47 results
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how giving models strong reasoning capabilities and access to a "source of truth" can effectively eliminate outdated coding patterns.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 17, 2026 / Cloud
When you’re prototyping locally with AI agents like Gemini CLI, Claude Code, or your own agent, thei...
MARCH 11, 2026 / AI
Gemini CLI now features Plan Mode, a read-only environment that allows the AI to analyze complex codebases and map out architectural changes without the risk of accidental execution. By leveraging the new ask_user tool and expanded Model Context Protocol (MCP) support, developers can collaboratively refine strategies and pull in external data before committing to implementation.
MARCH 10, 2026 / AI
The Gemini Code Assist team has introduced a suite of updates focused on streamlining the core coding workflow through high-velocity tools like Agent Mode with Auto Approve and Inline Diff Views. These enhancements, along with new features for precise context management and custom commands, aim to transform the AI from a general assistant into a highly tailored, seamless collaborator that adapts to your specific development style.
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.
FEB. 11, 2026 / AI
To simplify the user experience and prevent startup failures, the Gemini CLI has introduced structured extension settings that eliminate the need for manual environment variable configuration. This update enables extensions to automatically prompt users for required details during installation and securely stores sensitive information, such as API keys, directly in the system keychain. Users can now easily manage and override these configurations globally or per project using the new Gemini extensions config command.
FEB. 9, 2026 / AI
Data Commons has launched a free, hosted Model Context Protocol (MCP) service on Google Cloud Platform, eliminating the need for users to manage complex local server installations. This update simplifies connecting AI agents and the Gemini CLI to Data Commons, allowing Google to handle security, updates, and resource management while users query data natively.
FEB. 4, 2026 / AI
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.
JAN. 28, 2026 / AI
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.
DEC. 17, 2025 / AI
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.
