Operations | Monitoring | ITSM | DevOps | Cloud

Non-deterministic UIs are the future and will replace your dashboards, but they’re not here yet. So until then, we’re stuck with conversational interfaces. In an effort to try and describe what I consider the future of UIs to look like, I wrote about how you (and I) have been designing dashboards wrong. The core insight was that we've been designing for static representations of data that sit on a TV in the office, when the actual use case is someone at a desk using them to debug an issue.

Cursor is fantastic at cranking out code changes. I recently used it to splice a brand-new downstream API call into one of our Go microservices, and the diff looked great. The unit tests finished before I lifted my coffee mug, yet I still had zero certainty the change would survive contact with real traffic. That gap is all about integration tests, so I paired Cursor with proxymock and the outerspace-go demo service to prove the behavior end to end.

Life is pretty good. I’ve shipped a mobile app and I’m (happily) drowning in telemetry. Battery impact, time in foreground/background per screen, crash rates, slow frames, network retries – the works. The data is brilliant; the challenge is turning signals into reliable alerts that catch real issues which are relevant to my app’s functions. So… what should I actually listen for, and where should I set the thresholds?

Everyone wants to move faster with AI, but few are truly ready for it. This is where organizations can position themselves better as AI readiness isn’t just about algorithms, it’s about whether your data, systems, and teams are prepared to support intelligent automation safely and effectively.

Recently I've been to Lisbon's Web Summit conference, a 3 day, 70,000 participants, 15 stages, 800+ speakers event. Even though there was a track called "AI Summit", all the talks were about AI and AI Agents and how the future of the web, business, economy is more and more AI, and how businesses and people should take steps to adapt as soon as possible to an online world managed and operated by Artificial Intelligence.

When we introduced Canvas in beta, our goal was to reimagine how teams explore and collaborate around their observability data without requiring manual querying. Canvas has quickly become the AI-guided workspace that helps teams transform raw telemetry into meaningful, shared understanding faster than ever before. And today, we’re thrilled to announce that Canvas is now Generally Available (GA) for all Honeycomb users.