Operations | Monitoring | ITSM | DevOps | Cloud

AI Integration #speedscale #ai #integration #mcp #march

Ken Ahrens from Speescale dives into the best AI API integration model of March 2025 — Anthropic's MCP model. This innovative integration enables seamless communication with browsers and various tools, including the popular Cursor. Discover how the MCP model is revolutionizing AI-powered workflows and boosting productivity.
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What Are Cloud Development Environments?

Especially, if you have a globally distributed team, CDEs give you a smoother developer experience just by its online nature. Instead of wrestling with conflicting dependencies, trudging with inconsistent local setups, or waiting for your code to compile, you have a powerful, instantly accessible development environment in the cloud. CDEs remove typical limitations like hardware and scalability. You can quickly get started with minimal setup and configuration, but confidently move forward due to the flexibility and customization features CDEs provide.

Modernize Test Data Management with Traffic Replay

In software testing or platform engineering, having realistic data is crucial. For years, teams have relied on Test Data Management (TDM) to copy entire production databases, scrub any sensitive information, and then spin up test environments from these sanitized data sets. While TDM gets the job done, it can be costly, time-consuming, and can quickly become outdated.

How to Mock AI APIs Using proxymock

APIs often represent the cutting edge of the technology space. This is especially true with Artificial Intelligence – as AI has evolved from speculative technology to mass adoption, it has shown up significantly in APIs as a modality and mechanism. However, as with all new technologies, using AI APIs comes with significant challenges.
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What Is Shadow Traffic? All You Need to Know

Production traffic can often be unpredictable, and distinguishing genuine user interactions from mere noise becomes a pivotal step in comprehensively grasping the types of requests and workflows occurring within your deployment. One important concept to explore in this context is shadow traffic, which plays a significant role in analytics and cybersecurity but is often misunderstood or rarely discussed.

Using Python MockServer for API Testing

Using a mock server is a popular method of working around these limitations and realities, allowing you to test web server assets against specific requests, ensuring that your response data matches the expected outcome. Today, we’re going to look at a powerful solution for Python clients in the form of MockServer. We’ll walk through the tool’s basics and learn how to use it for your own testing.

Intro to proxymock, a free traffic-based service mocking tool within VS Code.

Speedscale's proxymock is a free VS Code plugin that passively listens to transactions, so developers can replay past responses or inbound transactions like a time machine. Past transactions can serve as non-rate-limited service mocks, editable databases, or even regression/load/chaos tests. Building service mocks to serve as service virtualization/mocks can be time consuming and manual. Maintaining complex, shared environments for engineering incurs expensive cloud costs and aren't often accurate.

Ultimate Guide to Creating a JSON Mock API for Testing

Using a JSON mock allows you to avoid using fake data or simulating interactions, resulting in better final output and stronger data flows. Today, we’re going to dive into the process of creating a mock API using JSON data and tools like JSON-server. This guide will help you understand the basics of this process and get started quickly with your own mock API, allowing you to speed up development and testing without relying on a live backend.