THE BIG ONE
Google AI has just released Gemini 3.5 Transcribe, a speech-to-text model that boasts a mere 2.6% average word error rate (WER) across 85+ languages. This new model comes with two separate endpoints: one for streaming, which delivers near-instant responses, and another for batch processing, optimizing transcription costs and efficiency. This is a game-changer for applications needing real-time language processing, such as customer service or live event transcription. If you're building an app that relies on accurate and fast speech recognition, integrating Gemini 3.5 should be on your roadmap. Check out the full details here.
QUICK HITS
1. GlucoFM: Foundation model for continuous glucose monitoring
Google's new GlucoFM model is tailored for continuous glucose monitoring. It can accurately predict glucose levels, which is essential for developing better diabetes management solutions. With this model, you'll be able to create applications that provide real-time insights, helping users manage their health more effectively. Learn more.
2. Batch write and discover records in Amazon SageMaker Feature Store
SageMaker Feature Store now supports batch writing of up to 25 records across multiple feature groups in one API call. This is a huge time saver and enhances efficiency for developers working with large datasets. You can streamline your feature management process and reduce the complexity of your data pipelines. Find out how.
3. AgentHands: Generating interactive hand gestures for XR
Google's AgentHands is a tool designed to create realistic hand gestures for spatially grounded agent conversations in extended reality (XR). This could significantly enhance user interactions in VR and AR applications, making conversations feel more natural and engaging. If you’re developing XR experiences, incorporating this could elevate your project. Explore the potential.
4. How Decathlon runs demand forecasting at scale
Decathlon has implemented Chronos-2 on AWS for demand forecasting across thousands of products worldwide. They’ve optimized their inventory management and can now predict future sales trends more accurately. Learning from their approach could help you refine your own forecasting models, especially if you're in retail or e-commerce. Read the case study.
5. Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio
In a recent tutorial, NVIDIA shows how to set up an ensemble weather forecasting workflow using Earth2Studio. This setup can provide more accurate weather predictions by combining multiple models. If you're interested in environmental data or climate modeling, consider trying this approach for better accuracy. Check it out.
ONE THING TO TRY
If you’re looking to enhance your AI applications, consider integrating the new Gemini 3.5 Transcribe into your workflow. Its dual endpoints can save you time and improve efficiency. Experiment with real-time transcription for your next project and see how it enhances user experience.
SIGN-OFF
That’s a wrap for this week! I’m excited to see how you leverage these new tools and models in your projects. If you have any questions or want to share your experiences, hit reply!