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  • SEPT. 16, 2025 / AI

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

    The recent 0.2.0 release of Google’s Agent Development Kit (ADK) for Java adds an integration with t...

    adk-langchain4j
  • SEPT. 9, 2025 / AI

    A2A Extensions: Empowering Custom Agent Functionality

    A2A Extensions provide a flexible way to add custom functionalities to agent-to-agent communication, going beyond the core A2A protocol. They enable specialized features and are openly defined and implemented.

    GfD_evergreen_meta
  • SEPT. 9, 2025 / AI

    Announcing Genkit Go 1.0 and Enhanced AI-Assisted Development

    We are launching 1.0 stable release of Genkit Go, empowering Go developers to build performant, production-ready AI-powered applications with Genkit. Recent enhancements include support for integrating and building MCP tools, expanding third-party model provider support, and production AI monitoring with Firebase. Additionally, we are announcing a new feature in the Genkit CLI to provide AI development tools, like the Gemini CLI and Cursor, with the latest knowledge of Genkit - supercharging Genkit development experience when using AI assistance.

    Genkit-Go-1.0-BlogMeta
  • 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.

    JAX's-Symbolic-Power-Blog-Meta
  • SEPT. 4, 2025 / AI

    From Fine-Tuning to Production: A Scalable Embedding Pipeline with Dataflow

    Learn how to use Google's EmbeddingGemma, an efficient open model, with Google Cloud's Dataflow and vector databases like AlloyDB to build scalable, real-time knowledge ingestion pipelines.

    EG+Dataflow_Metadatal
  • AUG. 28, 2025 / AI

    How to prompt Gemini 2.5 Flash Image Generation for the best results

    Detailed prompting techniques and best practices for various applications, including photorealistic scenes, stylized illustrations, product mockups, and more using Google's newly released Gemini 2.5 Flash Image; a natively multimodal model capable of generating, editing, and composing images using text, supporting capabilities like text-to-image, image editing, style transfer, and multi-image composition.

    Gemini 2.5 Flash Image
  • AUG. 21, 2025 / Gemini

    What's new in Gemini Code Assist

    Gemini Code Assist's Agent Mode, now available in VS Code (Preview) and IntelliJ (Stable), streamlines complex coding tasks by proposing detailed plans for user review and approval. This intelligent, collaborative approach, enhanced with features like inline diffs and persistent chat history, aims to boost developer productivity and efficiency.

    New in Gemini Code Assist: Agent Mode more widely available, IDE improvements and Gemini CLI updates
  • AUG. 13, 2025 / Gemini

    Gemini CLI + VS Code: Native diffing and context-aware workflows

    The latest Gemini CLI update provides a deep IDE integration within VS Code for intelligent, context-aware suggestions, and native in-editor diffing, allowing developers to review and modify proposed changes directly within the diff view for a more efficient workflow.

    Gemini CLI + VS Code integration
  • AUG. 12, 2025 / Kaggle

    Train a GPT2 model with JAX on TPU for free

    Build and train a GPT2 model from scratch using JAX on Google TPUs, with a complete Python notebook for free-tier Colab or Kaggle. Learn how to define a hardware mesh, partition model parameters and input data for data parallelism, and optimize the model training process.

    Train a GPT2 model with JAX on TPU for free
  • JULY 24, 2025 / AI

    The agentic experience: Is MCP the right tool for your AI future?

    Apigee helps enterprises integrate large language models (LLMs) into existing API ecosystems securely and scalably, addressing challenges like authentication and authorization not fully covered by the evolving Model Context Protocol (MCP), and offering an open-source MCP server example that demonstrates how to implement enterprise-ready API security for AI agents.

    The Agentic experience: Is MCP the right tool for your AI future?