AICASSINDRA · LEARN BY EXPERIMENTING

Observability labs

Explore the interactive side of AICassindra. Read a topic, test an idea, and follow the results.

Choose a topic, explore the model, and compare what changes. Each experiment explains its assumptions and limitations.

Browse the collection below.

Observability

Agent metrics: count the requests behind the chart

Inspect counters, completed-request windows and real cumulative latency buckets.

Interactive lab
Observability

Agent observability: trace a failed dependency

Inspect actual span IDs, parent relationships, sampling decisions and a deterministic execution schedule.

Interactive lab
Observability

Agent SLOs: define good events before judging the target

Compare availability with successful-and-fast service using exact rolling denominators.

Interactive lab
Observability

Alert fatigue

Explore the controls, compare outcomes and record your observations.

Interactive lab
Observability

Deep trace inspection: critical path versus span totals

Inspect actual span IDs, parent relationships, sampling decisions and a deterministic execution schedule.

Interactive lab
Observability

Golden signals: watch a service saturate and recover

A bounded FIFO, real request outcomes and four measured service signals.

Interactive lab
Observability

Latency percentiles: what does p99 actually select?

Real editable samples, exact ranks and reproducible synthetic cohorts.

Interactive lab
Observability

Prompt records: retention and backup recovery

Follow actual fictional records through primary storage, a backup, an export and a restore.

Interactive lab
Observability

SLO burn rate: when should this alert clear?

Two real rolling windows. Request-weighted counts. An explicit budget projection.

Interactive lab
Observability

Trace observability: distinguish execution from export

Inspect actual span IDs, parent relationships, sampling decisions and a deterministic execution schedule.

Interactive lab
Observability

Trace sampling: what disappeared before the decision?

One reproducible cohort, two policies and explicit resource limits.

Interactive lab
Observability

Trace visualization: inspect the waterfall

Inspect actual span IDs, parent relationships, sampling decisions and a deterministic execution schedule.

Interactive lab
Observability

Traceparent: validate and propagate version-00 context

Inspect actual span IDs, parent relationships, sampling decisions and a deterministic execution schedule.

Interactive lab

Views are my own and do not represent any employer, past or present.