GenAI Processors is a new open-source Python library from Google DeepMind designed to simplify the development of AI applications, especially those handling multimodal input and requiring real-time responsiveness, by providing a consistent "Processor" interface for all steps from input handling to model calls and output processing, for seamless chaining and concurrent execution.

Updates in Firebase Studio include new Agent modes, foundational support for the Model Context Protocol (MCP), and Gemini CLI integration, all designed to redefine AI-assisted development allow developers to create full-stack applications from a single prompt and integrate powerful AI capabilities directly into their workflow.

T5Gemma is a new family of encoder-decoder LLMs developed by converting and adapting pretrained decoder-only models based on the Gemma 2 framework, offering superior performance and efficiency compared to its decoder-only counterparts, particularly for tasks requiring deep input understanding, like summarization and translation.

The new batch mode in the Gemini API is designed for high-throughput, non-latency-critical AI workloads, simplifying large jobs by handling scheduling and processing, and making tasks like data analysis, bulk content creation, and model evaluation more cost-effective and scalable, so developers can process large volumes of data efficiently.

Google has released a new Python client library for Data Commons – an open-source knowledge graph that unifies public statistical data, and enhances how data developers can leverage Data Commons by offering improved features, support for custom instances, and easier access to a vast array of statistical variables – developed with contributions from The ONE Campaign.

The Gemma 3n model has been fully released, building on the success of previous Gemma models and bringing advanced on-device multimodal capabilities to edge devices with unprecedented performance. Explore Gemma 3n's innovations, including its mobile-first architecture, MatFormer technology, Per-Layer Embeddings, KV Cache Sharing, and new audio and MobileNet-V5 vision encoders, and how developers can start building with it today.

A research prototype simulating a neural operating system generates UI in real-time adapting to user interactions with Gemini 2.5 Flash-Lite, using interaction tracing for contextual awareness, streaming the UI for responsiveness, and achieving statefulness with an in-memory UI graph.

KerasHub enables users to mix and match model architectures and weights across different machine learning frameworks, allowing checkpoints from sources like Hugging Face Hub (including those created with PyTorch) to be loaded into Keras models for use with JAX, PyTorch, or TensorFlow. This flexibility means you can leverage a vast array of community fine-tuned models while maintaining full control over your chosen backend framework.

KerasHub enables users to mix and match model architectures and weights across different machine learning frameworks, allowing checkpoints from sources like Hugging Face Hub (including those created with PyTorch) to be loaded into Keras models for use with JAX, PyTorch, or TensorFlow. This flexibility means you can leverage a vast array of community fine-tuned models while maintaining full control over your chosen backend framework.

The new AI-first Google Colab enhances productivity with improvements powered by features like iterative querying for conversational coding, a next-generation Data Science Agent for autonomous workflows, and effortless code transformation. Early adopters report a dramatic productivity boost, accelerating ML projects, debugging code faster, and effortlessly creating high-quality visualizations.