Operations | Monitoring | ITSM | DevOps | Cloud

GitKraken Insights | Engineering Intelligence in Minutes

Most software intelligence tools take months to implement, cost a fortune, and end up collecting dust. GitKraken Insights is different. It helps engineering leaders measure what matters: AI impact, code quality, delivery performance, and developer experience, all in one place. It’s the latest evolution of the GitKraken DevEx platform, trusted by over 40 million developers. Insights connects data from across your GitKraken tools to give you a complete picture of engineering health and value. We're talking DORA metrics, pull request metrics, and AI impact.

What is ServiceNow's AI Control Tower?

What happens when AI agents stop being scattered and start being steered? Customer service queues shrink, teams get time back for high-value work, and everyone finally works off the same data. That’s the power of the ServiceNow AI Control Tower—all your AI, all under control. No more fragmentation. No more busywork. Just visibility, control, and workflows that scale across the entire business.

Observability for GenAI Applications (Grafana OpenTelemetry Community Call)

In this episode, we’re diving into observability for Generative AI apps. AI helps us write code and monitor applications in production - but how do we observe the AI itself? And how do we make sense of complex, non-deterministic AI systems? We’re joined by two great guests: Ishan Jain, working on GenAI observability and Luccas Quadros, working on Grafana Assistant. Together, they bring both platform-level insights and real-world perspectives.

From idea to agent: Building AI workflows with relaxAI and n8n

Join us for this live online webinar as we explore how to design, build, and deploy practical AI agents using n8n’s workflow automation platform powered by relaxAI’s UK sovereign infrastructure. Our speaker, Ben Norris, AI Engineer at Civo, will guide you through the real-world process of creating intelligent agents that automate tasks across tools and services, all without deep coding expertise.

[Webinar] Building Quality-Driven Agentic AI in Noisy Big Data Environments

Watch as Itiel Shwartz, Komodor CTO and Co-Founder as he shares hard-won lessons from developing an AI agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking. This webinar covers: Building production ready systems that maintain reliability when 90% of your data is noise. How Komodor developed an AI SRE agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking.

How to Use PostgreSQL AI for Query Writing and Optimization

PostgreSQL AI is gaining attention as SQL complexity increases in production environments. It addresses a common problem: extended queries that accumulate joins, nested logic, and edge cases. Without AI assistance, these queries are often harder to write and review, driving 20–40% of developer time into debugging. In practice, these challenges affect PostgreSQL users in different ways.

How GitKraken's AI-Powered Commit Composer Eliminates Git Cleanup Headaches

As developers, we’ve all been there: a frantic coding session, a few hasty commits, and suddenly our Git history looks like a patchwork quilt of “fix,” “oops,” and “stuff.” While git rebase -i is a powerful tool for cleaning up, it’s also a source of anxiety for many, often leading to more headaches than it solves. What if you could achieve a pristine, meaningful commit history without the fear of breaking things or hours spent squashing and rewriting?

Why AI Automation for ITOps Needs Context Graphs

AI automation in ITOps fails because execution loses decision context, and context graphs turn incident history into durable execution memory that systems can actually reuse. AI automation for ITOps fails because it remembers what it did, but not why. Fixing an issue depends on what was tried last time, what failed, what worked, which exceptions were approved, and under what conditions. That information rarely lives in the system.
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Digital Twins Gone Wild: My Unexpected AI Doppelgänger

I recently tried using AI to create a digital twin of myself. I uploaded a photo, expecting a futuristic, slightly improved version of me... and what did I get in return? A picture of Kim Jong Un. Clearly, AI has a sense of humor-or a very different definition of "twin." Forget Arnold Schwarzenegger and Danny DeVito. Digital Twins 2-Now Starring My AI Doppelgänger From Speedscale's perspective, a digital twin is built from real production traffic, continuously updated, and executable in your test and CI/CD environments.