Two confident fixes missed this production bug
A production bug survived two confident fixes and green tests. Replaying the captured request exposed the missing state and proved the real fix.
Posts about developer tools
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The API tests passed. The database didn't.
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.
Trace without traces
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.
Which Bugs AI Agents Fix Better With Traffic
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.
Beware of PII in Testing Data: The Security Iceberg and Where PII Actually Hides
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.
Fixing 403 auth errors when you replay traffic
Replay an authenticated flow and the protected calls fail with 403. Here is how proxymock recommendations fix the expired bearer token in one click.
Capture once, test forever
Production traffic is the most complete record of what your system does, and most teams throw it away. One capture powers reproduce, validate, and sandbox.
Logs told me something broke. Traffic showed me what.
Capture production traffic and store it in your own Elasticsearch with Speedscale BYOC. Pull it locally with es-gather.py and reproduce bugs with proxymock.
Your AI agent is fixing the wrong service
I tested 100 bugs across 240 microservices the model has never seen. Alert only: 51% pass rate, wrong service 34% of the time. Traffic captures: 77%.