Diffusion LLM a new model for language generation is faster than generative LLMs. """ We trained diffusion large language models that are up to 10x faster and cheaper than current LLMs, pushing the frontier of intelligence and speed for language model 1 We are announcing the Mercury family of diffusion large language models (dLLMs), a new generation of LLMs that push the frontier of fast, high-quality text generation. 2 Mercury is up to 10x faster than frontier speed-optimized LLMs. Our models run at over 1000 tokens/sec on NVIDIA H100s, a speed previously possible only using custom chips. 3 A code generation model, Mercury Coder, is available to test in a playground. We offer enterprise clients access to code and generalist models via an API and on-premise deployments. """ Speed Benchmarks in Image

(If you find this useful please like) This no-cost course, designed by Google ML researchers and engineers, explores key Generative AI technologies through theory, hands-on learning, and community engagement. Conclude this 5-day course with a practical GenAI challenge through a capstone project. Register for the Course Everyday, participants will receive the following in their inbox: - 📚 Daily AssignmentsThis includes newly published white papers, a companion podcast (generated by NotebookLM), and companion code labs in AI Studio. - 💬 Discord Discussion ThreadsKaggle’s Discord server will have a dedicated channel for focused discussion on the course. It's an excellent place to find further clarification, connect with other learners and get support from Google experts. - 🎥 Daily Livestream Seminars and AMAsWe're going live everyday on Kaggle's YouTube channel, where the authors and course contributors will dive deeper into the topics and answer your burning questions. Plus, we've got fun surprises in store to keep the learning engaging. - 🎯 Capstone ProjectApply your skills in a practical project for a chance to win swag, Kaggle badges, and recognition on Kaggle and Google’s social media. For more information, visit the event website here. (If you find this useful please like)

Hey everyone, I just completed a new course for you: "Data Science Accelerator". This course will be unlocked, together with "Building Applications with LLMs" at level 3. How to level up? Just interact with the group, get likes and comments, and watch your level go up!

I'm curious: do you prefer using pure Python / Code for workflow automations, or do you lean more towards platforms like Zapier and Make? If you have a strong preference, could you explain why? Which approach would you recommend for someone just starting out?

Hey everyone! As we’re heading towards the final month of 2024, I’ve been reflecting on what a whirlwind the past few months have been. Q3 and Q4 have been incredibly busy — between running Datalumina, managing client projects, seeing amazing growth in Data Freelancer, shipping the GenAI launchpad, moving into a new office, and onboarding the first customers for our new SaaS product, it’s been a ride. 🚀 On top of that, keeping up with the YouTube channel has been both a challenge and a joy. Honestly, it’s one of my favorite parts of what I do — helping you all learn, grow, and tackle data challenges with confidence. Seeing your feedback and progress keeps me motivated to keep creating. Now, I’m starting to map out my content plans for 2025, and I’d love to hear from YOU. What are you currently learning? What topics would you like me to cover next year? 🎥 Would you like to see more LLM-based tutorials? Deeper dives into machine learning workflows? Maybe even some freelancing tips for data professionals? Or something else entirely? Let me know in the comments — what are you most excited to learn in 2025? This is your chance to shape the direction of the content we create together. Thanks for being part of this community! 🙏🏻 — Dave