AI Automation: The Double Edged Sword for Business #aiautomation #itmanagement
IT organizations are often called to do more with less — but when end users begin to automate more, how do you manage those security implications?
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The latest in Cribl Packs
Join us to chat about the latest Cribl Packs release, how AI comes into play, and a demo to see it all in action.
2025 Best Creative AI Tools for Group Face Swaps & Intelligent Photo Editing
AI creativity tools became dramatically more powerful in 2025 - especially in two categories that creators rely on daily: This guide compares both tools with their strongest competitors in their respective categories, helping you choose the best AI solution for your workflow.
Conversational AI in Healthcare: The Rise of Virtual Health Assistants
Healthcare organizations are facing mounting challenges as demand for services increases while resources remain constrained. Patients now expect digital-first experiences that are fast, accessible, and available beyond traditional office hours. In response, healthcare providers are increasingly turning to intelligent conversational technologies to modernize patient engagement and improve internal efficiency. What began as basic automated chat has evolved into advanced systems that are reshaping how patients and providers interact across the care journey.
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Test Code Changes with Claude Code Using Real Traffic
TL;DR: Claude Code writes features fast, but you need integration tests. With proxymock MCP, Claude Code can pull real production traffic and validate your changes automatically-one prompt, no manual setup. Watch it catch bugs before production.
How AI Agents automate incident response #ai #cybersecurity #telemetry
Clint Sharp demonstrates how Cribl Search leverages AI to streamline incident investigation. Starting from a Slack channel, the AI builds an interactive notebook, analyzes order processing logs, and identifies suspicious traffic spikes. It connects high CPU usage to a recent Jenkins deployment, hypothesizing a supply chain attack, and ultimately recommends a rollback. This isn't a far off concept. It is the future of operations arriving right now.
Why AI agents need a common data model #ai #telemetry
Clint Sharp explains why a common model like OCSF is critical for the future of AI. Agents need standardized data to analyze information effectively on your behalf. He contrasts the traditional manual workflow of checking Slack, tickets, and wikis while asking colleagues with a future where AI fuses this human context with machine data. Instead of just search results, AI agents will hand you examined hypotheses so you know exactly where to take your investigation.
AI Reliability, Part 2: When the Datacenter Becomes the Bottleneck
In Part 1, we talked about all the hidden complexity inside AI systems: the pipelines, GPUs, embeddings, vector databases, orchestration layers, and everything else that quietly determines how reliable an AI-first product really is. But all of that software still rests on something far less glamorous: the physical infrastructure underneath it.
The Reality of GenAI in Production with Eduardo Ordax (AWS)
GenAI demos are easy. Production is where everything breaks. In this episode, Eduardo Ordax, Principal GTM GenAI at AWS, breaks down what actually stops companies from shipping reliable AI systems, and why the real blockers have little to do with technology.
How to use AI to analyze and visualize CAN data with Grafana Assistant
Note: A version of this post originally appeared on the CSS Electronics blog. Martin Falch, co-owner and head of sales and marketing at CSS Electronics, is an expert on CAN bus data. Martin works closely with end users, typically OEM engineers, across diverse industries, including automotive, maritime, and industrial. He is passionate about data visualization and AI—and he’s been working extensively with Grafana Assistant.