38 results
SEPT. 26, 2025 / AI
No AI/Agents without APIs!Many users interact with generative AI daily without realizing the crucial...
SEPT. 26, 2025 / AI
Delight users by combining ADK Agents with Fancy Frontends using AG-UIFor developers building genera...
SEPT. 24, 2025 / AI
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.
SEPT. 22, 2025 / AI
Gemini CLI now seamlessly integrates with FastMCP, Python's leading library for building MCP servers. We’re thrilled to announce this integration between two open-source projects that empowers you to effortlessly connect your custom MCP tools and prompts, directly to Gemini CLI!
SEPT. 10, 2025 / AI
Batch API now supports Embeddings and OpenAI CompatibilityToday we are extending the Gemini Batch AP...
SEPT. 9, 2025 / AI
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.
AUG. 12, 2025 / Google Labs
Jules' critic functionality addresses potential issues like subtle bugs and missed edge cases in AI-generated code by acting as a peer reviewer within the generation process. This "critic-augmented generation" means proposed code changes undergo adversarial review, allowing Jules to improve its output and ultimately deliver higher-quality, pre-reviewed code.
JULY 30, 2025 / Gemini
The Gemini Embedding model enhances AI applications, particularly through context engineering, which is being successfully adopted by various organizations across industries to power context-aware systems, leading to significant improvements in performance, accuracy, and efficiency.
JUNE 26, 2025 / AI
Google has released a new Python client library for Data Commons – an open-source knowledge graph that unifies public statistical data, and enhances how data developers can leverage Data Commons by offering improved features, support for custom instances, and easier access to a vast array of statistical variables – developed with contributions from The ONE Campaign.
JUNE 24, 2025 / Kaggle
KerasHub enables users to mix and match model architectures and weights across different machine learning frameworks, allowing checkpoints from sources like Hugging Face Hub (including those created with PyTorch) to be loaded into Keras models for use with JAX, PyTorch, or TensorFlow. This flexibility means you can leverage a vast array of community fine-tuned models while maintaining full control over your chosen backend framework.
