Introducing VaultGemma: Google's Private Enterprise LLM

We’re excited to announce the public preview of Microsoft Sentinel MCP (Model Context Protocol) server, a fully managed cloud service built on an open standard that lets AI agents seamlessly access the rich security context in your Sentinel data lake. Sentinel MCP server solves that by providing standardized, secure access to that context—across graph relationships, tabular telemetry, and vector embeddings—via reusable, natural language tools, enabling security teams to unlock the full potential of AI-driven automation and focus on what matters most. Model Context Protocol (MCP) is a rapidly growing open standard that allows AI models to securely communicate with external applications, services, and data sources through a well-structured interface. Think of MCP as a bridge that lets an AI agents understand and invoke an application’s capabilities. These capabilities are exposed as discrete “tools” with natural language inputs and outputs. The AI agent can autonomously choose the right tool (or combination of tools) for the task it needs to accomplish. https://lnkd.in/d99YcZQE

AI Agents Just Got a Game Changer! 🚀 The Claude Team has just announced Claude Sonnet 4.5, and it's set to revolutionize how we approach AI agents, coding, and computer use. This model is designed for long-running tasks with enhanced domain knowledge across coding, finance, research, and cybersecurity. Key points making this a game changer: Improved accuracy: for long-running tasks, with stronger domain knowledge in coding, finance, research, and cybersecurity. Context Management: New memory and context editing tools help developers build AI agents that handle long tasks without losing information or hitting context limits. This allows for more intelligent and robust agent performance. See Claude Sonnet 4.5 in action playing Catan (link in comment) It's available on the Claude Developer Platform, Amazon Bedrock, and Google Cloud’s Vertex AI. Pricing is consistent with previous Sonnet models.

Not all LLM-assisted coding is created equal. AI coding assistants often struggle with integration-level work like infrastructure that has to talk to cloud providers. It demands systems thinking, and a lot of the context is implicit. I use a simple heuristic to decide if AI will help: 1. Is the problem well-understood? (How many permutations exist?) 2. Can I express the context succinctly and explicitly? Or will gathering/specifying everything cost more than it saves? 3. Is the output easy to verify? How tight is the feedback loop? Infrastructure often fails this test: • Combinatorial explosion (provider, service, version, IAM, network shape). • Critical context is implicit—you discover it by probing. • Actions can take minutes, so mistakes are costly. This example is from software, but the heuristic applies in many domains.

🚀 The Future of AI is Here: From Chat to Action Ever asked an AI assistant to "download research papers and book a flight" only to get a polite "I can't do that"? That frustrating gap between what AI can understand and what it can actually DO is finally closing. Enter MCP Servers (Model Context Protocol) - the game-changing architecture that's transforming AI from smart conversationalists into capable digital assistants. What makes MCP revolutionary? 🔹 Modular Design: Instead of monolithic AI, we now have smart orchestrators coordinating specialized tools 🔹 Real Actions: Download papers, book services, send emails - actual work gets done 🔹 Intelligent Routing: Semantic matching connects user intent to the right tools automatically 🔹 Extensible Ecosystem: New capabilities can be added without rebuilding the core system The Technical Magic Behind It: ✅ Intent analysis with NLP ✅ Confidence scoring for tool selection ✅ LLM-powered parameter extraction ✅ Standardized API protocols ✅ Graceful error handling The Result? AI assistants that don't just chat - they act. Imagine telling your AI: "Find and download 10 recent papers on reinforcement learning, then summarize the key findings" - and it actually happens. That's the MCP difference. I've written a comprehensive deep-dive into MCP architecture, complete with real code examples and flow diagrams. If you're building AI systems or curious about the future of human-AI interaction, this is a must-read. Link to full article: https://lnkd.in/g9pFMjqb What real-world AI capabilities are you most excited about? Drop your thoughts below! 👇 #AI #MachineLearning #TechInnovation #MCP #AIAssistants #TechArchitecture #FutureOfWork #ArtificialIntelligence #SoftwareDevelopment #TechLeadership

The World After Oracle + OpenAI If Oracle and OpenAI integrate deeply, it could mark a turning point in enterprise AI. Until now, most AI models lived outside enterprise data, which is powerful, but disconnected and this integration changes that equation. Oracle brings what OpenAI has lacked: governed, structured, enterprise-grade data. OpenAI brings what Oracle hasn’t offered natively: reasoning and natural language intelligence. When models move closer to the data layer: • AI stops being an overlay and becomes a core operational engine. • Decision-making becomes autonomous, not reactive. • Imagine AI systems that don’t just analyze trends but automate workflows, draft reports, and predict risks within the governed walls of Oracle Cloud. This marks a new era: 👉 From innovation hype to operational intelligence 👉 From generic models to enterprise-aware reasoning 👉 From siloed data to actionable insight Oracle grounds AI in data reality and OpenAI gives that data a voice. #AI #Oracle #OpenAI #EnterpriseAI #DataStrategy #DigitalTransformation

As enterprises increasingly seek to capitalize on artificial intelligence (AI) and train large language models, many encounter significant delays moving vast amounts of data. Datasets that span petabytes are often distributed across a variety of data centers, edge locations and public cloud environments. This creates bottlenecks when attempting to securely deliver data to AI-optimized GPU clusters, causing data transfers to take months, impeding time-to-value, and elevating costs for AI projects. 🟢 Riverbed Technology's Data Express Service addresses these issues by securely facilitating data transfers that are up to 10x faster than current industry solutions, and helping customers overcome one of the biggest barriers to AI adoption: Getting the right data to the right location, with industry-leading speed and security to maximize the return on their AI investment! https://lnkd.in/dajZV4At

The AI Data Center Race: Why It Matters for Multi-Generational Families The headlines today are filled with announcements of massive new AI data centers—Microsoft’s $500B Stargate Project, Amazon’s $100B Project Rainier, Google’s expansion of custom TPU-powered campuses, and Meta’s Hyperion and Prometheus mega-hubs. These are not simply “server farms”—they are the backbone of artificial intelligence and the foundation of next-generation computing. But why should families focused on preserving and growing wealth across generations pay attention? 🔹 Capital Flows Shape Opportunity Hundreds of billions of dollars are being poured into chips, power infrastructure, cooling systems, and sustainable energy to fuel AI workloads. This scale of investment creates ripple effects across entire industries—semiconductors, utilities, water, construction, and cybersecurity. Families with a long-term lens can identify where durable value is being created. 🔹 The Operating System of the Future Economy These data centers are not just about training models like GPT or Gemini—they are the control towers of tomorrow’s economy. From healthcare to finance, logistics to education, every industry will be influenced by who owns and operates the core infrastructure of AI. For multi-generational clients, understanding this shift helps ensure their legacy is aligned with where the future is headed. 🔹 Generational Relevance and Careers The next generation isn’t just inheriting wealth—they’re stepping into a world reshaped by AI. Whether through career paths, entrepreneurship, or stewardship of family capital, exposure to the sectors driving AI adoption will define the opportunities available to them. 🔹 Sustainability and Legacy Google’s carbon-free power focus and Meta’s modular “Tent” data centers highlight a broader truth: AI infrastructure will consume gigawatts of energy and massive water resources. Families committed to values-based and sustainable investing must weigh both opportunity and responsibility as this race accelerates. In short, the AI data center race is not about flashy headlines—it’s about the underlying foundation of the 21st-century economy. For families building and protecting wealth across generations, this moment represents both an investment opportunity and a strategic imperative. #WealthAndLeisure For weekly insight on legacy planning & passion. #AI #DataCenters #WealthPlanning #MultiGenerationalWealth #Innovation #FamilyOffice #Sustainability #WealthAndLeisure

OpenAI GPT-5 will be available natively in Databricks, so you can build, evaluate, and scale production-grade AI apps and agents directly on your governed enterprise data. No extra setup, with governance and security built in! Learn more: https://lnkd.in/gqqPPAXW

This exciting agreement positions the Databricks platform as a central environment for enterprise AI, allowing organizations to combine governed data management with direct access to state-of-the-art models in a single, unified solution.

This is amamzing partnership. Databricks + GPT-5 = AI agents that can debug complex systems, modernize legacy apps, and generate production-ready code at scale. The future of coding is here. #Databricks #GPT5 #AI #GenerativeAI #LakehouseAI #MachineLearning #FutureOfWork #DataEngineering #AIagents #EnterpriseAI