Let Your LLM Debug Using Production Recordings
Connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster using Speedscale and proxymock.
Posts by Matthew LeRay
Matthew LeRay is a contributor to the Speedscale blog. • 9 posts published
How to Do Full-Text Search Across All Application Traffic with Speedscale
This how-to video shows how to use Speedscale's full-text traffic search to instantly find where a specific piece of data appears as it flows through your application—without needing prior instrumentation, tracing, or logging.
Peeking Under the Hood with Claude Code
Claude is one of the go-to AI-native code editors for developers. It provides a smooth and simple chat-based CLI that is easy to understand. But this blog aims to help answer what API calls are going on under the hood and what data is being sent.
Part 2: Building a Production-Grade Traffic Capture, Transform and Replay System
Learn how to build a traffic analysis tool for network traffic transformation. Complete guide covering traffic analysis techniques, parsing, and building recommendations.
Mitmproxy vs Proxymock: Replaying Traffic for Realistic API Testing
A pragmatic comparison of mitmproxy and proxymock for traffic replay. Learn which tool to use for investigative debugging vs developer productivity, how they handle mocking, and when each approach shines for realistic API testing.
Unlocking AI Coding Reliability with Traffic Replay
Discover why AI coding agents need traffic replay to bridge the gap between stochastic AI and deterministic software engineering. Learn how proxymock enables AI agents to test against real production traffic for more cheaper and reliable sotware.
Speeding up AI Coding Assistants using Deterministic Feedback
Every engineering leader has seen it: a senior developer is “in the zone”…then Slack pings, CI fails, or an AI suggestion derails everything.
5 Tips for Agent-to-Model Mocking
The near-ubiquity of LLM systems in 2025 has changed the game in many ways. While Large Language Models have been around for some time...
Take Control of Your AI Routing: Mocking Claude, Gemini, and GPT-4
A few short years ago, the idea of using a Large Language Model was relegated to some specific models and implementations for a given industry or use case.
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