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logicmonitor

How to Reduce MTTR with AI

Mar 26, 2026 By Margo Poda In LogicMonitor
The quick download: AI reduces MTTR by helping teams detect issues sooner, pinpoint root causes faster, and resolve incidents with less manual effort. IT downtime costs organizations an average of $9,000 per minute. AI-powered observability can cut incident resolution time by up to 70%. Here’s what it takes to get there. Every minute an incident goes unresolved, the meter is running.
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Automate Your Monitoring and Incident Handling: How Agents Dominate the Checkly CLI

Mar 26, 2026 By Checkly In Checkly
50% of Checkly's CLI users are already coding agents. We predict that agents will become dominant by the end of 2026. This video demonstrates an agentic workflow where an alert reports a broken Shopify store login flow, and Claude Code, using the installed Checkly Skill and the Checkly CLI, pulls monitoring results, identifies a Playwright test failure, investigates the codebase, finds and fixes a bug, and then updates a Checkly status page by creating an incident.
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datadog

Introducing Bits AI Dev Agent for Code Security

Mar 26, 2026 By Kassen Qian In Datadog
As organizations adopt AI-assisted development and increase their release velocity, they are not only generating more code but also finding more vulnerabilities from static analysis. The traditional remediation workflow of manually triaging issues, creating tickets, and opening individual pull requests (PRs) cannot keep pace. Fixing tens of thousands of vulnerabilities one by one is not a viable remediation strategy.
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datadog

Datadog achieves ISO 42001 certification for responsible AI

Mar 26, 2026 By Aaron Ta In Datadog
As AI-powered products and services become central to how organizations operate, the need for responsible AI governance has never been greater. Customers, partners, and regulators are seeking assurance that AI systems are built, managed, and monitored responsibly and effectively. Datadog is committed to the responsible use of AI, both in how we build our products and in how we help customers observe their AI workloads.
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harness

LiteLLM Compromise: Securing AI Pipelines from PyPI Supply Chain Attacks | Harness Blog

Mar 26, 2026 By Pranay Shah In Harness
On March 24, 2026, the AI open-source ecosystem was impacted by a critical supply chain attack involving the widely used Python package LiteLLM. Attackers compromised the LiteLLM PyPI distribution pipeline and published malicious versions (notably in the 1.82.7-1.82.8 range), embedding a multi-stage payload designed to steal credentials and execute remote code.
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harness

AI Deployment in Production: Orchestrate LLMs, RAG, Agents | Harness Blog

Mar 26, 2026 By Chinmay Gaikwad In Harness
For the past few years, the narrative around Artificial Intelligence has been dominated by what I like to call the "magic box" illusion. We assumed that deploying AI simply meant passing a user’s question through an API key to a Large Language Model (LLM) and waiting for a brilliant answer.
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cloudzero

Jensen Huang's warning: lead the AI transition - or finance it

Mar 26, 2026 By Erik Peterson In CloudZero
The wrong people got the most attention from Jensen Huang’s comments last week. Huang told the All-In Podcast that he’d be “deeply alarmed” if a $500,000 engineer consumed less than $250,000 in AI tokens annually. Within 48 hours, the discourse collapsed into a compensation debate.
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#054 - From Shiny Objects to FinOps: Taming Cloud Costs in the AI Era with Josh Schlanger (CloudX...

Mar 26, 2026 By Komodor In Komodor
In this episode of the Kubernetes for Humans podcast, we are joined by infrastructure and FinOps expert Josh Schlanger. Drawing on over 15 years of experience across Martech, e-commerce, and health tech, Josh shares why solving core business problems should always take priority over chasing new, "shiny object" technologies.
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cortex

QA, AI, and the return of the adversarial mindset

Mar 26, 2026 By Cortex In Cortex
The best QA engineers are always asking themselves (and others around them) what might break. When engineering teams shifted to agile delivery, that mindset largely moved out of dedicated roles and into the background. Automated testing took over the repetitive work, developers owned quality end-to-end, and velocity improved. What didn't carry over was the habit of looking at a feature and asking how a real user, an edge case, or unexpected load might expose it.
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