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We've been shipping faster. A dark mode for the UI, AppSignal MCP, the AWS dashboard templates — things we would have kept internal a year ago until everything was polished. Now we don't. A v1 in your hands beats a v3 in our heads. We learn more from a week of real use than from a quarter of internal review. So we're giving that work a home. AppSignal Labs is where you'll find the earlier versions. Real software, available today, with a direct line to the team building it.

Local development and remote validation were always meant to work together: developers iterate on their machine, run a few manual checks, then push to CI to clear code for production. But AI development broke that balance, flooding CI with a volume of commits no developer has read, let alone tested. Chunk sidecars restore the balance: lightweight, preconfigured environments that run alongside your local workflow and validate changes as they happen.

Edwin AI extends ServiceNow Now Assist with real-time incident intelligence, acting as a context broker between observability data and ServiceNow incidents. Responders get the context they need inside the IT operations workflow they already use. Edwin AI now: The Edwin AI Agent for ServiceNow brings real-time incident intelligence into Now Assist and Workspace, giving ITOps teams root cause, impact, and recommended next steps directly inside the ServiceNow incident record.

When was the last time you asked a developer if they actually use the platform you built for them, or whether they’ve found a faster way around it? We talk with companies every day who deal with this exact scenario. They spend months or even years building their IDP. Then a new project requires a stack or workflow that the IDP doesn’t support. The developer is under pressure to deliver, so they spin up their own solution. This is why most IDPs fail quietly.

AI promise and AI capabilities often diverge, with developers often reporting much faster code production, but not enough change in how incidents are handled. When the rate of change is faster than ever, but the rate of recovery from incidents isn’t moving, developers wind up stuck in firefighting mode. And, when these systems fail, it’s costly. According to PagerDuty’s State of AI-First Operations, over a third of surveyed companies report losing $500K per hour of downtime.

We’ll be rolling out new Slack capabilities to eliminate more manual toil from your incident workflow: click once to promote any alert to an incident, get dedicated channels created automatically, page responders without leaving Slack, and manage all your settings in one place. This is part of our path to autonomous operations: reducing toil, protecting your capacity, and letting you stay in flow. If you’re only using PagerDuty for on-call scheduling, you’re missing the full picture.

Lots of big companies lock down their IT infrastructure in the internal network, sometimes they even use only locally mirrored repositories. I totally understand this, especially since our CVE-2024-49369. Nowadays, when LLMs find security holes even in OpenBSD, you definitely shouldn’t expose any services to the public without need.

There’s a new question teams are asking. How can we prevent AI agents from deleting production. When Cursor deleted PocketOS’s entire production database in nine seconds, the agent wasn’t malfunctioning. It had full technical capability, but it was inferring operational authority from static code rather than live environment state. That gap between capability and context is the root cause. This article breaks down exactly how that happens, and what runtime visibility does to stop it.