AI writes code in seconds, but delivery still takes days
AI pushed throughput up 59%, yet median delivery got worse. The bottleneck moved to validation. How replaying production traffic in CI closes the gap.
Latest insights on Agentic AI workflows, cloud native architectures, and performance optimization best practices from the Speedscale team.
AI pushed throughput up 59%, yet median delivery got worse. The bottleneck moved to validation. How replaying production traffic in CI closes the gap.
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.
Metrics, logs, and traces were built for humans and cheap storage. AI inverts both assumptions, and the next maturity level is a deterministic replay sandbox.
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.
Skip hand-writing WireMock stubs. Speedscale records your real request and response traffic and exports it straight to WireMock mappings.
Logs, metrics, and traces are a lossy compression of production. Five things you can do with a traffic data lake that observability can't.
Replay an authenticated flow and the protected calls fail with 403. Here is how proxymock recommendations fix the expired bearer token in one click.
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.