Faster Horses
AI is doing our old job faster, like a horse with more horsepower. Which skill the next abstraction layer devalues, and which one it rewards.
Testing AI systems and applying AI to software quality, test design, analysis, and automation.
AI is doing our old job faster, like a horse with more horsepower. Which skill the next abstraction layer devalues, and which one it rewards.
Taking over AI output builds no relationship with the result. Why ownership grows from working through things, not from generating them.
Kamchatka 2008 with books instead of a ChatGPT itinerary: what the double compression of time and work does to us, and why deliberate brakes help.
AI generates movies and apps while teams drown in legacy code. Why we should use AI for the problems that actually hurt, not for demos.
AI noise from every direction: why total refusal rarely works, and how small steps and the Gartner Hype Cycle help you stay relaxed but mindful.
The future of testing lies not in better tools but in humanity. Why testers are the interpreters between AI, code, and the people behind it.
Explore the future of software testing with AI in software testing and essential soft skills for achieving better quality and user satisfaction.
Explore how AI in software development is reshaping the landscape, making human-centric programming languages potentially obsolete.
Explore Stoicism in the AI era: reduce decision stress and navigate life with stoic principles for a focused mindset. Empower your decision making.
In 2025, agile coaching techniques empower software developers to thrive alongside AI, enhancing creativity and talent in development processes.
Explore how effective test data management can drive software quality. Discover strategies for overcoming testing challenges in 2025.
Discover how AI in software testing can enhance your quality and efficiency, transforming your testing approaches in 2025.
AI is transforming Software Testing and AI strategies. Discover how innovative tools enhance testers' efficiency in 2025.
In 2025, discover the future of software testing with agile quality management integrated with AI and new methodologies.
Explore the future of software testing with AI in software testing techniques revolutionizing quality assurance strategies.
Explore Software Testing with AI and discover the synergies, opportunities, and challenges of this innovative technology in software development.
AI Personas as Documentation Testers: What a Simulated Junior Developer with 1.8 Million Tokens Reveals About Where Documentation Really Falls Short.
Students who used ChatGPT lost 47% of their cognitive capacity. What that means for how we work, hire, and build software teams in the AI age.
AI makes team communication more objective, but that isn’t progress. What is lost in terms of trust, empathy, and collaborative thinking—and how teams are countering this trend.
Cutting QA teams doesn't eliminate bugs, it just makes users find them first. Here's why communication and critical thinking still matter most.
AI in a regulated medical lab sounds like a compliance nightmare. Here is how a strategy-first approach made it work without breaking the rules.
Generative AI cannot be used ethically as long as training data is used without consent and billionaires control the models.
Testing a chatbot breaks every rule traditional testing relies on: same input, wildly different outputs, and bugs that live outside the code.
AI won't replace testers, but it will shift what they do. Where it saves real effort, where it still needs human control, and why starting now matters.
If you have bad principles and use AI, you will get worse faster. Why agentic engineering needs more than code generation.
AI systems can be tricked into revealing protected data through clever prompts. Where the points of attack lie and what OWASP recommends.
Trusting an AI agent works the same way as trusting a colleague: you need clear communication, checks, and a system that catches what the model gets wrong.
Why can't LLMs do cause-and-effect? And what does Quality Function Deployment have to do with it? The answer changes how you prioritize testing.
AI-generated test cases in the medical technology environment: how a RAG system remains regulatory clean without tool validation.
AI-generated code sounds tempting, but who pays the bill? Why software engineering needs more quality control, not less.
AI writes the code, AI writes the tests — but who checks if any of it is actually right? Critical thinking is the skill testers cannot afford to outsource.
Specialist departments hardly ever test because there is a lack of test knowledge. How an AI assistant called Sherlock is changing this and delivering test cases according to ISO standards.
176 episodes, 122,000 downloads, flaky tests, AI fears and a microphone faux pas: what the year really left behind.
AI won't replace testers, but it will reward those who use it well. Here's where the real productivity gains are in 2026.
AI doesn't make bad processes better, it catches up with them faster. What testers really need in 2026: Gut feeling, basic knowledge and community.
From two weeks of manual testing to three hours: how an AI-based solution automates legacy apps without element IDs.
AI systems have bias, but the real problem is that people adopt it without realizing it. What this means for the use of AI in companies.
Vague requirements are the most expensive problem in testing. How AI really helps when determining, formulating and checking requirements.
AI takes over testing? What it can't do: genuine curiosity, intuition and thinking outside the box. Why human tester thinking remains.
A third of respondents already use AI for coding, but many still lack automation when it comes to regression testing. What the Software Testing Survey 2024 really shows.
Static analysis throws up thousands of findings - AI fixes two thirds of them reliably. What this means for old code bases and where the method clearly reaches its limits.
Most programmers using AI tools will create legacy code faster, not better. Here's what actually separates quality developers in 2034.
Trusting AI agents with your business works like hiring a new colleague: you need selection, probation, and continuous performance checks built in.
Who modernizes legacy code when there are no experts? RAG-based AI draws knowledge directly from legacy code - and makes subject matter experts replaceable.
Testing AI without structure is like testing without scale. How a three-dimensional matrix brings order to the chaos of AI testing dimensions.
Requirements often have more gaps than expected. DEFOSPAM is an acronym that systematically uncovers definitions, scenarios and ambiguities - and AI could help with just that.
Making AI systems testable: How capabilities, quality criteria and structured test descriptions turn abstract standards into concrete testing approaches.
Many people know test design methods, but hardly anyone uses them. Why this is the case and how AI closes this gap without making you stop thinking.
AI is becoming the standard tool in testing, accessibility is becoming mandatory and test automation continues to gain momentum. What really matters in 2025.
AI dominates programming and testing, but lags behind in requirements engineering. Where Scrum really stands today and what that means for software quality.
AI-generated test code that compiles but tests the wrong thing: Why writing unit testers yourself and having business code generated instead is often the smarter approach.
Accessibility tests today end up in a jumble of Word, Excel and browser tabs. How an integrated test environment can change this and how AI can help.
Testing AI sounds complex, but it often boils down to a single question: does the system behave deterministically or not? What this means for your test strategy.
AI testing, test data, accessibility: which trends testers should really have on their radar and why the right tool often doesn't solve the actual problem.
AI doesn't make developers more productive, it makes them faster at creating legacy code. What this means for quality and jobs until 2034.
There is often a gap between theory and practice in software testing. How AI-supported certification aims to change this - and why 40 national boards are getting involved.
LLMs testing like a pro: Acceptance Test Driven Development meets fine-tuning - creating a measurable quality process for AI systems.
Using ChatGPT for test cases, test data and exploratory ideas: What really works, where caution is advised and why prompting matters.
Testing AI means rethinking: no clear test oracle, statistical quality instead of pass/fail. What this means in concrete terms and which methods really help.
Fair AI sounds good, but what does it mean in measurable terms? Why fairness measures can contradict each other and how a structured assurance case can help.
Testing audio AI without being able to look into the code: Why golden test sets, training data tracking and ChatGPT play a role in this.
AI can generate test cases, but can you rely on their correctness? That remains risky. Which prompt patterns really help and where the limits lie.
From carpenter to agile engineering coach: How a career change and a book about system testing sparked an entire career in testing.
200 failed tests, but only seven real causes: How machine learning radically simplifies testing operations without rationalizing away roles.