N-able Launches Cat-MIP, Setting the Industry Standard for AI Accuracy in Cybersecurity and IT Management
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AI Reliability Insights: How to Build a Gremlin MCP Server
Gremlin’s Reliability Intelligence helps teams uncover the cause behind failure modes so they can move faster and improve reliability without sacrificing velocity. The new Gremlin MCP Server, part of Reliability Intelligence, gives you new ways to explore your data, giving you access to insights and recommendations to improve reliability and better run your systems using Gremlin. In this webinar, Gremlin CTO Sam Rossoff shows you how to integrate your favorite LLM and use plain language to query data, uncover insights, create dynamic dashboards, and more.
Introducing Honeycomb Intelligence Canvas
Canvas is an AI-guided workspace inside Honeycomb that combines an AI assistant with an interactive notebook for visualizing query results and traces. You can ask a natural language question about your data and Canvas will immediately start exploring your traces, through multiple queries and other tools, to find the right next steps. Instead of having to write each query yourself, Canvas automatically proposes relational queries, comparisons, and visualizations that explain why an SLO fired or what changed after a deploy.
FireHydrant 4-Minute Demo
Get a quick walkthrough of the FireHydrant platform. FireHydrant is the all-in-one incident management platform that helps teams resolve incidents up to 90% faster — and prevent them from happening again. From flexible alerting and powerful automation to retros and AI insights, it brings clarity and control to every step of your response.
Debug, query, and build faster with AI: How we use Grafana Assistant at Grafana Labs
We recently released Grafana Assistant into public preview for Grafana Cloud, and we’ve been excited to see how our customers have already made it part of their daily observability routines. At the same time, Assistant is becoming a go-to companion for developers right here at Grafana Labs, whether they’re debugging on-call issues, helping customers, or trying to remember tricky PromQL syntax.
Meet Canvas: Your AI-guided Workspace Within Honeycomb
Modern systems are wonderfully capable, but relentlessly complex. Debugging across microservices, frontends, and cloud edges often means switching between five or more tools, trying to stitch together “what changed” and “why it broke.” Honeycomb’s wide events model has proven to be a superpower for taming that complexity, by allowing you to easily observe and query end-to-end traces without worrying about how much granular data you attach to your events.
Behind the Dashboard: How to monitor your LLM integrations
Behind the Dashboard is an ongoing series where we look under the hood of a specific Catchpoint feature. Each episode breaks down the technology itself, what’s challenging about using it for monitoring, and how we removed friction and toil to make it a valuable part of the Catchpoint platform. In this episode Leon, Mursi, and Rahul take a look at Catchpoint’s LLM monitoring capabilities, including ensuring your integrated LLMs are up and performing optimally; as well as knowing if you’re using the most effective (accurate) and economical (cheapest per query) option in your suite.
Debugging issues with Sentry's MCP
Turns out, this MCP thing is pretty solid. We've built the MCP server to tap into all the different areas of context within Sentry and make it easy to bring these into your editor client to help debug your application. Want to know the most fixable issues in your environment? Easy. Want to see your query performance for your backend? Just ask it.
Musk Challenges Apple-OpenAI Integration, Raising Questions on AI Competition
Elon Musk - via X Corp. and xAI - recently filed an antitrust suit in Texas. He's going after Apple and OpenAI. The claim? That Apple lets ChatGPT run deep inside iPhones and Macs, which may give Apple a nearmonopoly that hurts his own chatbot Grok and various other rivals. Musk is seeking billions in damages and a court order to stop the practice. The case arises while he still constantly talks about his early days at OpenAI, which he left after its commercial shift.
Finding the Ghost in the Machine
The industry is rapidly moving towards deeper AI integration than ever before. What was once simply focused on chatbots or recommendation engines has pivoted significantly to AI systems communicating with other AI systems. These AI tools are leveraging multi-agent workflows to accomplish complex tasks that traditional systems have struggled with. Innovation without validation is a liability. Any developer worth their salt will know that these systems require ample testability and validation.