101 결과
2025년 12월 2일 / AI
The new Data Commons extension for the Gemini CLI makes accessing public data easier. It allows users to ask complex, natural-language questions to query Data Commons' public datasets, grounding LLM responses in authoritative sources to reduce AI hallucinations. Data Commons is an organized library of public data from sources like the UN and World Bank. The extension enables instant data analysis, exploration, and integration with other data-related extensions.
2025년 11월 19일 / AI
The JAX AI Stack is a modular, industrial-grade, end-to-end machine learning platform built on the core JAX library, co-designed with Cloud TPUs. It features key components like JAX, Flax, Optax, and Orbax for foundational model development, plus an extended ecosystem for the full ML lifecycle and production. This integration provides a powerful, scalable foundation for AI development, delivering significant performance advantages.
2025년 11월 19일 / AI
Gemini 3 Pro Preview is introduced as a powerful, agentic model for complex, (semi)-autonomous workflows. New agentic features include `thinking_level` for reasoning control, Stateful Tool Use via Thought Signatures, and `media_resolution` for multimodal fidelity. It has Day 0 support for open-source frameworks like LangChain, AI SDK, LlamaIndex, Pydantic AI, and n8n. Best practices include simplifying prompts and keeping temperature at 1.0.
2025년 11월 19일 / AI
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.
2025년 11월 13일 / AI
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.
2025년 11월 13일 / AI
Google has launched the redesigned **Android AI Sample Catalog**, a dedicated, open-source application to inspire and educate Android developers on building AI-powered apps. It showcases examples using both on-device (Gemini Nano via ML Kit GenAI API) and Cloud models (via Firebase AI Logic SDK), including image generation with Imagen, on-device summarization, and a "Chat with Nano Banana" chatbot. The code is easy to copy and paste to help developers quickly start their own projects.
2025년 11월 7일 / AI
The new **User Simulation** feature in the Agent Development Kit (ADK) replaces rigid, brittle manual test scripts with dynamic, LLM-powered conversation generation. Developers define a high-level `conversation_plan`, and the simulator handles the multi-turn interaction to achieve the goal. This dramatically reduces test creation time, builds more resilient tests, and creates a reliable regression suite for AI agents.
2025년 11월 7일 / AI
The Agent Development Kit (ADK), an open-source, code-first toolkit for building powerful and sophisticated AI agents, now supports Go. ADK moves LLM orchestration and agent behavior directly into your code, giving you robust debugging, versioning, and deployment freedom. ADK for Go is idiomatic and performant, leveraging Go's strengths, and includes support for over 30+ databases and the Agent-to-Agent (A2A) protocol for collaborative multi-agent systems. Start building today!
2025년 11월 4일 / AI
Agent Garden is now available to all users to simplify AI agent creation and deployment using the Agent Development Kit (ADK). It provides curated agent samples, one-click deployment via Agent Starter Pack, and customization through Firebase Studio. It helps developers with complex business challenges and multi-agent workflows, with Renault Group cited as an early success story.
2025년 10월 30일 / AI
The blog post argues the request-response model fails for advanced multi-agent AI. It advocates for a real-time bidirectional streaming architecture, implemented by the Agent Development Kit (ADK). This streaming model enables true concurrency, natural interruptibility, and unified multimodal processing. ADK's core features are real-time I/O management, stateful sessions for agent handoffs, and streaming-native tools.
