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  • FEB. 4, 2026 / AI

    Introducing the Developer Knowledge API and MCP Server

    Google is launching the Developer Knowledge API and MCP Server in public preview. This new toolset provides a canonical, machine-readable way for AI assistants and agentic platforms to search and retrieve up-to-date documentation across Firebase, Google Cloud, Android, and more. By using the official MCP server, developers can connect tools directly to Google’s documentation corpus, ensuring that AI-generated code and guidance are based on authoritative, real-time context.

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  • FEB. 3, 2026 / AI

    Easy FunctionGemma finetuning with Tunix on Google TPUs

    Finetuning the FunctionGemma model is made fast and easy using the lightweight JAX-based Tunix library on Google TPUs, a process demonstrated here using LoRA for supervised finetuning. This approach delivers significant accuracy improvements with high TPU efficiency, culminating in a model ready for deployment.

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  • JAN. 29, 2026 / DeepMind

    Beyond the Chatbot: A Blueprint for Trustable AI

    At Thunderhill Raceway Park, a team of Google Developer Experts (GDEs) put a new "Trustable AI Framework" to the test. Here is how they used GCP, Gemini and Antigravity to turn high-velocity racing into a masterclass for agentic architecture.

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  • JAN. 28, 2026 / Mobile

    LiteRT: The Universal Framework for On-Device AI

    LiteRT, the evolution of TFLite, is now the universal framework for on-device AI. It delivers up to 1.4x faster GPU, new NPU support, and streamlined GenAI deployment for models like Gemma.

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  • JAN. 28, 2026 / AI

    Tailor Gemini CLI to your workflow with hooks

    New Gemini CLI hooks (v0.26.0+) let you tailor the agentic loop. Add context, enforce policies, and block secrets with custom scripts that run at predefined points in your workflow.

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  • JAN. 16, 2026 / AI

    A Guide to Fine-Tuning FunctionGemma

    FunctionGemma is a specialized AI model for function calling. This post explains why fine-tuning is key to resolving tool selection ambiguity (e.g., internal vs. Google search) and achieving ultra-specialization, transforming it into a strict, enterprise-compliant agent. A case study demonstrates the improved logic. It also introduces the "FunctionGemma Tuning Lab," a no-code demo on Hugging Face Spaces, which streamlines the entire fine-tuning process for developers.

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  • JAN. 11, 2026 / AI

    Under the Hood: Universal Commerce Protocol (UCP)

    The Universal Commerce Protocol (UCP) is a new, open-source standard for agentic commerce, co-developed by Google and industry leaders. It establishes a common, secure language to connect consumer surfaces (like Gemini and AI Mode in Search) with business backends, enabling seamless shopping from product discovery to purchase. UCP simplifies integration for businesses, supports various payment providers, and is designed to power the next generation of conversational commerce experiences.

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  • JAN. 5, 2026 / AI

    A Developer's Guide to Debugging JAX on Cloud TPUs: Essential Tools and Techniques

    A practical guide to debugging and profiling JAX on Cloud TPUs. It outlines core components (libtpu, JAX/jaxlib) and essential techniques. Tools covered include: Verbose Logging (via libtpu env vars), TPU Monitoring Library for performance metrics, tpu-info for real-time utilization, XLA HLO Dumps for compiler debugging, and the XProf suite for in-depth performance analysis.

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  • DEC. 19, 2025 / AI

    Real-World Agent Examples with Gemini 3

    Gemini 3 is powering the next generation of reliable, production-ready AI agents. This post highlights 6 open-source framework collaborations (ADK, Agno, Browser Use, Eigent, Letta, mem0), demonstrating practical agentic workflows for tasks like deep search, multi-agent systems, browser and enterprise automation, and stateful agents with advanced memory. Clone the examples and start building today.

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  • DEC. 17, 2025 / AI

    Gemini 3 Flash is now available in Gemini CLI

    Gemini 3 Flash is now available in Gemini CLI. It delivers Pro-grade coding performance with low latency and a lower cost, matching Gemini 3 Pro's SWE-bench Verified score of 76%. It significantly outperforms 2.5 Pro, improving auto-routing and agentic coding. It's ideal for high-frequency development tasks, handling complex code generation, large context windows (like processing 1,000 comment pull requests), and generating load-testing scripts quickly and reliably.

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