What Is This?
AI in Testing Daily is a live, public learning experiment.
It explores a simple question:
Can AI‑generated podcasts and newsletters deliver practical, useful value for people who practice software testing — while being honest about where AI works, where it fails, and where human judgment still matters?
This is not a product launch.
It is an experiment, run in public.
The project combines:
- Large Language Models (LLMs)
- Prompt engineering
- AI‑assisted content generation
- AI voice cloning (an AI twin of my voice)
The output is a short daily podcast and companion newsletter, generated, reviewed, and iterated as part of the experiment.
Content alternates between two tracks:
- ***AI in Testing Coach (Mon · Thu)*Hands-on, learning-first guidance on:
- Testing AI / LLM-powered systems in real-world contexts
- Applying AI thoughtfully in testing and automation workflows
- Evaluating models when it actually matters (and when it doesn’t)
- ***Testing Tech Horizon (Tue · Fri)*Practical, signal-over-noise updates at the intersection of AI, software testing, and QA-relevant technology. The focus is on what’s changing, what’s emerging, and what’s worth paying attention to — not news for news’ sake.
Everything you hear or read is part of the experiment — including the mistakes.
Why This Exists
1. To test usefulness first
I want information that is useful to me as a tester.
If it helps me make better testing decisions, there’s a good chance it helps others too.
If it doesn’t, the experiment changes — or stops.
Usefulness comes before polish.
2. To show real LLM usage — not demos
This is not a showcase or a highlight reel.
It is a transparent, working example of how LLMs behave when used every day:
- Prompting for consistency
- Structuring recurring outputs
- Managing drift
- Handling mistakes and gaps
You’ll see:
- Where LLMs are genuinely helpful
- Where they break down
- Where human judgment is still essential
That contrast is the point.
3. To experiment with AI voice cloning openly
The podcast is narrated by an AI twin of my voice.
Not to replace humans — but to explore:
- Authenticity and trust
- Accessibility
- Failure modes
- The limits of synthetic voices
Both capability and shortcoming are visible, side by side.
4. To invite criticism, bugs, and feedback
This experiment expects problems:
- Content misses
- Hallucinations
- Wrong emphasis
- Awkward phrasing
- Voice artifacts
- Workflow breakdowns
If you notice them, that’s not failure — that’s a signal. You may also have learned something about how AI systems behave when tested.
Feedback is part of the system.
Who This Is For
This is for software testing practitioners and leaders.
People who are actively working with the realities of modern testing — especially as AI becomes part of the system under test, as well as the technology assisting testing work.
That includes:
- Exploratory testers practicing critical thinking and sense-making
- Automation engineers and SDETs working close to the code
- Test leads, QA managers, and engineering leaders shaping testing strategy
- Developers who actively practice and care about software testing
- Anyone learning AI in testing by doing, not by slides
If you want polished marketing content, this probably isn’t for you.
If you want to see AI systems succeed, fail, drift, and surprise us — and learn from that — you’re in the right place.
What This Is Not
This is not:
- A polished media brand
- A marketing funnel
- A replacement for human testers
- A claim that “AI solves testing”
This is:
- A QA lab
- A learning surface
- A shared experiment
- An honest look at applied AI
Build in public. Learn in public.
Call to Feedback (Bottom CTA)
Help break this experiment.
If something feels wrong, inaccurate, confusing, or misleading — say so.
That feedback is more valuable than praise.
Testing is how we learn.
AI is no exception.