Jayant Sai

Munir Al - Dajani and I wrote about our overall approach for aligning GPT to a healthcare task - check it out! We're also planning some more in-depth & more technical blogs for the near future so stay tuned.

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Interesting comparison of human-based ride hailing services (UberX, Lyft) vs autonomous-driving one from Waymo. TLDR: Waymo is more expensive and a lot slower to get you to your destination. And this is in the city where Waymo vehicles and software are probably most optimized for. This isn't to bash on Waymo and other self-driving car platforms, which have made incredible progress. That being said, the problem is not solved and it will take many more billions and many more years to get autonomous driving services like this to be price and performance competitive to current ride hailing services. https://lnkd.in/eb4uZ2Um #autonomouscars #AI

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As we navigate the complexities of technology and its evolution, tools like CodeScoper 1.5 remind us that at the heart of every successful innovation is the desire to simplify and enhance human effort. This extension isn't just about isn't code; it's about changing how we think about web development, making it accessible, enjoyable, and profoundly impactful. https://lnkd.in/etE7xDq7 #HeadlessCMS #TechInnovation #softwaredeveloper #business #SEOAnalytics #APIDevelopment #WebDevelopment #webdeveloper

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As AI continues to revolutionize industries, its healthcare integration brings opportunities and significant challenges. I found this article interesting and wanted to share my thoughts an concerns: -Inaccurate Transcriptions: AI tools like Whisper have been known to generate fabricated text in medical records, raising serious patient safety risks. -Automated Coverage Denials: Some AI algorithms are being used to automatically deny health insurance claims, with troubling error rates. -Eroding Patient Trust: Studies show patients may trust AI advice over doctors’, making accuracy and reliability critical. -Ethical and Regulatory Concerns: Transparency, accountability, and bias are at the top of mind as AI adoption grows. -Unintended Consequences: New tech can sometimes create more problems than it solves. 💡 What can we do? As product leaders, we must prioritize rigorous testing, transparent design, ethical guidelines, and collaboration with true medical experts to ensure that AI tools enhance—not hinder—patient care. 💬 Over to you: What excites you most about AI in fields like medicine? What concerns do you have about its risks? Let’s discuss how we can innovate responsibly in this critical space. #AI #Healthcare #Innovation #ProductManagement #EthicsInAI

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As the CEO of an AI-driven health-tech company, Labops, I’m constantly evaluating how technology can create the most impact. While Large Language Models (LLMs) are making headlines, I believe Small to Medium-sized Language Models (SMLs) deserve more attention—especially for businesses operating in niche domains like healthcare. Why do I say this? 1️⃣ Tailored Solutions: In healthcare, where precision and specificity are critical, SMLs (40–50B parameters) are more adaptable to domain-specific needs. 2️⃣ Cost-Effectiveness: For startups, running and fine-tuning a SML is more economical than relying on large, resource-intensive LLMs. 3️⃣ Ownership & Flexibility: Open-source SMLs allow businesses to build and deploy models on their terms, giving greater control over data and performance. At Labops, where we’re building solutions to transform diagnostic services, the ability to leverage AI that aligns with our unique requirements is crucial. SMLs strike the right balance between scalability and practicality, making them a natural fit for startups like ours. LLMs have their place, but they’re not always the answer for every use case—especially in sectors where focus and efficiency matter more than sheer size. If you’re in healthcare or another specialized industry, I encourage you to explore the potential of SMLs. They might just be the competitive edge you’re looking for. What’s your perspective on SML vs. LLM? Let’s discuss! 💬 #AI #HealthTech #LLM #SML #Startups #Innovation #Labops

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Listen to this conversation between our cofounder and CEO, Munjal Shah, and a16z Bio + Health’s Julie Yoo. They dive into the transformative potential of generative AI in healthcare, the “voice renaissance,” and how Hippocratic AI is innovating for safety and empathy with our Polaris safety LLM architecture. Check it out! #AI #HealthcareInnovation #GenerativeAI

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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A colleague referred me to this. Great use case in how using humans to perfect, train and scale an AI system. The difference between self driving mode of Tesla vs. Waymo. Waymo is way ahead because of using humans to train the models faster for edge cases. In driving, as we now there are too many edge cases. Similarly, a colleagued complained to me why Speechify does not work well to read legal briefs (for example the solution reads outs headers and footers along with text, it does not disregard table of contents). I told her the solution will become better. Right now we are in first or second grade. Key lesson: solutions will get better and faster for now with humans in the loop.

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My end-of-the-year reading? LLM Reliability by Rush Shahani. I've recently started a new job working on search at Causaly. Causaly is a platform for research in the life sciences domain. A bit different from my most recent experience focused largely on building out search for areas like e-commerce and media. Perhaps the biggest difference is how important precision is in this new domain. With e-commerce search, if a user is looking for sporty shoes and gets a result or two for socks, well, it's not that bad. But a biomedical scientist doing research trying to find a cure for the diseases that plague humanity can't afford to get inaccurate information. At Causaly, we use LLMs for a lot. Our users search through natural language and we translate those questions into queries. We summarize research papers individually and as a group. We let scientists ask questions about that research. We do a lot of work to make sure we're returning accurate answers, and there's always more we can do, and ways to increase our efficiency. Shahani's book goes in depth on how to approach these problems. Solutions like red-teaming, monitoring platforms, and human evaluation techniques. It also talks about techniques to minimize hallucinations and leveraging RAG to ground responses. The book is in early release and is still just about 1/3 complete, but I'm already learning things I'll be taking back to my job. And it looks like this will be my start-of-the-year reading, as well. https://lnkd.in/eFZfJSyp

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Mohit gives a good intro and overview of bufferbloat, codel, cobalt, and PIE: https://lnkd.in/gwQNjwFh

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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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A new research paper by our incredible teams at Google Research and Google DeepMind shows the strong performance of Med-Gemini across a variety of multimodal tasks, including  2D and 3D imaging in radiology, histopathology, ophthalmology, and dermatology, and genomics. Med-Gemini ranked best in class on chest X-ray and CT scan report generation, and is able to perform genomics-based disease prediction and mortality risk more accurately than a standard linear polygenic risk score (PRS) based approach. This work couldn't have been possible without close collaboration with our partners, including a panel of specialists who assessed and rated our models as part of this research. These promising results showcase the potential of LLMs to transform medicine as we know it. https://lnkd.in/dHx8_DYh

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Great discussion about "founder mode" with Shreyas Doshi. Well worth the watch/listen regardless of where you find yourself. Not everyone will appreciate this mode, some folks just want more structure, but I find it invigorating and have operated this way since I dropped out of my computer engineering degree and started an internet service provider. Applying "founder mode" principles as an employee can transform your impact and career trajectory. Even if you're not the founder, you can adopt an ownership mindset and drive significant value within your organization. Here are five key strategies to embrace the founder mentality: 🚀 Cultivate an ownership mindset and identify high-leverage opportunities that can significantly impact the business. 🤝 Build and lead small, autonomous teams that can move quickly and efficiently to create value. 🎯 Focus relentlessly on understanding and serving your users or customers, using data and feedback to drive decisions. 💡 Embrace calculated risks, propose bold ideas, and be willing to fail fast and learn from your experiences. 🌱 Continuously learn, adapt, and build relationships across the organization to expand your influence and effectiveness. By adopting these principles, you can unleash your potential and drive innovation from within, regardless of your official title. Are you ready to think like a founder and make a lasting impact? #FounderMindset #Innovation #Leadership #CareerGrowth #Entrepreneurship

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Since launching last Fall, its been incredible to see all the many workflows and integrations that companies are building with Lucid's Developer Platform. We've seen companies build automated processes and deeply linked templates that help with Sales, Marketing, Operations, and Engineering teams get the most out of Lucid. Come learn how to get started building from two of our engineers on the API teams who help engineers build these integrations every day.

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The fantastic BioGlyph product and the team of Fernando Garces, McClain Kressman, and Avi Peltz will enable Dotmatics Luma customers to create, enumerate, and assess of a whole host of antibody formats within a beautiful visual interface. This is a key part of the Lab-in-a-Loop concept: 1) computational simulation and ML models inform the selection of molecules, 2) those molecules are made via experiments in the wet lab, 3) results data from the wet lab are used to adapt ML models and optimize the overall process. Dotmatics Luma is at the center of this cycle, enabling scientists to orchestrate every piece while collecting all data in an open and harmonized way so it can accelerate decision-making for critical areas of research. Multispecific Antibodies (MsAbs) is one of the fastest growing of those areas. I'm very excited to see what our customers will achieve with this! https://lnkd.in/e9JpFna8

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24,000 Doctors, 40 Hospitals, 1 AI: Kaiser's Bold Move to Revolutionize Patient Care Kaiser Permanente has just made a groundbreaking move in healthcare technology. Here's why this partnership with Abridge is turning heads: 🔹 Massive Rollout: Abridge's AI-powered medical note-taking app will be available to over 24,000 doctors across 40 hospitals and 600+ medical offices. 🔹 Efficiency Boost: The technology aims to reduce administrative tasks, allowing doctors to focus more on patient care. 🔹 Cutting-Edge Tech: Abridge's AI converts patient-clinician conversations into structured clinical notes in real-time, integrating seamlessly with EHRs. 🔹 Privacy First: The implementation includes rigorous testing and quality checks, with patient consent required. 🔹 Multilingual Capability: The tool is available in 14+ languages and over 50 specialties. 🔹 Industry Trend: This partnership signifies a growing adoption of AI in clinical documentation across major health systems. Key Takeaway: Kaiser Permanente's large-scale adoption of Abridge's AI technology could set a new standard for how healthcare systems leverage AI to improve patient care and clinician efficiency. #AIinHealthcare #HealthTech #FutureOfMedicine #HealthcareInnovation

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A successful systems design and delivery strategy begins by assuming that change is likely the only constant. Be adaptable, or fail.

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