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Get Ahead of the Game: LLM Compliance and Mocking
As Large Language Models (LLMs) become increasingly integrated into enterprise applications, organizations face new challenges around compliance.
4 Tips for Developing Model Context Protocol Server
The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling.
AI Code: How AI Is Changing How We Write and Test It
As a software engineer, I’ve always leaned on a solid foundation of code reviews, unit tests, and CI pipelines to ensure quality.
Catch Prompt Misfires Before They Burn Trust in LLM
Large Language Models (LLMs) are incredibly powerful, but they are also incredibly fragile.
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.
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...
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.
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.
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.
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.
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.
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.
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.
Moving Our Observability Data Collector from Sidecars to eBPF
Speedscale transitions from Kubernetes sidecar-based observability collection to eBPF for lower latency, reduced resource consumption.
The PII Testing Dilemma
Software is hard to test when production data contains PII and AI systems are causing an explosion in bugs.
Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes
Use traffic replay via MCP to create a tight feedback loop for AI coding agents, preventing hallucinated success by validating against immutable.
How AI Coding Is Breaking Synthetic Data Generation
AI codingagents are accelerating the breakdown of synthetic data generation approaches.
DLP, Traffic Replay, and the Missing Link to Software Quality
DLP applied to production traffic enables safe observability and realistic traffic replay, closing the gap between testing and production for faster.
Speedscale Named in Gartner® Market Guide for API Testing
Speedscale is a Representative Vendor in the Gartner Market Guide for API and MCP Testing Tools. See how traffic replay modernizes testing.
Oracle JDK to OpenJDK: A Guide to Reliable Migration Testing
Record production traffic on Oracle JDK, replay it on OpenJDK, and catch every regression before users do. A step-by-step Speedscale guide.