Systems | Development | Analytics | API | Testing

OAuth scope naming is critical for securing APIs and simplifying their use. Poorly named scopes can confuse developers and create security risks, while clear, consistent naming ensures precise permissions and easier maintenance. Here’s a quick summary of best practices: Use Clear, Descriptive Names: Follow a resource.action format like users.read to specify both the resource and action. Maintain Consistency: Stick to a unified naming pattern across all APIs to avoid confusion.

An API endpoint is a defined URL or path where an API accepts a request from the client, such as a web app, mobile app, automation script, or AI agent, and sends back a response. It serves as the official communication point between the frontend and backend. In other words, if an API is a restaurant, then the endpoint is the exact table where we take your order.

Disclosure management has traditionally relied on Microsoft Office products like Excel and Word as workhorses for finance teams. However, the modern business landscape demands a more efficient and integrated approach to ensure accurate and timely financial disclosures.

Traditional API Gateways: Excellent for routing, auth, and microservice traffic; poor at AI workloads. Limitations: Can't track tokens, manage streaming responses, enforce content-level security, or use semantic caching. AI Gateways Purpose-built for LLMs with: Architecture Recommendation: Layered approach: Benefits: Lower costs (20--40%), better performance, centralized governance, future-proof AI infrastructure. Market Context.

Multiplayer transforms the chaos of support tickets, eliminating manual work, sloppy hand-offs, and grepping through log files. End-user support has always been messy. Manual steps, tool-switching, and scattered communication turn what should be a simple fix into a marathon of frustration. Tickets feel like scavenger hunts: everyone’s searching for details, logs, screenshots, or that missing repro step. Developers are left waiting on context that never arrives.

If you recall part one of this blog post, we were going to use ChatGPT in parallel with how we would work to cover these aspects of performance testing. We left the first part of this blog post at the point at which we had compared Requirements Gathering and Risk Assessment, we will pick this post up by looking at Script Creation before concluding with Results Analysis. Our performance testing tool of choice will be JMeter.

If you believe many articles online you would believe that automation in testing will soon be defined, managed and executed by Artificial Intelligence (AI). AI is embedded in many organisations technology landscape and to think that this model will change is shortsighted. AI is here to stay undoubtedly in one form or another, but should it be responsible for the automated testing of your applications under test?

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.

What happens when an inquisitive, unpredictable user, without manual or training, just begins clicking and typing in your application? Will everything handle the unpredictability gracefully or crash prematurely? This chaotic scene is not hypothetical in the field of Quality Assurance (QA); it is actually an established testing technique called Monkey Testing. While structured testing is important, it often ignores the unstructured actions of actual users.

Test automation has evolved far beyond QA. Today, it plays a direct role in product speed, developer efficiency, and even customer retention. That means one thing: it’s no longer just a technical investment. It’s a financial decision. If you’re a CFO, you’ve likely seen test automation mentioned in strategy decks or budget line items. But what does the return really look like?