Operations | Monitoring | ITSM | DevOps | Cloud

No one can guarantee that a website or service never goes down. What you can promise is a measured availability target, and with a multi-location, active-active design you can reach 99.999% (five nines), about 5 minutes 15 seconds of downtime a year. That takes redundancy at every layer, automatic health-based failover across regions and ideally providers, safe deployments, failure testing, and outside-in monitoring. Control Plane is built for that tier.

At incident.io we are huge fans of Postgres; we've written about it a lot over the years, including how to choose the right indexes and how we're proud of being boring (The Pet Shop Boys). We use Postgres as our primary transactional database, which as of today has ~900 tables, and counting! The vast majority of our codebase does something along the following lines: read some data from Postgres, execute some business logic, then write that data back to Postgres. It is not, however, always that simple.

AI-assisted development can increase the rate at which teams produce code, but teams only realize those velocity gains if CI can keep pace. More pull requests (PRs) mean more builds, tests, and pipeline executions. Slow jobs leave developers and coding agents waiting for feedback, flaky failures consume time in reruns and investigations, and unnecessary test execution increases runner demand as delivery volume grows.

Most AI spend comes in with no tags and no owner attached. Your provider console shows total spend, maybe broken out by API key or model. It won’t tell you that the sales team spent $1,700 on Claude this week, let alone what the work was. And the problem is growing. McKinsey found that 56% of organizations now use AI in three or more business functions. More teams means more spend, and most companies respond with a spending cap. Set it too low and you slow down the work you wanted AI to help with.

Ubuntu 26.10 “Stonking Stingray” arrives at a time when AI tools are accelerating vulnerability discovery. Patching faster is essential, but the resilience of the underlying operating system must also limit what an attacker can achieve before a fix is available.

After-hours calls can create missed messages and send urgent issues to the wrong clinician. For private medical practices, that risk grows when call answering and on-call escalation rely on the same loosely defined workflow. Answering service receives an inbound call, captures information, and categorizes the request. On-call routing identifies the responsible clinician, delivers the message, and escalates it when the clinician does not respond.

Why is the telemetry AI agents generate so intimidating? If you turn on Claude Code’s internal telemetry it’ll throw a wall of text at you. And, it’s very expensive to store. But, the bigger issue is that you can’t make sense of it. Luckily it’s all OpenTelemetry native. That means you can configure it to send, transform, and store what you really need. Which raises the only question that matters. What do you actually need?

Western Canada is becoming a live test of whether sovereign AI capacity can be built responsibly at scale, and the answer will depend less on what operators promise than on what they can measure and show. Meta’s planned C$13-billion Alberta data center, BCE’s expansion of its Saskatchewan project to a 1.2-GW hub, and the federal Responsible Data Centre Development Principles all point to the same requirement: operational transparency that regulators, utilities, and communities can verify.