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speedscale

Part 3: Building a Production-Grade Traffic Capture and Replay System

Nov 12, 2025 By Josh Thornton In Speedscale
At a previous company, we had over 100 microservices. I’d make what seemed like a simple change to one service and deploy it, only to discover it broke something completely unrelated. A change to the user service would break checkout. An update to notifications would break reporting. We spent more time fixing unexpected bugs than shipping features. The problem was our test scenarios were too simple.
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Speedscale Proxymock: Freely testing cloud native apps alongside AI code assistants

Nov 6, 2025 By Jason English In Speedscale
We’ll always remember 2025 as the year AI code assistants went big. Copilot, Cursor, Claude, Windsurf, whatever. Developers went from mistrusting these tools, to being expected to turn over much of their coding labor to them. Even if, according to an extensive Stack Overflow survey, only 3 percent of professional developers say they ‘highly trust’ AI coding tools.
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speedscale

Part 2: Building a Production-Grade Traffic Capture, Transform and Replay System

Oct 31, 2025 By Matthew LeRay In Speedscale
When developers try to build realistic mocks and automated tests from production network traffic, the real challenge isn’t just in the capturing—it’s in the data manipulation. Raw traffic is a chaotic sea of patterns, dynamic tokens, environment-specific secrets, and tangled dependencies that seem impossible to untangle by hand. Over my two decades of building these sytems, I learned that solving this problem requires more than brute-force parsing or ad hoc scripts.
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speedscale

Settle Your QA Debt Before the Bugs Start Breaking Kneecaps

Oct 27, 2025 By Alan Mon In Speedscale
In Part One, we discussed how QA debt builds silently over time — causing slower releases, late-night firefights, and unpredictable test cycles. The next step is understanding how much debt you have and where it hides. This post goes deeper into measuring QA debt — what to track, how to collect data, and how to use those insights to create a sustainable plan for improvement.
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Mitmproxy vs Proxymock: Replaying Traffic for Realistic API Testing

Oct 21, 2025 By Matthew LeRay In Speedscale
Replaying traffic is a core tool in your toolbox when you need to reproduce a tricky bug or validate how your app behaves. Traffic replay is especially valuable for testing complex software applications that rely on APIs and microservices, where integration and functionality must be thoroughly validated.
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speedscale

Part 1: Building a Production-Grade Traffic Capture and Replay System

Oct 21, 2025 By Ken Ahrens In Speedscale
A few years ago I was on call during the Super Bowl. At the time I was working for an observability vendor and one of our customers had an outage caused by a surge in user traffic. But our monitoring system didn’t have enough data to know what went wrong and I sat on a call for 2 hours painfully listening to them spinning up more servers and trying to catch up with the user load.
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Debugging Without a Net: The Pain of Reproducing Production Issues

Oct 20, 2025 By Alan Mon In Speedscale
Every engineer has been there — a late-night page, a broken feature in production, and no clear way to reproduce it. The logs are vague. The metrics look normal. Your local environment works fine. Yet something somewhere is failing for real users. So begins the detective work — debugging a live system with almost no tools, no perfect test data, and no clone of production.
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MySQL Mocking with Speedscale's Proxymock: A Complete Guide

Oct 15, 2025 By Alan Mon In Speedscale
Testing database-driven applications is notoriously painful. If your app depends on MySQL, you’ve probably spent hours setting up local databases, running migrations, loading data, and then cleaning everything up just to rerun your tests. This repetitive cycle slows development, breaks pipelines, and introduces inconsistency between local and production environments.
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A Developer's Guide to Improving AI Code Reliability

Oct 13, 2025 By Matt Tanner In Speedscale
You've probably been there: your AI coding assistant just generated what looks like a perfect solution to your problem. Decent code quality, reasonable structure, and even some comments. You run it, and... it works. So you ship it. Three weeks later, your production logs are full of 500 errors from edge cases the AI never considered, or worse, you discover the code has been making unvalidated database calls that could have been prevented with basic input sanitization.
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QA Debt: The Silent Risk That Can Take Down Your Business

Oct 10, 2025 By Alan Mon In Speedscale
In engineering, we talk a lot about technical debt — the shortcuts and compromises made in code that pile up over time. But there’s another kind of debt that’s just as dangerous and far more invisible: QA debt. QA debt is what happens when testing isn’t given the same attention as features, architecture, or performance. It’s the accumulation of missed edge cases, outdated test suites, incomplete automation, or skipped regression checks.
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