Operations | Monitoring | ITSM | DevOps | Cloud

The Future is Faceless: Why Stock Footage is Dying in 2026

Remember the last time you searched for "diverse business team laughing at laptop" on a stock footage site? You scrolled past the same forced smiles, the same generic office backgrounds, and the same overacted "eureka" moments that have been circulating for a decade. Then you paid a subscription fee for the privilege of looking like every other brand on the planet. That era is ending. In 2026, stock footage is dying-not because we need fewer visuals, but because creators have finally found something better: total creative freedom without the cheesy middleman.

Scaling AI Workflows With Proxy Infrastructure

AI workflows require consistent access to diverse data sources to maintain accuracy. How do teams guarantee that their systems do not go dead when rate limits are reached? The scaling of these processes is based on a stable connection layer that eliminates interruptions during retrieval. Writers are likely to have difficulties with their automated scripts triggering blocks on social sites. This article discusses the process of establishing a trustworthy machine learning and automation environment.

Buy vs Build in the Age of AI (Part 1)

A few months ago, I spoke to an engineering manager who proudly told me they had rebuilt their monitoring stack over a long weekend. They’d used AI to scaffold synthetic checks. They’d generated alert logic with dynamic thresholds. They’d then wired everything into Slack and PagerDuty, and built a clean internal dashboard. “It used to take us weeks to prototype something like this,” they said. “Now it’s basically instant.” They weren’t wrong.

Introducing Rocky AI to General Availability

After months of being available in Beta for our app users, Rocky AI is now generally available to all users and plans. Rocky AI is Checkly’s AI agent that works around the clock, 24/7, to make sure your application’s reliability is optimal. In this first release, Rocky AI ships with the ability to run continual Analysis on test and check failures, giving your teams AI-powered root cause analysis, impact analysis, and more.

We Turned Our WireShark Wizard Into a Markdown File

Rocky AI — Checkly’s AI agent — is now Generally Available. We developed Rocky AI over the last ~6 to 8 months. This is an aeon in AI-years. During this period, we learned a ton. About AI, but mostly about how to fit them into an existing SaaS product, not just another chat widget. This is my ramble…

How to Build AI-Native Security Resilience (And Finally Get Developers And Security On The Same Team) | Harness Blog

Developers and security professionals have struggled to get on the same page for what seems like forever and AI is only making that divide larger, according to results from our State of AI-Native Application Security 2025 research report.

Hot Takes: What the AI Hype Gets Wrong About Software Engineering Excellence | Harness Blog

Ahead of the DevOps Modernization Summit, Matthew Skelton, CEO & CTO of Conflux shares his takes on output-driven AI, how DORA metrics aren’t enough, and why governance and compliance must be built into the platform. ‍ Matthew Skelton is the CEO & CTO of Conflux and a featured speaker at this year’s DevOps Modernization Summit. Ahead of our annual summit, Matthew has shared his hot takes on AI, DORA, and the key to successful automation.

7 Real Ways to Modernize NetOps with Kentik AI Advisor

Kentik’s AI Advisor acts as a virtual network engineer, helping teams of all skill levels troubleshoot, manage, and optimize their infrastructure with unprecedented speed and context. We explore seven practical NetOps use cases, from rapid incident triage and capacity planning to upcoming live-device command support, that demonstrate how using AI as a collaborative teammate dramatically reduces manual investigative work.

Skills vs. MCP: You're probably reaching for the wrong one

Everyone is adding Model Context Protocol (MCP) servers to everything right now. And I get it. MCP is clean. It’s standardized. You write a server, expose some tools, and suddenly your LLM can query your log platform, pull a dashboard, and fire an alert. It feels like the right abstraction. But I’ve watched teams at serious companies burn weeks building MCP integrations for workflows that should have been skills, and build skills for things that genuinely needed MCP.

Inside Pandora's Box: How CloudZero AI Hub Cracks Cloud Cost Intelligence

Years in the FinOps trenches taught me one thing: The data has never been the problem. The data exists. It’s out there, scattered across provider invoices, buried in tagging gaps, locked behind dashboards that maybe three people in your org actually know how to navigate. The real problem? Nobody can get to it when they need it. Engineers ship features without understanding what they cost the business, let alone whether they improved margin.