Systems | Development | Analytics | API | Testing

API Staging Is Not Production - But Speedscale Makes It Close

Staging environments are often looked at as the testing ground ahead of the “real” production environment. The idea is simple – build a duplicate of your production environment, run your tests, and ship with confidence. But the reality of using staging in the real world as part of a holistic API testing strategy is rarely that clean. No matter how meticulously you mirror production services, staging always falls a little short.
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Boba Paradox

It's 2PM on a Thursday. Your engineering team is knee-deep in bugs from a recent release. But what's the Slack channel buzzing about? Not flaky tests. Not integration coverage. Not mocking services. It's whether to order brown sugar boba or taro with oat milk. Let's be honest: for many companies, it's easier to justify $8 on boba than $800 on testing tools. And we're not here to judge-we're here to understand why.

From Guesswork to Guarantees: How Traffic Replay Improves Release Confidence

In modern software development, the pressure to move fast is matched only by the need to get it right. Teams working within the software development lifecycle (SDLC) must constantly balance velocity and quality, ensuring releases are stable, secure, and performant. Traditional software development models often relied on manual verification and human intuition to validate releases; however, as systems have grown in complexity, guesswork is no longer sufficient to meet these rising needs.
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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.
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How to Mock OpenAI's APIs with Speedscale's ProxyMock

Developing APIs can often be a complex web of dependencies, external dependencies, and murky network traffic. In order to build better, developers need a certain amount of stability to test a query or feature against, and when this stability is lacking, development can get more complicated and difficult. Enter API mocking. API mocking is an approach to generating a mock service that provides dependable data for a variety of testing purposes. This data can then be used as a test case for actual API calls, allowing for more complete and accurate development.

Eliminating Flaky Tests with Traffic Replay

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Easy Cross-Platform cgo Builds

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

Unlock Cheaper & Faster AI Testing: Mocking Claude and MCP

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.

Getting Started with gRPC: A Developer's Guide

Within the realms of microservices and distributed systems, gRPC has emerged as a cornerstone technology. Its adoption by tech giants like Google, Netflix, and Square underscores its capability to facilitate high-performance, scalable inter-service communication. Built as a modern take on the traditional Remote Procedure Call (RPC) paradigm, gRPC enables services, potentially written in different languages, to communicate efficiently and reliably across networks.

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. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.