Operations | Monitoring | ITSM | DevOps | Cloud

Get Kafka-Nated Special Episode: A Kristmas Kafka

Join us for A Kristmas Kafka, an informal and deeply technical roundtable with Apache Kafka committers, contributors and community leaders. This conversation brings together the people closest to the Kafka codebase to reflect on where the project started, how it has evolved and what lies ahead for streaming systems.

Part 3: What If IT Stopped Reacting to Incidents and Started Predicting Them?

Enterprises are experiencing a turning point. Systems scale faster than teams can, AI is rewriting the rhythms of operations, and the cost of downtime grows heavier every quarter. In this new landscape, reacting is no longer enough. Teams need foresight. They need to get ahead of the issue. They need a different model entirely. This third installment centers on a simple but transformative idea. What if IT operations could finally step out of reaction mode and move into anticipation?

Detect, diagnose, and resolve network issues easily with CNM Network Health

In many organizations, developers, SREs, network engineers, and security teams work in specialized domains, which can make it hard to establish a shared view of network health. As a result, engineers often struggle to determine when a network problem that originates outside of their domain of expertise is the root cause of an incident. This lack of visibility slows investigations and delays remediation.

Driving AI ROI: How Datadog connects cost, performance, and infrastructure so you can scale responsibly

AI innovation has accelerated faster than most organizations’ ability to monitor and manage it. The shift from experimentation to production-scale workloads has driven a new class of operational challenges: rising GPU costs, opaque model performance, and the difficulty of linking spend to business value. As AI investments grow, executives need a unified way to measure efficiency and return without slowing down innovation.

Introducing Real-Time Conversations with Netdata AI

Over the past few months, we’ve seen incredible adoption of our AI Investigations and Insights reports. Teams are using them to automate the deep, thoughtful analysis required for complex post-mortems, capacity planning, and performance optimization. These comprehensive reports are fantastic when you need a well-researched, shareable document. But what about the moments during an investigation?

CTO Predictions for 2026: How AI Will Change Software Development | ShipTalk S4E7 Special Episode

In this special ShipTalk episode, host Dewan Ahmed (Principal Developer Advocate, Harness) sits down with @Harnessio Field CTO Nick Durkin for spicy—but practical—2026 predictions across AI, software delivery, DevSecOps, MLOps, and developer experience. Will we see the first “AI-caused meltdown”? Are AI “confidence scores” even trustworthy? Is 2026 the year of AI cleanup crews and recovery engineering? Nick’s take: the answer isn’t more gates—it’s guardrails, policy in the pipeline, and teams operating with the same “rulebook.”

Harness AI For Everything After Coding

AI didn’t just change how we write code. It changed everything that comes after. Application teams are shipping more code than ever with AI — but 70% of the work still happens after coding: testing, security, deployment, optimization, and keeping everything moving. As coding gets faster, delivery becomes the bottleneck. That’s where Harness comes in.

2026 Observability Predictions: What Lies Ahead?

What remains of the 2025 AI hype? After a year of “AI will fix everything” promises, engineering teams in 2025 hit a wall of reality: AI is a tool, not a magic bullet. We’re now seeing a more practical approach: identifying broken workflows and tasks where AI can help and leveraging AI strengths like data analysis at speed and scale to derive meaningful, valuable insights. Looking ahead, 2026 will reward organizations that combine AI innovation with a practical approach.

How to Integrate App Synthetic Monitoring into Your CI/CD Pipeline for Flawless Deployments Meta Description:

In today’s age of continuous delivery, a failed deployment or a drop in performance can affect thousands of users in just a few minutes. Traditional testing happens before deployment, but what about after the code is live? This is where app synthetic monitoring becomes a critical part of your CI/CD pipeline. Integrating synthetic monitoring into CI/CD transforms your pipeline from a simple delivery mechanism into a proactive quality and performance gatekeeper.

What Is An AIOps Platform? AIOps Platform Definition And Deep Dive for 2026

If you’re running a SaaS business today, you’ve probably noticed the alarms never really stop. Logs. Alerts. Tickets. They pile up faster than many teams can triage them. Add multiple clouds, microservices, and AI-driven workloads, and suddenly, your “always-on” infrastructure feels like it’s always on fire. AIOps platforms promise to connect dots that human teams struggle to see fast enough. For engineers, these include surfacing root causes and outwitting outages.