Operations | Monitoring | ITSM | DevOps | Cloud

15 AWS cost optimization tactics across compute, storage, networking, and monitoring plus a quick-reference table by effort and savings potential.

Large language models are effective at security review for the same reason they are effective at many other tasks: they reason rather than pattern match. In plain terms, a traditional scanner checks code against a list of known bad patterns, the way a spell checker flags a misspelled word, regardless of what the sentence means. An LLM can instead follow the program's logic: trace a piece of attacker-controlled input through several layers of application code to determine whether it is reachable.

The hard part of a feature flag is rarely adding it. It is remembering to remove it months later, when the rollout is over, the original context has faded, and there is always a more urgent piece of work waiting. Since April 2026, one Atlassian team has used Agentic Pipelines to clean up their monthly backlog of stale feature flags. The workflow prepares the change and opens a pull request, while engineers still review and merge the pull request.

Improving agent experience (AX) is one thing. Keeping it good as your product changes is harder. A renamed field, different error response, or overlapping tool can turn a workflow that worked yesterday into extra retries, wasted tokens, or human intervention. CI gives teams a way to catch AX regressions as part of the development process. You can test the interfaces agents depend on, run representative agent workflows against product changes, and preserve fixed failures as regression cases.

Many universities and academic institutions use data centers for a wide range of purposes: secure data storage for student and faculty information, learning management systems, and administrative operations. Universities also use data centers for research and data analysis. Research, simulations, machine learning models, and data analysis all require high-performance compute.

AI is rapidly becoming a bigger part of how IT Operations teams detect incidents, interpret signals and decide what to do next.

So you want your own pet LLM huh? Knowing where to start can be quite tricky, so luckily for you I’ve put together this end-to-end guide. It’ll get you not just started; you’ll end with a fully working chatbot that you’ve fine tuned on the dataset `nampdn-ai/tiny-webtext`, which is a training dataset designed to improve models’ critical thinking abilities. Buckle up, this is going to be both fun and deep.

How shared Android capacity helps engineering teams move beyond fixed device labs In the first blog of this series, we discussed how programmable Android environments can replace manual device preparation with a repeatable lifecycle. A workflow requests an environment with a predefined configuration, executes the required task, collects the results, and releases the resources once the work is completed. Automation enables a team to create a single Android environment reliably.

A couple days ago, I told you how a spammer got a certificate for dev-docs.trackjs.com, and that we only found out because Google emailed us. Google knew because the spammer claimed the hostname in Search Console. An attacker running a phishing page wouldn’t have done that, but they would still need a certificate. Every publicly trusted certificate gets written to a public log, and we track that log in our database. We just weren’t watching it. Now we are, and you can too.

This week marks the end of our fourth Civo Navigate London event, and it feels like a good moment to say that this one had a slightly different energy from the ones before it. Over the past four years, we have hosted 10 Civo Navigate events across North America, India, and Europe, and each one has taught us something new about what this community actually wants from a day like this. London 2026 was no exception, and I think this year's lineup pushed that a little further than usual.