From creating everything manually
More testing material can now be generated or drafted with AI assistance.
Come Learn with Me
A path into modern testing, wherever you are starting.
Software testing is changing quickly, but you still need to investigate behavior, evaluate evidence, question generated output, prescribe to and train AI agents, and communicate risk. Beyond that, the skill set to lead human-agent teams is the weapon that helps you get ahead.
The work is evolving, and so is the way we learn it.
Whether you are entering the profession or adapting after years of experience, you do not need to have everything figured out before you begin.
More than 40 years into this profession, I am still relearning parts of it. Not because everything I knew was wrong, but because the work keeps changing.
AI has accelerated that change. It can generate test ideas, code, automation, analysis, and documentation. But someone still has to decide whether the output fits the context, whether the evidence is strong enough, and what to communicate next.
I am not inviting you to follow a finished formula. I am inviting you to keep learning with me as we work out what modern testing requires.
More than 40 years in software testing, co-founder of LogiGear, software-testing author, practitioner, and educator.
◆ Hung Nguyen,Founder and practitioner, Shade of Hue
The profession did not disappear. The work moved.
AI is producing more of the raw material that testers once created manually. Test ideas, draft cases, automation code, summaries, and possible explanations can now appear in seconds.
That changes where a tester's attention is most valuable.
To directing the work
Testers increasingly define the task, provide context, and choose what evidence is needed.
From accepting output
Generated material must be reviewed against requirements, product behavior, risk, and experience.
To making responsible decisions
Human judgment remains essential when deciding what to trust, change, communicate, or release.
Modern testing requires both sides of the AI shift: applying AI to testing work and testing products whose behavior includes AI.
Choose the path that sounds most like you.
These paths are starting-point guidance, not separate products and not fixed roadmaps. You can move between them as your needs change.
New entrants and career switchers
Build testing foundations, understand how software teams work, and develop a structured starting point for practical testing.
Suggested starting course: Pragmatic Software Test Engineer
Explore foundationsExperienced practitioners
Extend your existing experience through APIs, automation, AI-assisted testing, testing AI systems, and modern human-agent workflows.
Suggested areas: Testing with AI and Testing AI Systems · API Testing · Test Automation Engineering · Focused and advanced courses
Explore the Practitioner PathBuild the foundation and the modern context together.
For new entrants and career switchers
You do not need to spend years learning an older version of testing before understanding how AI affects the work. You can build testing fundamentals and modern AI fluency side by side.
What you already bring
- Curiosity
- Attention to detail
- Willingness to investigate
- Experience from another profession or industry
- The ability to ask when something does not make sense
- Interest in how products behave for real users
A technical background can help, but it is not the only useful starting point. Domain knowledge, communication, critical thinking, and structured curiosity also matter.
What you will develop
- Testing foundations
- Risk-based thinking
- Test design and investigation
- Evidence collection
- API and automation literacy
- AI-assisted testing skills
- Understanding of AI-enabled system behavior
- Technical communication
A practical starting point
- 01
Begin with testing foundations
Pragmatic Software Test Engineer
Build a structured understanding of testing, analysis, risk, test design, investigation, evidence, and communication.
- 02
Add AI fluency early
Testing AI Systems and Testing with AI
Learn how AI can support testing work and how AI-enabled products introduce new behaviors, risks, and evaluation questions.
- 03
Expand through APIs and automation
API Testing with Postman, Hoppscotch, and InsomniaTest Automation Engineering Series
Build enough API and automation understanding to investigate modern systems, collaborate effectively, and review AI-assisted technical work with judgment.
Explore Learn Lab
This is guidance, not a mandatory sequence. Your previous experience and goals may change where you begin.
Your experience is not a liability.
For experienced practitioners
Experience gives you something generated output does not have on its own: context, memory, judgment, and an understanding of how software fails in the real world.
What your experience already gives you
Testing judgment
You recognize when an output looks complete but misses the real risk.
Product and domain context
You understand the environment, users, constraints, and consequences behind the requirement.
Investigation experience
You know how to follow an unexpected behavior beyond the first explanation.
Communication and influence
You know that testing evidence only creates value when people understand what it means and what should happen next.
Start from the actual gap
Use testing foundations as a refresher when needed. Prioritize the AI concepts that help you understand generated output and AI-enabled behavior. Strengthen your API and automation literacy so that you can direct technical work and review what AI produces.
You do not need to become an SDET to benefit from understanding APIs, automation architecture, generated code, and the evidence automated workflows produce.
You do not start at square one. You start wherever the current gap is.
Move from understanding to practice to responsible use.
Learn Lab
Learn testing knowledge, methods, APIs, automation, AI-assisted testing, testing AI systems, and communication.
Play Lab
Practice through guided activities, course-linked resources, communication scenarios, sample applications, and low-risk environments.
Apply Lab
Bring selected, reviewed methods and resources into real testing work while retaining human responsibility.
Learn the knowledge and skills. Play with the methods and tools. Apply AI to testing, test AI systems, and put both into practice.
Different starting points. Shared modern capabilities.
Whichever path you are on, I would focus on the same capabilities.
Testing foundations
Understand behavior, risk, evidence, coverage, and decision-making.
AI fluency
Use AI more critically and understand the systems and limitations behind generated output.
API and automation literacy
Understand how modern systems connect and how automated testing work is designed, executed, and reviewed.
Human judgment
Evaluate whether an output fits the product, requirement, user, and risk.
Communication
Explain progress, evidence, blockers, impact, and next action with confidence.
I would not ask you to learn every tool. I would ask you to understand what the tool produces, when it helps, where it can fail, and how to judge the result.
Not a pile of tool tutorials. Not a certification finish line. Shade of Hue connects testing knowledge, deliberate practice, and responsible application.
Learning with a testing team?
The same Learn, Play, Apply model can support teams that need a shared testing foundation, practical AI fluency, stronger communication, and a structured way to develop human-agent testing capability.
For TeamsCome learn with me.
I did not build this around the idea that I have reached the end of the profession and can now explain it from a finished position.
I built it because testing has kept changing throughout my career, and AI is another change we have to examine carefully rather than either fear or accept without question.
If you are new, you do not have to wait until you feel fully technical before you begin.
If you are experienced, you do not have to erase what you already know.
Start where you are. Keep your judgment. Add what the work now requires. I will be learning alongside you.
Hung NguyenFounder and practitioner, Shade of Hue
One subscription for Learn, Play, and Apply.
Shade of Hue Labs Annual connects structured learning, deliberate practice, and selected application resources in one platform.
Courses and resources continue to evolve as AI and testing practice change.
Choose a starting point that fits your experience, then continue learning as the profession moves.