All news with #gemini tag

🛡️ This week at the Munich Security Conference, Google Cloud released a whitepaper, "Staying Ahead of the Shadows: Digital Resilience in the Era of AI," that outlines current digital threats and recommends a unified, full‑stack defense to help democracies. It highlights supply‑chain targeting, employee‑focused manipulation, and sustained China‑nexus espionage. The paper prescribes a five‑layer resilience model — Infrastructure, Architecture, Models, Applications, and Security — supported by technologies such as Gemini, Workspace, CodeMender, SAIF, and post‑quantum cryptography.

🛡️ Google Cloud outlines an AI-driven approach to operational resilience for financial institutions, using Gemini Enterprise to generate context-aware incident scenarios based on a customer's real production data. The Technical Account Management team ingests logs, architecture, SLAs and support history to craft bespoke timelines that test cross-functional response and communication. The program supports DORA readiness and has driven immediate remediation actions and process changes.

🔧 This guide explains how to adapt foundation models on Google Cloud by fine-tuning both managed and self-managed workflows. It contrasts a fully managed Vertex AI Supervised Fine-Tuning path for models like Gemini with a customizable GKE approach using LoRA on open-source models such as Llama. The labs walk through data preparation, baseline evaluation, tuning, and automated evaluation metrics, as well as GKE infrastructure, GPU provisioning, security with Workload Identity, and containerized training for production readiness.

🧰 The Google Cloud samples team describes building a specialized end-to-end system that uses Gemini on Vertex AI and Genkit to produce production-ready educational code samples across many languages and products. Their architecture separates generation, validation, and delivery so LLM outputs are combined with deterministic automations, linters, unit tests, and human review. The post presents seven practical technical takeaways—decomposition, determinism, precise prompts, vetted evaluation, scaled downstream processes, end-to-end testing, and solid engineering practices—that drove reliable, scalable sample generation.

🚀 Google has opened the Gemini Enterprise Agent Ready (GEAR) learning program to all developers and professionals as a dedicated pathway within the Google Developer Program. GEAR provides 35 monthly Google Skills credits for hands-on labs and sandbox testing and features end-to-end learning paths such as Introduction to Agents and Develop Agents with the ADK. Members can earn profile badges and pursue instructor-led certification tracks to validate enterprise agent engineering skills and accelerate production-ready deployments.

🔓 This article explains how to begin using Gemini 3 by activating the Google Cloud Free Trial, which grants new users $300 in credits valid for 91 days plus ongoing Free Tier usage. It walks through eligibility checks, signing in with a Google Account, accepting terms, and completing identity verification with a payment method (you are not charged automatically). The post highlights useful Free Tier limits — including Cloud Run, Compute Engine, Cloud Storage, and BigQuery — and shows how to enable billing, import the Cloud Project into Google AI Studio, create a project-attached API key, and call Nano Banana Pro and other Gemini Pro models.

🤖 Google’s Agent Factory episode demonstrates how Gemini 3, the Gemini CLI, and Antigravity enable rapid creation of agentic workflows and lightweight “AI employees.” Smitha Kolan, Vlad Kolesnikov, and guest Brandon Hancock present live demos building a portfolio site, parallel market-research agents, and a video-generating agent. The session highlights multimodal prompting, SOP-driven automation, parallel execution, and one-click deployment to Cloud Run.

🧭 Antigravity and Gemini CLI offer two complementary approaches for running agent-driven workflows. Antigravity delivers an approachable, graphical experience with an Agent Manager, in-browser application views, guided walkthroughs, and a native debugger for inspecting stack traces. Gemini CLI is terminal-first, installs via npm (npm install -g @google/gemini-cli, requires Node.js), supports headless/CI-friendly execution, and can call local tools like gh or gcloud. Both are extensible with MCP and Agent Skills, and both provide generous free tiers so teams can evaluate which workflow best fits their needs.

🔧 This guide explains how to build custom employee onboarding agents using the Agent Development Kit (ADK), Vertex AI Agent Engine, and Application Integration to connect conversational AI with enterprise systems such as ITSM, ERP, and CRM. It describes a grounded agentic workflow where a Gemini Enterprise front-end captures intent, a low-code Application Integration layer performs deterministic transformations and authentication, and backend systems execute transactions. The result is a role-aware, auditable onboarding experience that automates tasks like laptop provisioning while keeping business rules and approvals intact.

✨ BigQuery now integrates Gemini and Vertex AI models directly into SQL, enabling in-place generative AI and embedding workflows. New functions—AI.GENERATE(), AI.GENERATE_TABLE(), AI.EMBED(), and AI.SIMILARITY()—support text and multimodal inputs and return structured outputs via an output_schema for immediate querying. End User Credentials simplify authentication, and Gemini 3.0 Pro/Flash support is available for advanced generative tasks.

🚀 Gemini for Government packages Google’s Gemini models, secure commercial cloud services, and agentic AI tools into a FedRAMP High-authorized platform for the public sector. It is positioned to accelerate adoption of AI agents across defense, health, transportation, and research by offering enterprise-grade compliance and scalability. Early adopters include the Department of War’s GenAI.mil rollout, the FDA, and the Department of Transportation, and Google is offering webinars and downloadable agent toolkits to help agencies start building and deploying solutions today.

🚀 Fastweb + Vodafone rebuilt its Customer 360 platform on Google Cloud to deliver unified, real-time customer insights using Spanner, BigQuery, and Gemini. The team replaced brittle, high-maintenance integrations with a connected platform that enables low-latency serving, centralized governance, and AI-assisted engineering workflows. Spanner’s native BigQuery integration sped migrations and simplified monitoring, while Apigee exposes consistent data to call centers, digital channels, and partners.

🛠️ Google SREs describe using Gemini 3 and Gemini CLI to accelerate incident response across the full lifecycle: paging, mitigation, root cause analysis, and postmortems. The CLI integrates with SRE tools (alerting, logs, timeseries, and source control) and selects deterministic, typed actions while enforcing policy to keep humans in the loop. In practice it lowers Mean Time to Mitigation by automating playbook selection, executing safe mutations with approval, and producing fixes and postmortems that are auditable and repeatable.

🚀 This guide demonstrates how to create and deploy a public web app using Gemini 3 Flash Preview via Google AI Studio and Google Cloud Run. In Build mode you describe the application in natural language and let the model "vibe code" a complete app, which appears instantly in the Preview panel for testing. When satisfied, a single Deploy App action pushes the app to Cloud Run, exports your API key as an environment variable, and provides a shareable URL. Note that deployment requires a Google Cloud project with billing enabled.

📚 This free course from Google Cloud and DeepLearning.ai teaches practical use of Gemini CLI, guiding users through installation, context management, extensibility, and specialized workflows. It is designed for developers and non-developers who want to integrate the CLI into daily tasks such as data analysis, content generation, and personalized learning. The curriculum runs in under two hours and provides hands-on lessons covering GEMINI.md, memory features, MCP servers, and extensions.

🚀 Google Cloud has launched a new Bangkok (asia-southeast3) region to deliver low-latency, high-performance cloud services while enabling local data residency under Thailand’s PDPA. The region is part of a USD $1 billion investment and is expected to generate THB 1.4 trillion (US$41 billion) in economic value over five years and support roughly 130,000 jobs annually. It offers certified security controls (ISO/IEC, PCI DSS, SOC), default encryption, customer-managed keys, and direct access to Vertex AI, enterprise Gemini, and generative models to accelerate local AI adoption.

📰 Google says Gemini will not include ads for now, a stance confirmed by DeepMind CEO Demis Hassabis at the Davos Economic Forum. Google AI leadership reiterated that it currently does not plan to monetize Gemini with advertising, although the company did not rule out future changes. Meanwhile, OpenAI has begun testing ads in ChatGPT in the U.S. for Free and Go users, with paid tiers expected to remain ad-free.

⚠️ Researchers at Miggo Security demonstrated that Google Gemini can be manipulated via malicious Calendar invites to exfiltrate private event data. By embedding natural-language prompt-injection payloads in an event description, attackers can cause Gemini to summarize private meetings and write that summary into a new event visible to participants. Miggo reported the issue and Google has implemented mitigations.

📅 A newly disclosed weakness in Google’s Gemini demonstrates how routine calendar invites can be weaponized to influence model behavior. Miggo researchers found that Gemini ingests full event context — titles, times, attendees and descriptions — and may treat that content as actionable instructions. The issue reframes calendar entries from inert data into a potential prompt‑injection vector, highlighting risks as enterprises embed generative AI into day‑to‑day workflows.

🚀 This post introduces Gemini 3 Flash, Google’s low-latency, cost-efficient model in the Gemini 3 family, optimized for advanced reasoning, multimodal understanding, and agentic workflows. It guides developers through obtaining an API key from Google AI Studio and configuring it for local use or environment-based invocation. The article demonstrates interactive prompt testing in the Playground, explains toggles like Structured outputs and Thinking level, and shows how to export language-specific sample code via the "Get code" feature to run with the Google GenAI SDK.