All news with #gemini tag

🚀 Google Cloud has launched the Production-Ready AI Learning Path, a free curriculum designed to guide developers from prototype to production. Drawing on an internal playbook, the series pairs Gemini models with production-grade tools like Vertex AI, Google Kubernetes Engine, and Cloud Run. Modules cover LLM app development, open model deployment, agent building, security, RAG, evaluation, and fine-tuning. New modules will be added weekly through mid-December.

🚀 Google Cloud introduces hands-on labs for Gemma 3, a family of lightweight open models offering multimodal (text and image) capabilities and efficient performance on smaller hardware footprints. The labs present two deployment paths: a serverless approach using Cloud Run with GPU support, and a platform approach using GKE for scalable production environments. Choose Cloud Run for simplicity and cost-efficiency or GKE Autopilot for control and robust orchestration to move models from local testing to production.

🚀 Google Cloud reports a new state-of-the-art Single Trained Model Track score on the BIRD benchmark, achieving 76.13 with a fine-tuned Gemini 2.5-pro. The team credits rigorous data filtering, multitask supervised fine-tuning, and test-time self-consistency selection for the gains. These improvements bolster NL2SQL features in AlloyDB AI and BigQuery, and enhance developer tooling such as Gemini Code Assist for reliable SQL generation.

🤖 This recap of The Agent Factory episode summarizes a conversation between Amit Maraj and Ravin Kumar (DeepMind) about building open-source agentic models. It highlights how agent training differs from standard ML, emphasizing trajectory-based data, a two-stage approach of supervised fine-tuning followed by reinforcement learning, and the paramount role of evaluation. Practical guidance includes defining a 50-example final exam up front and considering hybrid setups that use a powerful API like Gemini as a router alongside specialized open models.

💬 Google Cloud has made Looker Conversational Analytics generally available, bringing natural-language data queries to all Looker users. Built on the Looker semantic layer and powered by Gemini and Google’s agentic frameworks, the feature provides instant, explainable answers and supports multi-turn exploration across up to five connected Explores. Analysts can build and share agents, use LookML for fine tuning, and rely on a governed foundation that surfaces “How was this calculated?” explanations. Admins can enable the capability now to accelerate data discovery and improve self-service across teams.

🚀 Google outlines its plan to embed Generative AI across enterprise platforms and endpoints, integrating Gemini into Chrome Enterprise, Android, Pixel phones and Chromebook Plus devices. The post highlights the general availability of Cameyo by Google to virtualize legacy and modern apps in the cloud and the launch of Gemini in Chrome with enterprise-grade controls. It also previews Android XR and Pixel features powered by Gemini Nano, while expanding data loss prevention and a one-click SecOps integration to help IT secure AI-driven workflows.

🔒 Google on Tuesday introduced Private AI Compute, a cloud privacy capability that aims to deliver on-device-level assurances while harnessing the scale of Gemini models. The service uses Trillium TPUs and Titanium Intelligence Enclaves (TIE) and relies on an AMD-based Trusted Execution Environment to encrypt and isolate memory on trusted nodes. Workloads are mutually attested, cryptographically validated, and ephemeral so inputs and inferences are discarded after each session, with Google stating data remains private to the user — 'not even Google.' An external assessment by NCC Group flagged a low-risk timing side channel in the IP-blinding relay and three attestation implementation issues that Google is mitigating.

🤝 Google Cloud is increasing local AI compute in India with its AI Hypercomputer powered by Trillium TPUs, enabling training and serving of advanced Gemini models with data residency and sovereignty controls. New local offerings include batch support for Gemini 2.5 Flash, a preview of Document AI, and real‑time grounding using Google Maps for location‑aware responses. Google is also supporting Indic Arena at IIT Madras with cloud credits to benchmark Indian multilingual models and to help grow the local AI ecosystem.

🧠 Gemini Code Assist on GitHub now supports persistent memory that learns from merged pull request interactions to capture a team's coding standards, style, and best practices. The memory is stored securely in a Google-managed project specific to each installation and is applied selectively to relevant reviews. It infers reusable rules from review threads and uses them both to shape initial analysis and to filter draft suggestions so the agent adapts over time and reduces repetitive feedback.

🚀 Deploy the official n8n Docker image to Cloud Run in minutes to run scalable, serverless AI workflows. Cloud Run scales from zero and persists data in Cloud SQL while you only pay for active usage. The post shows how to call Gemini as the agent LLM and optionally connect workflows to Google Workspace via OAuth for Gmail, Calendar, and Drive. For production, follow the n8n docs to add Secrets Manager, Cloud SQL, and Terraform-based deployment.

🤖 Google appears set to release two new models: Gemini 3 Pro, optimized for coding and general use, and Nano Banana 2 (codenamed GEMPIX2), focused on realistic image generation. Gemini 3 Pro was listed on Vertex AI as "gemini-3-pro-preview-11-2025" and is expected to begin rolling out in November with a reported 1 million token context window. Nano Banana 2 was also spotted on the Gemini site and could ship as early as December 2025.

🤖 This recap of The Agent Factory features Logan Kilpatrick from Google DeepMind demonstrating vibe coding in Google AI Studio, a Build workflow that turns a natural-language app idea into a live prototype in under a minute. Live demos included a virtual food photographer, grounding with Google Maps, the AI Studio Gallery, and a speech-driven "Yap to App" pair programmer. The episode also surveyed agent ecosystem updates—Veo 3.1, Anthropic Skills, and Gemini improvements—and highlighted the shift from models to action-capable systems.

🤖 Google’s open-source Agent Development Kit (ADK) simplifies creating autonomous AI agents that use LLMs such as Gemini as their reasoning core. The post presents three hands-on codelabs that guide developers through building a personal assistant agent, adding custom and third-party tools, and orchestrating multi-agent workflows. Each lab demonstrates practical patterns—scaffolding an agent, integrating tools like Google Search and LangChain components, and using Workflow Agents and session state to pass information—so teams can progress from experiment to production-ready agent systems.

🚀 This codelab walks developers through building a functional travel chatbot using Google's Gemini via the Vertex AI SDK. It explains how to connect a web frontend to Gemini, craft system instructions to shape assistant behavior, and enable function-calling to fetch live data such as geocoding and weather. No advanced ML expertise is required; the lab provides step-by-step code samples, API usage, and practical recommendations for iterating prompts so you can produce a working, production-ready demo.

🤖 Google researchers disclosed a VBScript threat named PROMPTFLUX that queries Gemini via a hard-coded API key to request obfuscated VBScript designed to evade static detection. A 'Thinking Robot' component logs AI responses to %TEMP% and writes updated scripts to the Windows Startup folder to maintain persistence. Samples include propagation attempts to removable drives and mapped network shares, and variants that rewrite their source on an hourly cadence. Google assesses the malware as experimental and currently lacking known exploit capabilities.

📰 In October, Google highlighted AI advances across research, consumer devices and enterprise tools, from rolling out Gemini for Home and vibe coding in AI Studio to launching Gemini Enterprise for workplace AI. The month included security initiatives for Cybersecurity Awareness Month—anti‑scam protections, CodeMender and the Secure AI Framework 2.0—and developer releases like the Gemini 2.5 Computer Use model. Research milestones included a verifiable quantum advantage result and an oncology-focused model, Cell2Sentence-Scale, aimed at accelerating cancer therapy discovery.

🤖 October updates feature major product releases, developer tools, and research milestones from Google, centered on Gemini models and new AI capabilities. Highlights include Gemini Enterprise, the Gemini 2.5 Computer Use model for UI agents, plus consumer integrations such as Gemini for Home and Samsung's Galaxy XR. The month also brought breakthroughs in quantum computing, cancer research (Cell2Sentence-Scale) and fusion-energy collaborations, alongside expanded AI security measures and developer learning resources.

🔬 Google Cloud presents how researchers can accelerate scientific workflows by combining Gemini Enterprise with integrated HPC infrastructure. It showcases AI agents—like the Deep Research agent for literature synthesis and the Idea Generation agent for proposing and ranking hypotheses—alongside developer tooling such as Gemini Code Assist and Gemini CLI for code, debugging, and workflow automation. The platform pairs these capabilities with purpose-built VMs (H4D, A4, A4X) and Google Cloud Managed Lustre to scale simulations and analysis.

📣Google says its ad business will remain central as it integrates advertising into AI-powered search experiences. Google currently offers AI Overviews and a more capable AI Mode, and has begun limited experiments placing ads within those results. Executives say ads won't disappear but may appear differently and become more personalized based on user data. Tests and further plans are expected to continue into next year.

🚀 Google has open-sourced the GKE Gemini CLI extension, bringing Google Kubernetes Engine directly into the Gemini CLI ecosystem while also functioning as an MCP server for other MCP clients. The extension injects GKE-specific context, tools, and tailored prompts so developers can use shorter, more natural language interactions and integrated slash commands to complete complex workflows. It simplifies common operations—like selecting models and accelerators or generating Kubernetes manifests for inference—while improving compatibility with Cloud Observability. The project is actively maintained with regular releases and community contributions.