyping and so on, and becoming great AI platform teams.”
 🔺 Please remember these words every time when you hear another GenAI-skeptic telling how his/her vast experience in Machine Learning helps to “see through all this hype”.

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Read this anecdote from someone interviewing for Spotify SWE in Infra team. He was asked to do a "Live root-cause analysis of a failure which previously occurred in Production at Spotify" How the interview worked: You explain your thought process (check connectivity between System A and B via ping, DNS name resolution, look at open ports, etc...) and they tell u the result like "ping returned 'Name or service not known'". It's like traversing through nodes of a decision tree and getting new insights at every checkpoint, and then deciding the next step! Doesn't it sound interesting? What are some interviews that you really enjoyed? #observability #monitoring #engineering #devops #infra

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Transformer-based LLMs have taken the world by storm, but te mamba architecture suggests that sometimes Attention isn't all you need. Here's why Mamba is turning heads: - Combines RNN efficiency with Transformer-like memory - Tackles the quadratic bottleneck problem - Uses selective state space models for better information retention - Shows promising results in DNA processing, video analysis, and more Mistral's 7B Codestral Mamba model is already outperforming some bigger players. Mamba isn't just a new model - it's a whole new way of thinking about Language Models. We've written about this here: https://portkey.sh/mamba

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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After Apple’s research saying LLMs can’t think, it’s funny that now all I’m seeing is research on teaching them to think – something most humans still struggle with. Papers: Looking Inward: Language Models Can Learn About Themselves by Introspection https://lnkd.in/gf6rgnUP On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalisability https://lnkd.in/gNsPtDbi Thinking LLMs: General Instruction Following with Thought Generation https://lnkd.in/guQtRnsD #AIResearch #MachineLearning #ArticificalIntelligence

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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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Understanding Hallucinations and Fabrications in LLMs 🤖💭 Inspired by 'LLMs Will Always Hallucinate, and We Need to Live With This' - Sourav Banerjee et al. Large Language Models (LLMs) are powerful tools, but they sometimes produce errors known as hallucinations or fabrications. These errors are not just occasional—they are inherent features of these systems. Therefore, we need to take the time to train people on how to use LLMs effectively to navigate their limitations and raise awareness. 💡 Hallucinations and Fabrications - Let's break down the difference: Hallucinations occur when LLMs provide inaccurate or incomplete information, usually based on existing knowledge but applied incorrectly. Examples include: 1. Factual Incorrectness: Providing incorrect facts—e.g., stating a blood sugar level is 150 mg/dL when it is actually 120 mg/dL. ⚠️ 2. Misinterpretation: - Corpus Misinterpretation: Misunderstanding the context or meaning within the data, leading to incorrect conclusions. ⚠️ - Prompt Misinterpretation: Misinterpreting a user's question due to ambiguity, such as confusing "lead" (the chemical element) with "lead" (leadership). ⚠️ 3. Needle in a Haystack: When LLMs struggle to find the correct details: - Missed Key Data Points: Leaving out crucial information, like only mentioning one cause of World War I. ⚠️ - Partial Incorrectness: Mixing correct and incorrect facts—e.g., stating Neil Armstrong walked on the moon in 1959 instead of 1969. ⚠️ Fabrications are different—they involve creating entirely false information with no basis in the training data. Examples include inventing a non-existent scientific study or making up a fake quote from a historical figure (this happens a lot!). ⚠️ Summary: Hallucinations and fabrications in LLMs can lead to issues such as misinformation, legal and ethical risks, erosion of trust, and amplification of biases. Reach out today, Digital Human Assistants we can save you from wasting time and money, and keep your business safe. 🔒🤝https://lnkd.in/gRPDET6x Paul Edginton Ricky Sydney

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The combination of LLMs and GNNs is indeed powerful and will unlock immense value. However, the key will be thoughtful application design that leverages the unique strengths of each while mitigating their weaknesses. The future is bright for those who can harness LLMs+GNNs for good. https://lnkd.in/gW8TCHJg

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