I built an API traffic classifier for business workflows
I pointed a classifier at 7,943 API calls to find business workflows. It worked, then showed where my assumptions about sessions and patterns fell apart.
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I pointed a classifier at 7,943 API calls to find business workflows. It worked, then showed where my assumptions about sessions and patterns fell apart.
Compare eBPF, sidecars, telemetry agents, OpenTelemetry, APM, and proxies through an evolving ladder of production visibility.
How our nettap eBPF agent produced garbage HTTP bodies from kernel iov_iter scatter-gather buffers, and the CO-RE plus task-local storage fix that made it correct.
The best model isn't the smartest — it's whichever should get the next unit of work. Notes from NVIDIA on routing, local models, and the AI factory.
A production bug survived two confident fixes and green tests. Replaying the captured request exposed the missing state and proved the real fix.
Our v2 release looked clean in HTTP tests until we diffed the SQL workload — an N+1 loop, a startup migration, and 70 ms of extra DB time hiding in plain sight. Here's how to compare two releases without database access.
The trace was sampled out. I found the bug anyway — by filtering recorded traffic on the customer's email instead of a trace ID. Here's how to follow one request across four services with no trace IDs and no OpenTelemetry.
A second run of our AI bug-fixing benchmark shows where captured traffic lifts agents toward 90%, why service maps barely help, and which bugs still fail.
Using 'production-similar' data in pre-production is a major security risk. Learn why traditional masking fails, where hidden PII hides, and how to fix it.