Operations | Monitoring | ITSM | DevOps | Cloud

Outrun the Threat Window: AI-accelerated Vulnerability and Patch Management

Sep 2, 2026 By N-able In N-able
The gap between vulnerability disclosure and active exploitation is shrinking—often from weeks to mere hours. Traditional patching cycles no longer cut it. In this session, discover how AI-accelerated solutions can help you: Identify exposed assets faster Prioritize vulnerabilities by real-world risk Remediate across Windows, macOS, Linux, and hundreds of apps Verify success with a connected workflow Learn how our approach, powered by AI-driven insights and automation, can help you close the gap before attackers strike. Watch now and take control of your patch management.
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SaaS Sprawl Is Becoming an IT Problem: Here's How to Bring It Under Control

SaaS Sprawl Is Becoming an IT Problem: Here's How to Bring It Under Control

Sep 2, 2026 By OpsMatters In OpsMatters
For most organizations, SaaS sprawl does not begin with a bad technology decision. It starts with a useful tool. Marketing needs a new analytics platform. Sales adopts prospecting software. HR adds an applicant tracking system. Engineering signs up for another monitoring service. Someone discovers an AI tool that saves several hours a week and puts it on a company card. Each purchase makes sense on its own.
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From AI Prototype to Production: The Technical Architecture Enterprises Need

From AI Prototype to Production: The Technical Architecture Enterprises Need

Sep 2, 2026 By OpsMatters In OpsMatters
Building a generative model that spits out flawless answers in a controlled notebook feels like a massive win for any engineering team. But watching that exact same model crash the second it hits real, concurrent user traffic? That is a frustrating reality check. The gap between a slick proof of concept and a mission-critical deployment is surprisingly wide, and it almost always comes down to the underlying infrastructure. If your systems cannot handle the dynamic load, the smartest algorithm in the world will not save you.
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Why AI Adoption Fails Without the Data Work First

Why AI Adoption Fails Without the Data Work First

Sep 2, 2026 By OpsMatters In OpsMatters
Most enterprise AI projects don't fail because the model wasn't good enough. They fail because the data underneath was a mess before anyone switched anything on. Duplicated contacts, contradictory fields, records that haven't been touched in three years but are still floating around in production tables. The AI doesn't know any of that context. It just reads what's there and runs with it.
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cloudzero

AI usage tracking: Monitor spend by team, feature & model

Sep 1, 2026 By Lyne Carolyne In CloudZero
AI usage tracking means measuring who and what consumes AI across your company, by team, feature, and model, then converting the usage into spend and cost per unit of work. Provider consoles stop at totals per API key. Tracking puts names on those totals: which team, which product, which model, and whether any of it was worth the money. In May 2026, CNBC reported that “almost every Fortune 500 is tracking overall AI usage,” quoting ModelOp CTO Jim Olsen. The same reporting carried his warning.
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upsun

The gap between individual AI productivity and team performance

Sep 1, 2026 By Upsun In Upsun
As a product manager at Upsun with a computer engineering background, Kateryna Dvornichenko had spent months researching competing tools in the agentic development space, running tests, comparing features, and building a picture of where the market was heading. She realized the tools were impressive, but something kept standing out. "Collaboration was not the strong point of any of them," she says. "Everyone stays on their own machine with their own setup.".
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AI can write database code fast. Here's how to keep it safe before production.

Sep 1, 2026 By Redgate Software In Redgate
AI can write database schema changes in seconds, but nothing should reach production until it's validated, tested, and approved. In this discussion, Ken Muse (GitHub), Steve Jones (Redgate), and Huxley Kendall (Redgate) show how a governed pipeline keeps AI-generated database changes safe without slowing teams down.
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How to set up CircleCI with Cursor Origin

Sep 1, 2026 By CircleCI In CircleCI
CircleCI now integrates with Cursor Origin, bringing scalable CI/CD to Origin-hosted repositories. In this demo, see how to connect an Origin repository to CircleCI, configure your pipeline triggers, run a build, and report CI status back to your Origin pull request. Already using CircleCI? Your existing.circleci/config.yml works as-is, with no Origin-specific CI syntax or separate config to maintain.
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How to Use Claude Code with CircleCI to Fix Failed Builds

Sep 1, 2026 By CircleCI In CircleCI
Give Claude Code direct access to CircleCI and let it diagnose failed builds, fix issues, and keep iterating until your pipeline is green. In this tutorial, we walk through how to connect Claude Code to CircleCI using the CircleCI CLI. You’ll see how Claude can read pipeline results, identify test failures, make fixes, trigger new builds, and monitor CircleCI without leaving the terminal.
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checkly

We Let AI Agents Rewrite a 92M-Message-a-Day Service in Go. Zero Incidents.

Sep 1, 2026 By Edvinas Janusevicius In Checkly
Our Results Daemon processes about 92 million messages a day. We recently rewrote it from Node.js to Go, and we let Claude Code write it. We wanted to know whether we could trust an agentic rewrite for a critical, high-throughput production service rather than a prototype. It shipped with zero incidents, a 70% reduction in running pods, and a lighter database load.
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