bebechien writes about DinoDesk AI, a LEGO dino desk companion built on a Raspberry Pi that pairs a local Gemma 4 model (via LM Studio) with cloud Gemini Flash through a unified OpenAI-compatible gateway, giving users a camera-free, privacy-first chat robot with three switching modes (local, cloud, and auto-hybrid routing). The physical body uses LEGO Technic lever mechanisms for neck and tail movement, a Pimoroni Pirate Audio shield for the 1.3" LCD eyes and 8-bit I2S beeps, and a 5-state finite state machine to coordinate expressions, sound, and motor action across Sleeping, Idle, Listening, Thinking, and Speaking states.
- Auto-hybrid mode uses a complexity classifier that detects multi-step reasoning keywords like "explain," "compare," and "write code" to transparently escalate prompts to the cloud engine
- The project is open-sourced at github.com/google-gemma/dinodesk-ai-companion
- Full voice chat is still a work in progress; current interaction is triggered by a physical red push button
- A commenter noted that absence of a camera does not guarantee voice data stays local, and suggested an explicit retention boundary for audio transcripts would strengthen the privacy claim
- Auto-hybrid mode uses a complexity classifier that detects multi-step reasoning keywords like "explain," "compare," and "write code" to transparently escalate prompts to the cloud engine
- The project is open-sourced at github.com/google-gemma/dinodesk-ai-companion
- Full voice chat is still a work in progress; current interaction is triggered by a physical red push button
- A commenter noted that absence of a camera does not guarantee voice data stays local, and suggested an explicit retention boundary for audio transcripts would strengthen the privacy claim
TOI Tech Desk writes that Google is moving its roughly 90-person AI responsibility team out of Google DeepMind and into Google's global affairs organisation (which handles lobbying and public policy), effective in September, as part of a broader reorganisation pulling DeepMind into a central "product area" structure. Team leader Helen King, a VP at DeepMind, told employees in an internal email that the team would retain access to DeepMind researchers, computing resources, and head count, though some staff worry the relocation could weaken their ability to independently assess emerging risks from frontier model development.
- The team tests Google's models for chemical, biological, radiological, and nuclear risks, and studies the psychological effects of chatbot interactions on users
- King noted that Demis Hassabis "still cares a lot about Responsible AI and is still planning to be involved"
- HR and policy groups are also being moved out of DeepMind into central Google as part of the same restructuring
- Google framed the move as consolidating AI safety work across the company to better inform safety for models and products
- The team tests Google's models for chemical, biological, radiological, and nuclear risks, and studies the psychological effects of chatbot interactions on users
- King noted that Demis Hassabis "still cares a lot about Responsible AI and is still planning to be involved"
- HR and policy groups are also being moved out of DeepMind into central Google as part of the same restructuring
- Google framed the move as consolidating AI safety work across the company to better inform safety for models and products
Dhruv Bhutani writes that he jailbroke his Kindle and added KOReader with an AI Assistant plugin powered by Gemini to get contextual reading help without tablet distractions. Highlighting text triggers the assistant for on-page explanations, simplified prose, contextual definitions and translation while keeping full book context. The setup removes friction of switching to a phone and preserves focus, making the Kindle a better reading companion for understanding difficult passages.
- Jailbreaking in 2026 is described as simple steps via KindleModding.org with KOReader installed by completion
- Plugin setup requires copying the assistant file to the plugins directory and entering Gemini API client and secret keys
- Custom prompts allow summaries of the book so far or explaining concepts as if to a five-year-old
- Author cautions against using AI as a crutch for speed reading and recommends treating it as a reference tool
- Jailbreaking in 2026 is described as simple steps via KindleModding.org with KOReader installed by completion
- Plugin setup requires copying the assistant file to the plugins directory and entering Gemini API client and secret keys
- Custom prompts allow summaries of the book so far or explaining concepts as if to a five-year-old
- Author cautions against using AI as a crutch for speed reading and recommends treating it as a reference tool
>"Pillar Security's research team, Eilon Cohen, Dan Lisichkin and Ariel Fogel, reproduced the bypasses over several months and published them today as a series they call the Week of Sandbox Escapes, one write-up a day."
This document contains system prompt instructions for an AI model designed to function as a Gmail Assistant. It details specific protocols for managing email threads, including deciding between providing single or multiple reply options based on user input complexity. The instructions cover tone maintenance, strict prohibitions against hallucinating information not present in context, and precise formatting rules for greetings and sign-offs.
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
Google explores the transition from traditional deterministic automation to agentic AI within Site Reliability Engineering. As system complexity grows due to microservices, cloud scale, and increased code generation, Google is implementing SRE AI across the entire software development lifecycle to enhance reliability. The approach includes using agents for automated runbook improvement, advanced anomaly detection, incident management orchestration, and autonomous investigation utilizing observability data.
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
Google announces several new AI-powered features designed to enhance productivity within the Google Workspace ecosystem and apps.
- Conversational voice features in Gmail Live, Docs Live, and Keep
- Google Pics image generation and precise editing tool
- Enhanced AI Inbox for streamlined task management
- Gemini Spark 24/7 personal AI agent integration
- Conversational voice features in Gmail Live, Docs Live, and Keep
- Google Pics image generation and precise editing tool
- Enhanced AI Inbox for streamlined task management
- Gemini Spark 24/7 personal AI agent integration
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.
