GenAI in ITSM: Benefits, Use Cases & Automation Explained
Generative AI is transforming IT service management from reactive support to predictive operations. Learn how GenAI automation accelerates resolution and reduces costs.
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How to Reduce MTTR with AI-Powered Runtime Diagnosis
Reducing Mean Time to Resolution (MTTR) in production systems requires understanding failure behavior in real time. While AI code agents significantly accelerated software development and deployment, incident resolution has remained constrained by incomplete pre-captured telemetry. AI SRE tools improve signal correlation, but MTTR reduction requires runtime-verified diagnosis that confirms execution behavior directly in production systems.
Actually Useful AI: Troubleshoot Issues Fast with Grafana Assistant
From "something's off" to "here's why (and how to fix it)." Grafana Assistant goes full detective mode: pulls the clues, connects the dots, and recommend a fix... with receipts.
Why Generic AI Fails in Ops: What Trustworthy Actually Requires
Enterprise operations reached a point where complexity outpaced human interpretation and outgrew the capabilities of generic AI. As environments became more distributed and interdependent, every incident, anomaly, and degradation produced ripple effects across systems that require context, lineage, and reasoning. Yet most AI models were not built for this reality. They were trained for general knowledge tasks, not the deeply connected operational truths that define enterprise performance.
How Developers Build a Meaningful Career in the Age of AI
What does a meaningful developer career look like in the age of AI? We brought together four experts to answer exactly that. In this GitKon panel, GitKraken CMO Kate Adams moderates a conversation with Leon Noel (Managing Director of Engineering, Resilient Coders), Danny Thompson (Director of Technology and host of The Programming Podcast), Maggie Hunter (Recruitment Lead, GitKraken), and Dimitry Fonarev (CEO, Testkube) to explore how software engineers can future-proof their careers, grow their skills, and navigate an industry that is changing fast.
The Hidden Cost of AI Productivity: When Efficiency Turns Into "Brain Fry"
A new HBR study reveals that the race to build and manage AI agents may be pushing knowledge workers toward a new form of cognitive overload. If you spend any time on LinkedIn these days, you’ve probably seen the same type of post over and over. Someone proudly announces they built an AI agent that now writes their emails, analyzes data, drafts presentations, and maybe even ships code.
Why AI scaling needs predictable infrastructure
"If your finances are tied to a third-party token cost, your business model can change overnight." Civo CTO Dinesh Majrekar and Techdome CEO Rahul Joshi break down the "variable factor" risk in AI scaling. Relying on external API endpoints means you have zero control over your margins.
Get Kafka-Nated S2E3: Yingjun Wu on Streaming Databases, SQL-First Processing, and Real-Time Data
March 11th, 4PM GMT Yingjun Wu is the founder and CEO of RisingWave Labs, a company building a distributed SQL database for stream processing, with over a decade of experience spanning academic research and large-scale production systems. In this episode we'll be sitting down for a coffee and a conversation with Yingjun's about what a streaming database actually is, how message brokers and streaming databases fit together in real-world architectures, and what his journey from database researcher to founder has taught him.
Edge AI in action powered by Ubuntu Core
Hans Michael Krause shows how ctrlX OS Ecosystem, powered by Ubuntu Core, enables real-time visual inspection directly on an industrial controller.
Evaluating Observability Tools for the AI Era
Every observability vendor has an AI story right now. Most have an MCP. Many have a chatbot. All have a demo where the AI finds the root cause of an incident in thirty seconds and everyone in the room nods. In the context of a public demo, these tools look almost identical. Ask the AI a question, the tool returns an answer, and the engineer fixes the bug. Impressive. But if you buy based on the demo, you may end up with an AI layer that looks great on a call and disappoints in production.