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  • NOV. 24, 2025 / Mobile

    Unlocking Peak Performance on Qualcomm NPU with LiteRT

    LiteRT's new Qualcomm AI Engine Direct (QNN) Accelerator unlocks dedicated NPU power for on-device GenAI on Android. It offers a unified mobile deployment workflow, SOTA performance (up to 100x speedup over CPU), and full model delegation. This enables smooth, real-time AI experiences, with FastVLM-0.5B achieving over 11,000 tokens/sec prefill on Snapdragon 8 Elite Gen 5 NPU.

    Train a GPT2 model with JAX on TPU for free
  • NOV. 19, 2025 / AI

    Building with Gemini 3 in Jules

    Jules, an always-on, multi-step software development agent, now features Gemini 3, offering clearer reasoning and better reliability. Recent improvements include parallel CLI runs, a stable API, and safer Git handling. Upcoming features include directory attachment without GitHub and automatic PR creation. Jules aims to reduce software writing overhead so developers can focus on building.

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  • NOV. 13, 2025 / AI

    Introducing Metrax: performant, efficient, and robust model evaluation metrics in JAX

    Metrax is a high-performance JAX-based metrics library developed by Google. It standardizes model evaluation by offering robust, efficient metrics for classification, NLP, and vision, eliminating manual re-implementation after migrating from TensorFlow. Key strengths include parallel computation of "at K" metrics (e.g., PrecisionAtK) for multiple K values and strong integration with the JAX AI Stack, leveraging JAX's performance features. It is open-source on GitHub.

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  • OCT. 7, 2025 / AI

    Building High-Performance Data Pipelines with Grain and ArrayRecord

    To avoid data bottlenecks when training large models, this guide introduces Grain and ArrayRecord for building high-performance data pipelines.

    The Agentic experience: Is MCP the right tool for your AI future?
  • SEPT. 26, 2025 / AI

    Apigee Operator for Kubernetes and GKE Inference Gateway integration for Auth and AI/LLM policies

    The GKE Inference Gateway now integrates with Apigee, allowing enterprises to unify AI serving and API governance. This enables GKE users to leverage Apigee's API management, security, and monetization features for their AI workloads, including API keys, quotas, rate limiting, and Model Armor security.

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  • SEPT. 24, 2025 / AI

    Introducing the Data Commons Model Context Protocol (MCP) Server: Streamlining Public Data Access for AI Developers

    Data Commons announces the availability of its MCP Server, which is a major milestone in making all of Data Commons’ vast public datasets instantly accessible and actionable for AI developers worldwide.

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  • SEPT. 24, 2025 / Cloud

    Google Colab Adds More Back to School Improvements!

    The new Colab features simplify and enhance notebook-based class materials. Instructors can now freeze runtime versions, and seamlessly present and copy notebooks. This improves reproducibility, stability, and shareability, making Colab ideal for researchers, educators, and developers. Slideshow mode and URL linking are also enhanced.

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  • SEPT. 16, 2025 / AI

    ADK for Java opening up to third-party language models via LangChain4j integration

    The Agent Development Kit (ADK) for Java 0.2.0 now integrates with LangChain4j, expanding LLM support to include third-party and local models like Gemma and Qwen. This release also enhances tooling with instance-based FunctionTools, improved async support, better loop control, and advanced agent logic with chained callbacks and new memory management.

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  • SEPT. 9, 2025 / Mobile

    Google AI Edge Gallery: Now with audio and on Google Play

    Google AI Edge has expanded the Gemma 3n preview to include audio support. Users can play with it on their own mobile phone using the Google AI Edge Gallery, which is now available in Open Beta on Play Store.

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  • SEPT. 9, 2025 / AI

    Beyond backpropagation: JAX's symbolic power unlocks new frontiers in scientific computing

    JAX, a framework known for large-scale AI model development, is proving to be a powerful tool in scientific computing, particularly for solving complex Partial Differential Equations (PDEs), now being leveraged by researchers to achieve significant speed-ups and memory reductions in solving high-order PDEs and demonstrating its potential to unlock new frontiers in scientific discovery.

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