ExplainerAI in production
Using ChatGPT for SRE analysis and writing
Runbook comparisons, timelines and draft updates provide checkable inputs for assistance. Incident closure also needs evidence about service recovery and unfinished work.
Articles
Guides, news and explanations about AI and reliable systems.
GuideObservability
What makes an alert worth paging for
A capacity warning needs enough lead time and a reachable responder who can act. The alert review separates useful urgency from a missing response arrangement.
GuidePlatform engineering
Where AI-generated tests fit in a release pipeline
A generated test can share the code’s mistaken assumption. Artifact identity and checks of the affected behavior explain what the passing result establishes.
ExplainerAI in production
What AIOps changes in the SRE workflow
Anomaly scores, correlated events and generated summaries provide different kinds of help. Each adds its own review, data and maintenance work.
GuideAI in production
Where workplace ChatGPT data goes
An approved summary task can take a new data path when a connector is added. Account controls, retention and output sharing apply at different points along that path.
GuideIncident response
Who can authorize the next incident mitigation?
A rollback debate can be blocked by compatibility evidence or unreachable authority. A decision worksheet separates those problems and records the recovery checks.
CommentaryEngineering leadership
SRE accountability needs authority, capacity, and a clear owner
A service owner may depend on another team’s access, risk decision or engineering time. Following a shared-database change reveals where that arrangement needs help.
ExplainerAI in production
How variational autoencoders learn telemetry patterns
The encoder, latent distribution and reconstruction objective explain why a VAE can flag a legitimate traffic change while overlooking a familiar failure.
GuidePlatform engineering
What a canary deployment actually tests
Traffic assignment determines which behavior reaches a canary. Promotion also depends on the baseline, service objectives and state left behind by rollback.
ExplainerAI in production
How GANs generate synthetic telemetry
GANs learn to resemble training data. A cascading-timeout example explains what generated traces contribute to testing and what a controlled fixture supplies.
ExplainerIncident response
Calculating mean time to detect from incident records
Worked incident records explain the MTTD formula, uncertain onset times and the effect of including failures first reported by customers.
CommentaryIncident response
Why blameless reviews investigate the conditions around a mistake
A mistaken target selection shows how an incident review can explain a responder’s choice and lead to a more useful interface improvement.