Operations | Monitoring | ITSM | DevOps | Cloud

Software used to be built by engineers. Not any more. Low-code tools brought in domain teams. AI copilots brought in everyone else. And now agents are building and acting alongside humans; marketing, finance, HR and legal are all shipping the apps they used to file tickets for. The number of people (and things) building on your data has exploded, and it isn't slowing down. And there's no single, standardized way to build, there likely never will be.

Everything that makes an app or agent real happens after it works on your machine. Locally coding an app is a joy: hot reload, a seeded database, a mocked API key. Then you go to ship it, and "deploy" quietly expands into a Dockerfile that behaves in CI, somewhere to actually run the container, a database it can reach, TLS, secrets wired into the environment, and a pipeline to hold it all together. The feature took an afternoon. The plumbing takes the rest of the week.

Ask a simple question "who are our most profitable customers?" and things fall apart. The data lives in six systems, nobody agrees which table is canonical, the column called profit is actually revenue, and the business rules that matter live in someone's head or a Confluence page nobody's touched since 2023. Now point an AI agent at that same mess.

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.

Ask a platform team why their observability bill keeps growing and you'll often get a one-sentence answer: And that's usually where it ends. The application teams own the log output, the platform team owns the bill, and nobody has the leverage to change what gets emitted. A single retry loop can print the same error thousands of times a minute. Every one of those lines is ingested, indexed, and stored. You pay for all of them, and they tell you exactly one thing: this error happened, a lot.

Status pages now have a place for the last step of an incident: the post-mortem. Once an incident is resolved, you can write what happened, why it happened, and what you are changing, then publish it on the incident itself. Until now, the updates you posted during an outage ended with "Resolved", and the explanation lived somewhere else: a blog post, a PDF sent to a few customers, or an email thread. Customers who read the incident on your status page never saw it.

Datadog Real User Monitoring (RUM) SDK settings live in your application code, so changing how the SDK collects RUM data has traditionally required shipping a new application version. These configuration changes can include adjusting sampling rates, enabling Session Replay, or changing which events the SDK collects. For mobile teams, this means that updates often sit in app store review for days or weeks before users start adopting the new version. Full user adoption can take weeks or months longer.

Logs are easiest to investigate when the values that matter are already captured as attributes. When those values are buried in a log message, even a straightforward question such as filtering on a status code, graphing the duration of a request, or following a unique transaction across a set of logs requires writing complex regular expressions or Grok patterns.