%term

Actually Useful AI: Troubleshoot Issues Fast with Grafana Assistant

Mar 12, 2026 By Grafana In Grafana

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.

View Video

Grafana

Read more about Actually Useful AI: Troubleshoot Issues Fast with Grafana Assistant

How to Reduce MTTR with AI-Powered Runtime Diagnosis

Mar 12, 2026 By Lightrun Team In Lightrun

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.

Read Post

Lightrun

Read more about How to Reduce MTTR with AI-Powered Runtime Diagnosis

GenAI in ITSM: Benefits, Use Cases & Automation Explained

Mar 12, 2026 By Infraon In Infraon

Generative AI is transforming IT service management from reactive support to predictive operations. Learn how GenAI automation accelerates resolution and reduces costs.

View Video

Infraon

Read more about GenAI in ITSM: Benefits, Use Cases & Automation Explained

Blind spots in hybrid IT: SolarWinds report finds 77% of IT teams lack full visibility across on-prem and cloud

Mar 11, 2026 By SolarWinds In SolarWinds

New data shows AI is accelerating incident response, reducing noise, and closing visibility gaps across increasingly complex IT environments.

Read Post

SolarWinds

Read more about Blind spots in hybrid IT: SolarWinds report finds 77% of IT teams lack full visibility across on-prem and cloud

Runtime Validation vs Static Analysis: Why You Need Both

Mar 11, 2026 By Ken Ahrens In Speedscale

Runtime validation does not replace static analysis. They solve different problems. Static analysis catches structural defects in code before it runs. Runtime validation catches behavioral failures by testing code against real production traffic. Enterprise teams adopting AI coding tools need both layers because AI-generated code introduces a new class of defects that neither layer catches alone. According to CodeRabbit's State of AI vs Human Code Generation report, AI-generated pull requests contain roughly 1.7x more issues than human-written ones. Many of those issues pass static checks cleanly.

Read Post

Speedscale

Read more about Runtime Validation vs Static Analysis: Why You Need Both

AI Coding Agents Have a UX Problem Nobody Wants to Talk About

Mar 11, 2026 By Kush Mansingh In Speedscale

The pitch was simple: let AI write your code so you can focus on the hard problems. Three years into the AI coding revolution, and developers are focused on hard problems alright, just not the ones anyone expected. Instead of designing systems and solving business logic, engineers in 2026 spend a startling amount of their day managing the AI itself. Should you use Fast Mode or Deep Thinking? Haiku or Opus? Cursor or Claude Code or Windsurf? Should you write a SKILL.md file or a custom system prompt?

Read Post

Speedscale

Read more about AI Coding Agents Have a UX Problem Nobody Wants to Talk About

Claude outage analysis: What happened on March 11

Mar 11, 2026 By Andy Libby In StatusGator

On March 11, 2026, users around the world began reporting problems with Claude, including login failures, API errors, and stalled responses. While the disruption did not affect every user, reports quickly showed that the issue was widespread. StatusGator began receiving outage reports at 13:56 UTC. Using its Early Warning Signals system, StatusGator detected the growing incident at 14:22 UTC. The provider officially acknowledged the outage later at 14:44 UTC.

Read Post

StatusGator

Read more about Claude outage analysis: What happened on March 11

The future of Search is here: Faster, simpler, AI-driven

Mar 11, 2026 By Jack Coates and In Cribl

Do more with less. That’s the mandate we’re all hearing. AI has fundamentally changed how we work. Modern AI workloads generate 10-100x more queries than humans ever could, pushing legacy architectures past performance limits. And the audacity of it all? Legacy logging vendors continue to raise costs without delivering meaningful innovation. IT and security teams are still forced to choose between speed and retention. Investigations are still slow. Data onboarding is still painful.

Read Post

Cribl

Read more about The future of Search is here: Faster, simpler, AI-driven

Why Your NOC Will Ignore AI

Mar 11, 2026 By Yann Guernion In Broadcom

Imagine you are driving to work and a yellow check engine light flickers on your dashboard. The car feels fine. It accelerates normally, there is no strange noise, and the temperature gauge is steady. What do you do? If you are like most people, you keep driving. You might make a mental note to look at it later, but you don't pull over on the highway and call a tow truck.

Read Post

Broadcom

Read more about Why Your NOC Will Ignore AI

The bare metal problem in AI Factories

Mar 11, 2026 By David Beamonte In Canonical

As AI platforms grow in scale, many of the limiting factors are no longer related to model design or algorithmic performance, but to the operation of the underlying infrastructure. GPU accelerators are key components and are responsible for a large part of the total system cost, which makes their continuous availability and stable operation critical to the output and efficiency of the entire AI platform.

Read Post