Systems | Development | Analytics | API | Testing

Exploratory Testing is one of the most valuable practices in software testing, especially when we want to discover issues that impact user experience. But at the same time, it is also one of the most demanding approaches in terms of time and focus. In a world where product development life cycles are becoming shorter and where QA teams need to ensure faster releases without compromising quality, a question arises: How can we gain time without sacrificing the depth of exploratory tests and their findings?

In the programming language, Python, binary data and text are often used when working with files, APIs, data encoding and decoding, and networking. In this article, we are going to answer some of the common questions that arise in this process. Some of which include what bytestrings are, how to convert strings to bytes and vice versa, their differences, and many more. This guide will cover all that is required for you to master bytestrings in Python.

In my previous post, we discussed how we can implement a basic AI Agent with Kong AI Gateway. In part two of this series, we're going to review LangGraph fundamentals, rewrite the AI Agent and explore how Kong AI Gateway can be used to protect an LLM infrastructure as well as external functions.

In today’s fast-paced world of software development, the pressure to deliver high-quality releases quickly is stronger than ever. Teams are pushing code changes to production multiple times a day, and expectations around stability, security, and performance haven’t gone down—in fact, they’ve gone up. Manual testing simply can’t keep up with the speed and complexity of modern deployment cycles.

In a significant leap forward for the software testing industry, Tricentis has introduced Agentic Test Automation, an advanced, AI-powered tool set to transform how QA teams create and execute automated test cases. This industry-leading agentic AI solution, directly embedded into Tricentis Tosca, allows teams to generate comprehensive test cases autonomously using natural language prompts, unlocking a new level of efficiency and productivity in software testing.

State governments across the U.S. are stepping up to deliver smarter services and take on bigger roles as federal priorities shift. A recent White House executive order puts state and local leaders at the forefront of driving preparedness, infrastructure upgrades, and resilience. From cyberattacks to natural disasters, local governments are leading the charge on risk-informed planning and making every dollar work harder. The result?

If you're working in software testing, you already know that measuring results is the daily concern. But the truth is that many teams end up focusing only on numbers when bugs get detected or when tests pass. But what if this is not the most strategic approach? The most successful teams are the ones that can connect QA metrics to business goals, showing that their software is not only stable, but also delivering real value to their users and its enterprise. In this article, you'll explore: Bottom line.

Businesses are constantly driven by the need to meet the growing demands of customers while also navigating fierce competition and rapid market changes. Whether you are releasing new applications or posting regular updates, launching your software to the market sooner can make all the difference. But how do businesses stay abreast of the emerging need without sacrificing stability or quality? Presenting DevOps services - a blend of automation, process, and culture to accelerate software delivery.

Hey, so you have been wondering why your favourite app sometimes feels like it sometimes stuck in slow motion, right? Well, keep your coffee aside because we are about to learn something that affects literally every digital experience you have, i.e. latency testing. In this blog, we are going to explore about latency testing and how it makes a bigger impact and why you should care about it. Latency is just one piece of the broader performance testing puzzle.

Agentic AI: autonomous, goal-seeking systems powered by large language models (LLMs) is rapidly transitioning from novelty to necessity. While developers are building impressive prototypes, many large enterprises remain stuck in pilot purgatory: mesmerized by potential, but unable to translate demos into durable systems that generate value at scale. Two leading visions frame the conversation: Each view highlights an essential dimension of the challenge but neither is complete on its own.