The author explores how Gemini Scheduled Actions represents a significant shift in Android automation by moving from rigid, trigger-based logic like Tasker to an intent-first architecture powered by Large Language Models. Unlike traditional tools that require programming knowledge and are prone to breaking when UI changes occur, Gemini understands natural language requests and manages complex workflows across devices via the cloud.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
An exploration of the Google Agent Development Kit (ADK), a modular open-source framework designed to streamline the creation, deployment, and orchestration of AI agents. While optimized for Gemini and the Google Cloud ecosystem via Vertex AI, the kit remains model-agnostic and supports multiple programming languages including Python, Go, Java, and TypeScript. The review highlights the toolkit's ability to handle multi-agent architectures, long-term memory, and tool integration through agent skills.
Key points:
* Support for diverse programming environments (Python, Go, Java, TypeScript).
* Integration with Vertex AI Agent Engine and Google Cloud Run.
* Built-in developer UI (ADK Web) for debugging, tracing, and evaluation.
* Use of the open agent skills format for expanding agent capabilities.
* Comparison against competitors like Amazon Bedrock AgentCore and LangChain.
Key points:
* Support for diverse programming environments (Python, Go, Java, TypeScript).
* Integration with Vertex AI Agent Engine and Google Cloud Run.
* Built-in developer UI (ADK Web) for debugging, tracing, and evaluation.
* Use of the open agent skills format for expanding agent capabilities.
* Comparison against competitors like Amazon Bedrock AgentCore and LangChain.
A Python package designed to provide production-ready templates for Generative AI agents on Google Cloud. It allows developers to focus on agent logic by automating the surrounding infrastructure, including CI/CD pipelines, observability, security, and deployment via Cloud Run or Agent Engine.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Google's recent Pixel Drop introduces a groundbreaking, albeit unusual, screen automation feature for Gemini. Unlike previous assistants limited by strict APIs, Gemini uses visual reasoning to interact with third-party applications directly. By reading on-screen elements like menus and text fields, the AI can perform complex tasks such as ordering food or booking rides within a secure sandbox. While this offers significant benefits for multitasking and accessibility, it also raises critical questions regarding privacy, the stability of automation when app UIs change, and the potential disruption of the ad-supported economy. Currently, this beta feature is limited to high-end devices like the Pixel 10 and Galaxy S26 series in select regions.
Gemini is an AI assistant integrated into Pixel phones to boost productivity. It streamlines daily life by automating tasks (like food and ride orders), managing Gmail and Calendar, planning travel via Maps, and organizing schedules through task apps and Pixel Screenshots. Advanced features are available on the Pixel 10 Pro via the Google One AI Premium Plan.
Google has released a new command-line interface for Google Workspace apps, designed to make it easier for AI agents like OpenClaw to interface with Google apps like Docs, Drive, and Gmail. The tool offers over 100 Agent Skills to simplify agent actions and supports integrations with other AI agents beyond OpenClaw. While published by Google, it's not an officially supported product, so use it at your own risk.
This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
This article presents findings from a survey of over 900 software engineers regarding their use of AI tools. Key findings include the dominance of Claude Code, the mainstream adoption of AI in software engineering (95% weekly usage), the increasing use of AI agents (especially among staff+ engineers), and the influence of company size on tool choice. The survey also reveals which tools engineers love, with Claude Code being particularly favored, and provides demographic information about the respondents. A longer, 35-page report with additional details is available for full subscribers.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
We’ve been experimenting with using large language models (LLMs) to assist in hardware design, and we’re excited to share our first project: the Deep Think PCB. This board is designed to be a versatile platform for experimenting with LLMs at the edge, and it’s built using a combination of open-source hardware and software. We detail the process of using Gemini to generate the schematic and PCB layout, the challenges we faced, and the lessons we learned. It's a fascinating look at the future of hardware design!
