Tag: Google

Google has announced the new Google Pay & Wallet Developer MCP server, an open-standard tool designed to securely connect AI development assistants and IDEs with real-time API and account context. The server allows developers to remain within their development environment to search official documentation, validate Wallet pass definitions, check integration status, and manage merchant accounts. Ultimately, this integration aims to reduce friction and accelerate development workflows by minimizing context switching and providing up-to-date, grounded AI support.

The Google Tensor ML SDK is graduating to its Beta phase, allowing developers to build and deploy high-performance machine learning models directly onto the TPU of Google Pixel 10 devices. By integrating with LiteRT, Google's edge deployment framework, the SDK provides a unified workflow for developers to convert, compile, and run PyTorch or TFLite models with robust fallback options. Additionally, a new model garden offers over 100 classic and generative AI models, including Gemma 3, enabling low-latency, private features like speech recognition, computer vision, and text generation.

LiteRT is a production-ready framework designed to help mobile developers unlock the power of Neural Processing Units (NPUs), overcoming the performance and battery limitations of traditional CPU or GPU processing. By providing a unified API that abstracts away hardware complexities, it allows industry leaders like Google Meet and Epic Games to deploy sophisticated AI models for real-time video, animation, and speech recognition with significantly higher efficiency. The platform further supports developers through benchmarking tools and cross-platform compatibility, enabling seamless AI deployment across mobile devices, AI PCs, and industrial IoT hardware.

Google Cloud has introduced a high-performance integration that connects Rapid Storage directly to PyTorch via the fsspec interface to eliminate AI training bottlenecks. By utilizing Google’s Colossus architecture and bidirectional gRPC streaming, the solution offers up to 15 TiB/s aggregate throughput and significant reductions in latency. These improvements allow developers to speed up total training time by 23% with zero code changes required beyond updating the storage bucket type.

Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for tasks like agentic RAG, visual search, and content moderation. By supporting over 100 languages and offering features like task-specific prefixes and Matryoshka dimensionality reduction, the model provides a highly efficient and accurate foundation for building complex AI agents.

Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting. By "painting" entire blocks of candidate tokens in a single forward pass rather than predicting them one-by-one, the system achieved average speedups of 3.13x, with peak performance nearly doubling that of existing methods like EAGLE-3. This open-source integration into the vLLM ecosystem optimizes TPU hardware by leveraging "free" parallel verification and high-quality draft predictions for complex reasoning tasks.

How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during "idle time" or server restarts. By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that "sleep" during pauses and wake up to resume complex tasks with high reasoning accuracy.

Genkit is an open-source framework designed to help developers build production-ready, agentic AI applications using TypeScript, Go, Dart, and Python. The framework utilizes a powerful middleware system that intercepts generation calls to inject custom behaviors like retries, model fallbacks, and human-in-the-loop tool approvals. By attaching hooks at the generate, model, and tool layers, developers can ensure high reliability and deterministic control over model outputs. Furthermore, Genkit allows for the creation and stacking of custom middleware, all of which can be inspected and debugged through a dedicated Developer UI.

Integration of Arm Scalable Matrix Extension 2 (SME2) and the Google AI Edge software stack enables high-performance, on-device generative AI by turning the CPU into a powerful matrix-compute accelerator. Using Stability AI’s "stable-audio-open-small" model as a case study, it outlines a streamlined "Convert, Optimize, and Deploy" pipeline that utilizes LiteRT, XNNPACK, and KleidiAI to automate hardware acceleration. The resulting implementation achieves over a 2x speedup in audio generation and a 4x reduction in memory usage while maintaining high audio quality on Arm-powered mobile devices and laptops.

The Google AI Edge Gallery app has expanded its on-device AI capabilities by introducing experimental support for the open-source Model Context Protocol (MCP) on Android, allowing Gemma 4 to coordinate complex tasks across external data sources like Google Workspace and Google Maps. To enable more proactive and persistent user interactions, the update adds a "Schedule Notification" skill for automating routines and a persistent chat history feature that restores long session contexts nearly instantly. Driven by an open-source toolkit, the platform encourages community developers to build and share custom utility-focused workflows, prompt configurations, and tool integrations via its GitHub repository.