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.

Imagen 4, Google's advanced text-to-image model, is now available in paid preview via the Gemini API and Google AI Studio, offering significant quality improvements, especially for text generation within images. The Imagen 4 family includes Imagen 4 for general tasks and Imagen 4 Ultra for high-precision prompt adherence, with all generated images featuring a non-visible SynthID watermark.

Gemini 2.5 Pro and Flash are transforming robotics by enhancing coding, reasoning, and multimodal capabilities, including spatial understanding. These models are used for semantic scene understanding, code generation for robot control, and building interactive applications with the Live API, with a strong emphasis on safety improvements and community applications.

Developers adapt LLMs like Gemma for diverse languages and cultural contexts, demonstrating AI's potential to bridge global communication gaps by addressing challenges like translating ancient texts, localizing mathematical understanding, and enhancing cultural sensitivity in lyric translation.

Google, along with Amazon and Cisco, announces the formation of the Agent2Agent Foundation under the Linux Foundation, establishing A2A as an industry standard for AI agent interoperability, fostering a diverse ecosystem, ensuring neutral governance, and accelerating secure innovation in AI applications.