51 results
APRIL 1, 2026 / AI
The Agent Development Kit (ADK) SkillToolset introduces a "progressive disclosure" architecture that allows AI agents to load domain expertise on demand, reducing token usage by up to 90% compared to traditional monolithic prompts. Through four distinct patterns—ranging from simple inline checklists to "skill factories" where agents write their own code—the system enables agents to dynamically expand their capabilities at runtime using the universal agentskills.io specification. This modular approach ensures that complex instructions and external resources are only accessed when relevant, creating a scalable and self-extending framework for modern AI development.
MARCH 31, 2026 / AI
The newly introduced continuous checkpointing feature in Orbax and MaxText is designed to optimize the balance between reliability and performance during model training, addressing issues with conventional fixed-frequency checkpointing. Unlike fixed intervals—which can either compromise reliability or bottleneck performance—continuous checkpointing maximizes I/O bandwidth and minimizes failure risk by asynchronously initiating a new save operation only after the previous one successfully completes. Benchmarks demonstrate that this approach significantly reduces checkpoint intervals and results in substantial resource conservation, especially in large-scale training jobs where mean-time-between-failure (MTBF) is short.
MARCH 25, 2026 / AI
To bridge the gap between static model knowledge and rapidly evolving software practices, Google DeepMind developed a "Gemini API developer skill" that provides agents with live documentation and SDK guidance. Evaluation results show a massive performance boost, with the gemini-3.1-pro-preview model jumping from a 28.2% to a 96.6% success rate when equipped with the skill. This lightweight approach demonstrates how giving models strong reasoning capabilities and access to a "source of truth" can effectively eliminate outdated coding patterns.
MARCH 23, 2026 / AI
This blog post introduces a workflow for extracting high-quality data from complex, unstructured documents by combining LlamaParse with Gemini 3.1 models. It demonstrates an event-driven architecture that uses Gemini 3.1 Pro for agentic parsing of dense financial tables and Gemini 3.1 Flash for cost-effective summarization. By following the provided tutorial, developers can build a personal finance assistant capable of transforming messy brokerage statements into structured, human-readable insights.
MARCH 18, 2026 / AI
This blog post introduces a suite of six protocols, such as MCP and A2A, designed to eliminate custom integration code by standardizing how AI agents access data and communicate. Using a "kitchen manager" agent as a practical example, it demonstrates how these tools handle complex tasks like real-time inventory checks, wholesale commerce via UCP, and secure payment authorization through AP2. By leveraging the Agent Development Kit (ADK), developers can also implement A2UI and AG-UI to deliver interactive dashboards and seamless streaming interfaces to users.
FEB. 27, 2026 / AI
Agent Development Kit (ADK) now supports a robust ecosystem of third-party tools and integrations. Connect your agents to GitHub, Notion, Hugging Face, and more to build capable, real-world applications.
FEB. 19, 2026 / Gemini
The Android XR team is using Gemini's Canvas feature to make creating immersive extended reality (XR) experiences more accessible. This allows developers to rapidly prototype interactive 3D environments and models on a Samsung Galaxy XR headset using simple creative prompts.
FEB. 13, 2026 / AI
Conductor for the Gemini CLI has introduced a new Automated Review feature designed to verify the quality and accuracy of AI-generated code. This update addresses the challenge of validating agentic development by automatically checking implementations against original plans, enforcing style guides, and identifying security risks or bugs. by incorporating test-suite validation and providing actionable reports, Conductor helps developers ensure that their AI agents deliver safe, predictable, and architecturally sound code before it is finalized.
JAN. 5, 2026 / AI
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.
DEC. 19, 2025 / AI
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.
