Operations | Monitoring | ITSM | DevOps | Cloud

Introducing Coralogix's AI Center: Real-time AI Observability

Traditional observability wasn't built for. The reason? AI operates in shades of grey, where outcomes are non-deterministic. That's why we built the AI Center, bringing real-time AI observability to thousands of enterprises worldwide. As part of our AI Center, we built an evaluation engine, designed to oversee and detect specific issues that are most common when building AI agents. Teams can choose the evaluators they want to oversee each agent and receive live alerts and reports into specific quality, security and compliance issues.

Unlocking Edge AI: a collaborative reference architecture with NVIDIA

The world of edge AI is rapidly transforming how devices and data centers work together. Imagine healthcare tools powered by AI, or self-driving vehicles making real-time decisions. These advancements rely on bringing AI directly to edge devices. However, building a robust architecture for diverse edge environments presents significant hurdles. This blog introduces our new reference architecture, designed to simplify edge AI deployment.

Building optimized LLM chatbots with Canonical and NVIDIA

The landscape of generative AI is rapidly evolving, and building robust, scalable large language model (LLM) applications is becoming a critical need for many organizations. Canonical, in collaboration with NVIDIA, is excited to introduce a reference architecture designed to streamline and optimize the creation of powerful LLM chatbots. This solution leverages the latest NVIDIA AI technology, offering a production-ready AI pipeline built on Kubernetes.

New In Playwright 1.51 - Can AI Fix Failing Tests With The New Error Prompt?

In this episode, Stefan Judis, Playwright ambassador, explores the new 'Copy as prompt' feature in Playwright 1.51. This feature allows you to copy a pre-filled LLM prompt with all the context of a failing test case. Does this mean that AIs can take over and magically fix all the failing tests? Let's find out!

Modernizing Data Centers for AI: Bridging Observability, Cost Control, and Intelligent Automation

Attend our webinar on April 3 to see our latest innovations live. Register IT Operations are more complex than ever, with modern data centers spanning on-premises, containers, multi-cloud environments, and AI-powered infrastructure. The rapid expansion of data sources has created an overwhelming volume of information, making manual monitoring across multiple tools impractical. Visibility gaps slow down troubleshooting and delay critical decisions, impacting business performance.

Uplink | Episode 1: The Future of AI Consumption with Chris Sharp, CTO of Digital Realty

Welcome to Uplink, the podcast where digital infrastructure leaders reveal the underlying technology powering AI and cloud innovation. In this episode, our host Michael Reid sits down with Chris Sharp, CTO of Digital Realty, to discuss how enterprises are consuming AI today, the role of private interconnection, and what the future of AI workloads looks like.

Observability Reimagined: How AI is Transforming Monitoring

Observability needs to evolve. With AI reshaping IT monitoring, how can businesses leverage predictive analysis, AI-driven monitoring, and auto-remediation workflows to create more resilient infrastructures? At Civo Navigate San Francisco 2025, Jemiah Sius, New Relic, explores how AI is transforming observability, shifting from reactive responses to proactive, intelligent solutions.

AI Integration #speedscale #ai #integration #mcp #march

Ken Ahrens from Speescale dives into the best AI API integration model of March 2025 — Anthropic's MCP model. This innovative integration enables seamless communication with browsers and various tools, including the popular Cursor. Discover how the MCP model is revolutionizing AI-powered workflows and boosting productivity.

AI Costs In 2025: A Guide To Pricing, Implementation, And Mistakes To Avoid

AI costs haven’t been a major factor in cloud computing — until now. For example, AI demands massive data processing and storage, such as for training Large Language Models (LLMs) and generative AI. Additionally, AI workloads require parallel processing, which traditional instances struggle to handle — forcing companies to invest in specialized (and expensive) GPUs to get the job done.