Gists
Introduction The Gemini API recently introduced the URL context tool, a feature designed to allow the model to directly fetch and utilize content from specified URLs to ground its responses. Ref
This report provides a practical demonstration of this tool’s capabilities. We will investigate its impact on two critical aspects of AI model interaction: the accuracy of the generated response and the total token consumption, which directly affects API costs.
Description I am thrilled to announce a significant update to the Taking Advantage of Web Apps with Google Apps Script repository! Now at Version 2.0.0, this comprehensive guide has been extensively revised and expanded to provide unparalleled insights and practical guidance for developers leveraging Google Apps Script Web Apps.
This major overhaul introduces a wealth of new and enhanced content, designed to empower you with a deeper understanding and more advanced capabilities.
Gists
Overview Google Sheets now supports managing smart chips via its API, enabling retrieval and placement with Apps Script and other languages.
Description Smart chips in Google Sheets and Docs are a powerful feature designed to enhance collaboration and information sharing. While there historically haven’t been built-in Google Apps Script methods to manage these chips, a workaround was previously developed to address this limitation (Ref).
However, Google has recently introduced direct methods for managing smart chips within the Google Sheets API (Ref).
Gists
Abstract A new unified Google Apps Script now deploys both Model Context Protocol (MCP) and Agent2Agent (A2A) networks as a single server, streamlining AI model integration for Google Workspace users.
Introduction The rapid growth of generative AI has led to increasing integration between AI models, exemplified by protocols like the Model Context Protocol (MCP) and Agent2Agent (A2A) Protocol. Recently, I released MCPApp and A2AApp, which establish the MCP and A2A networks using Google Apps Script.
Gists
Abstract This article announces that the Gemini API’s Python client library now supports “growing image” generation, a feature previously unavailable. Sample scripts for Python and Node.js are provided to demonstrate this new capability.
Introduction I recently published an article, “Generate Growing Images using Gemini API,” which detailed a method for progressively generating images. At the time of publication, the official Python client library for the Gemini API lacked the necessary functionality to fully implement this feature, preventing Python users from easily replicating the “growing image” effect.