Randy Bowie

Big news for AI workloads on Oracle Cloud Infrastructure: OCI + DDN Infinia are redefining how enterprises scale AI. In recent benchmarks, Infinia on OCI showed linear scalability, ultra-low latency, and GPU utilization near 100% — a huge step forward for running production-scale AI. This means enterprises can: ✅ Break down data silos with unified AI storage ✅ Support exabyte-scale datasets + 100Ks of GPUs ✅ Accelerate real-world use cases from finance to healthcare 👉 Full blog here: https://lnkd.in/emkxJuvE 💬 Curious — what’s your biggest storage bottleneck when scaling AI today? Thanks for publishing Pinkesh Valdria, Dhvani S., Salem Albeiroti Kumaran Rajaram

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We have made some exciting progress here at Vista on building out our "MCP Ecosystem". We just internally launched v1 of "vista-mcp", a framework for abstracting away the auth, observability, etc for rapidly building new MCP servers with developer tooling. We are then wiring those MCP servers to apps like VSCode and Librechat to build a fully connected AI ecosystem of tools and its been incredibly interesting to watch all of the possibilities. I have a lot of thoughts (and opinions) on where this is headed so I plan to start posting a fair bit more on what I am seeing. Whether its the future of software engineering, AI architectures, developer productivity, there is a lot to unpack and things are changing daily.

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Big news from the frontlines of observability & AI! I couldn’t be more thrilled about us announcing IBM Instana GenAI Observability: https://lnkd.in/ekREZ6zv This isn’t just “another observability add-on.” It’s a leap into the future — built to help enterprises tame the complexity and unpredictability of generative-AI systems in production. Here’s why it matters: 🔍 Why this is a game-changer • Full-stack + AI context in one pane — No more siloed tools. AI workflows, prompt chains, retries, tool calls—all tied into your existing infra and app traces. • Token-level cost oversight — Avoid “bill shock” by attributing spend to workloads, models, services, tenants, etc. • Faster root-cause across AI + infra — When things go wrong, you don’t have to switch between systems or guess what the AI component did. • Open, vendor-neutral instrumentation — Built with OpenLLMetry to keep your data portable and future-proof. • Ecosystem compatibility — From LangChain to WatsonX, Bedrock, vector databases and more, it’s designed for broad AI architectures. 💡 What this means for teams & leaders • For SREs / platform engineers: a single source of truth to monitor, debug, and optimize AI-infused systems. • For executives / leaders: governance, cost control, and confidence to scale AI investments. • For organizations: fewer surprises, lower risk, and faster innovation cycles. I’m proud of the team’s work here—this reflects months (actually years) of refining how we see observability in a world infused with AI. And to everyone building, deploying, or scaling genAI capabilities: I’d love to hear your thoughts. 📣 What’s on your wish list for AI observability? 🔧 What’s your biggest pain point right now when debugging AI systems in production? If you’re attending #IBMTechXchange in Orlando, come say hi—session #3122 is where we’ll dig into this. Let’s start a conversation 👇 Vikram Murali Bill Lobig Jacob Yackenovich Jayanth Putta Bipin Chandra Srivastava Arthur De Magalhaes Guangya Liu

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Bridging Copilot Studio and Azure AI Foundry: Architectural Insights for Enterprise AI Integration In my last week's Blog post, I discussed the advantages of integrating Copilot Studio with Azure AI Foundry for developing custom agents. In today's follow-up, I explore practical implementation routes for integrating AI Foundry features within Copilot Studio. I demonstrate through three different use cases how this combination can enhance enterprise-level solutions. The post provides hands-on implementation methods to improve custom agents with advanced AI functionalities. It outlines two primary integration methods - employing Azure Functions alongside Agent Flows for more complex scenarios, and direct model integration for simpler cases - along with three detailed examples: email classification, visual issue detection in IT support, and legal document summarization. Although this combined approach enables advanced features, it's worth noting that enterprises can create agentic layers using only Copilot Studio - mixing the two tools is optional, not required. #microsoft #copilotstudio #powerplatform #azure #aifoundry #agents #mvpbuzz

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Now that Memfault is integrated into nRF Cloud, standing up OTA and Observability for your nRF54, nRF53, or nRF52 takes six minutes, instead of 6 months. You don’t have to piece together tools, build out infrastructure, and manage delivery all on your own. With nRF Cloud powered by Memfault, you get a turnkey solution baked into the nRF Connect SDK. Flip a few config flags, create an account, and immediately start shipping with a global content delivery network, approval workflows, and audit logs in place. This shift redefines what’s possible for developers building with Nordic chips. Exclusive pricing for Nordic Semiconductor customers is available today. https://lnkd.in/e_jUfueD

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