Rocky reads your stack, not just your check
Rocky AI root cause analysis now reads OpenTelemetry traces. When a Checkly check fails, Rocky walks the backend span chain to find the root cause.
Read the full story on Checkly BlogHave you worked with this?
The story is what was announced. Nobody has discussed it yet, so if it touches your team, a short post about what you’ve seen helps the next reader.
More from Checkly
Recent updates from Checkly, so you can tell whether this is a one-off or part of a pattern.
We Let AI Agents Rewrite a 92M-Message-a-Day Service in Go. Zero Incidents.
How Checkly rewrote a Node.js service handling 92M messages a day into Go using AI agents, and the test harness that made it safe to ship.
Metabase Security Incident
On 3 August 2026, an attacker exploited a zero-day vulnerability in Metabase, the third-party analytics tool we use internally, and gained read access to a database holding a copy of Checkly operational data. The attacker bypassed authentication and obtained an administrator session on our Metabase Cloud instance. Metabase has since blocked the attack, patched the vulnerability, and published a…
CLIs are more token-efficient than MCP. Or are they?
MCP servers used to be the token-hungry option. Deferred tool loading and resource links changed that. A side-by-side Playwright MCP vs CLI token comparison.
More in Observability
What other companies in Observability are doing. The category page shows who’s active, side by side.
Cribl Privacy Model lost 20 heads and got 2.8x faster
Extend Datadog RUM and Product Analytics to Shopify and Salesforce
Use Datadog RUM and Product Analytics to monitor checkout journeys on Shopify and customer experiences in Salesforce Experience Cloud.
DevRel newsletter: September 2026
Hello from the Elastic DevRel team! In this newsletter, we cover jina-ocr-v1, the latest blogs and videos, and upcoming events like Elastic{ON}.
What if your agent's hallucinations had a budget? How to start using SLOs for agent behavior
At Grafana Labs, observability is what we do. So as we started building AI agents, we naturally reached for the same instincts we bring to every system: measure it, set targets, and make reliability something you can reason about instead of hope for. That instinct led us somewhere unexpectedly useful. It turns out one of the oldest ideas in reliability engineering, the error budget, maps…