The article discusses how integrating Google's Gemini AI could significantly improve Google Keep's functionality, turning it into a more powerful note-taking and productivity tool. It details potential features like AI-powered summaries, improved note creation with typo correction, audio note enhancements with speaker detection, smart Q&A from tagged notes, and seamless integration with Google Calendar.
Google has introduced LangExtract, an open-source Python library designed to help developers extract structured information from unstructured text using large language models such as the Gemini models. The library simplifies the process of converting free-form text into structured data, offering features like controlled generation, text chunking, parallel processing, and integration with various LLMs.
Google is integrating Gemini Gems into Workspace apps like Docs, Sheets, and Gmail, allowing users to access customizable AI chatbots directly within these applications.
Google Sheets now allows users to generate text, summarize information, and categorize data using Gemini AI directly in cells. The feature supports text generation, summarization, categorization, and sentiment analysis with optional data ranges.
This post explores how developers can leverage Gemini 2.5 to build sophisticated robotics applications, focusing on semantic scene understanding, spatial reasoning with code generation, and interactive robotics applications using the Live API. It also highlights safety measures and current applications by trusted testers.
Google today announced that the SDK for its Gemini models will natively support the Model Context Protocol from Anthropic. This move aims to simplify the connection between AI agents and data sources, aligning with the growing popularity of MCP and complementing Google's own Agent2Agent protocol. The company also plans to ease deployment of MCP servers and hosted tools for AI agents.
A summary of a workshop presented at PyCon US on building software with LLMs, covering setup, prompting, building tools (text-to-SQL, structured data extraction, semantic search/RAG), tool usage, and security considerations like prompt injection. It also discusses the current LLM landscape, including models from OpenAI, Gemini, Anthropic, and open-weight alternatives.
Google's AI function brings Gemini-powered language models right into your spreadsheet cells without any add-ons. With it, you can generate fresh text, summarize blocks of data, categorize entries, or even guess sentiments—all by typing a simple formula.
The article provides examples such as:
- *sentiment analysis* ```=AI("Is this customer feedback positive, negative, or neutral?", A2)```
- *data categorization* `=AI("Classify this expense as Travel, Office, or Other", D3)`
- *simple calculations* `=AI("Add the numbers in these cells", A1:A5)`
The article provides examples such as:
- *sentiment analysis* ```=AI("Is this customer feedback positive, negative, or neutral?", A2)```
- *data categorization* `=AI("Classify this expense as Travel, Office, or Other", D3)`
- *simple calculations* `=AI("Add the numbers in these cells", A1:A5)`
Google will host a separate event, 'The Android Show: I/O Edition,' on May 13th to discuss Android updates, a week before Google I/O. This suggests Google I/O will focus more on Gemini and other AI efforts. Android will still be present at I/O, but it's no longer the primary focus.
