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speedscale

Application Level Dependency Chaos Testing

Aug 26, 2026 By Matt LeRay In Speedscale
Somewhere in your service is a branch that has never executed. Not a rare one, a never one. It handles a dependency being unavailable: it reads from a cache, it returns a stale value, it marks the response degraded so callers know not to trust it too far. It was written carefully. It was reviewed. Whether it works is an open question, because nothing in the test suite makes that dependency fail, and the dependency does not fail on request.
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speedscale

From Telemetry to Traffic

Aug 19, 2026 By Matt LeRay In Speedscale
A metric says latency increased. A log says a request failed. A trace identifies the slow dependency. An APM agent points to the method. Manual instrumentation explains the business operation. Traffic capture shows the exact request and response that triggered it. Each layer answers a question the previous layer could not. Each also introduces a new cost, blind spot, and failure mode.
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speedscale

Reliability Engineering in the AI Era

Aug 19, 2026 By Matt LeRay In Speedscale
Engineering leaders have been claiming to “shift quality left” for years but production remains stubbornly stuck out of reach of software engineers. The realm of production remains mysterious with tools no one has access to and UIs that wouldn’t make sense to engineers anyway. I’ve noticed a small but growing trend of large enterprises hiring Reliability Engineers instead of Site Reliability Engineers. Dropping one word looks cosmetic but I think it points to a much bigger change.
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speedscale

Chaos Monkey Won't Find Your Bug

Aug 17, 2026 By Matt LeRay In Speedscale
We shipped a chaos feature that never caused any chaos. Our mock server has had a fault-injection effect for years with a straightforward job: withhold the response entirely and see whether the caller copes. Last week I audited it against the actual code path. It had never withheld anything. The handler returned early without writing a response. Go’s net/http then did what it is designed to do, which is synthesize a 200 OK and flush the recorded body.
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speedscale

Synthetic Monitoring Is Broken. Your Production Traffic Can Fix It.

Aug 13, 2026 By Ken Ahrens In Speedscale
Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.
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Observe Opaque Services With OpenTelemetry eBPF + proxymock

Aug 12, 2026 By Matt LeRay In Speedscale
Every SRE team operates services it cannot see into: a vendor binary, an inherited legacy deployment, a container whose owning team dissolved two reorgs ago. The routes are undocumented, the dependencies are unknown, and when a request takes 130 milliseconds nobody can say whether that time is application work or a wait across a network boundary.
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Flamegraphs Find It. Replay Proves It.

Aug 11, 2026 By Matt LeRay In Speedscale
I made an API endpoint 13 times faster. Then I realized my first verification only checked the status, headers, and response schema. I had not checked the totals. I had made the bug faster. That is the problem with giving an AI coding agent one kind of evidence. A CPU profile can show where the application is slow, but not whether an optimization preserves behavior. A traffic replay can prove that behavior stayed stable, but not explain why the code burns CPU. This walkthrough gives the agent two independent witnesses: Together, they turn AI code verification into an experiment with two independent checks.
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speedscale

The Pod Was Cheaper. The Service Wasn't.

Aug 11, 2026 By Matt LeRay In Speedscale
A smaller Kubernetes pod can lower allocation cost while completing less work. Green status codes and matching schemas can hide it. This walkthrough combines OpenCost allocation data with proxymock behavior and performance evidence. A candidate passes only when behavior and throughput hold while unit cost falls.
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Diagnose Serial N+1 API Calls With Tempo + proxymock

Aug 11, 2026 By Matt LeRay In Speedscale
One API request took 302 milliseconds. Nothing failed. CPU was mostly idle. The response was correct. The trace made the problem obvious: eight inventory calls, each waiting for the previous one. But the trace could not tell me why the application made eight calls, or whether changing their execution would preserve the response. It showed the shape of the wait, not the input that created it.
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