TestGuild Automation Podcast

What does it really take to build a test automation tool that millions of testers rely on, without venture capital, paid ads, or a massive team?
In this episode, we explore how SelectorsHub grew into one of the most widely used productivity tools in software testing, reaching over 1.6 million testers worldwide.
You'll discover:
How to build test automation tools that solve real QA pain
Why community-driven development beats chasing funding
How to prioritize features when you have thousands of users
Whether AI testing tools will replace selector-based automation
How to choose between Playwright vs Selenium using automation analysis
What founders and QA leaders can learn from scaling without VC
If you're an automation engineer, QA lead, DevOps professional, or tool builder looking to scale smarter, this episode delivers real-world insight without hype.
Whether you're building frameworks internally or launching your own automation product, you'll walk away with a clearer strategy for solving problems testers actually care about.

AI test automation is evolving fast — but most tools still generate brittle code that breaks with every UI change.
See it for yourself now: https://links.testguild.com/Thunders
In this episode of the TestGuild Podcast, Joe Colantonio sits down with Karim Jouini, founder of Thunders, to explore a radically different approach to AI testing: executing test automation in plain English without generating Selenium or Playwright code.
Instead of "auto-healing selectors," Thunders interprets natural language directly — allowing teams to:
Ship twice as fast
Achieve 10x test coverage with the same resources
Reduce regression cycles from weeks to days
Eliminate massive automation maintenance overhead
Karim shares real-world case studies, including:
A European bank that reduced a 3-year core banking upgrade testing effort to 4 months
A SaaS company that transitioned from a traditional QA team to AI-assisted product-led testing
We also discuss:
Whether AI test agents replace QA roles
How QA managers must shift from individual contributors to AI managers
The risks of adopting AI without a defined success metric
The future of shift-left testing in the AI era
If you're a software tester, automation engineer, QA lead, or DevOps leader trying to understand what's hype versus real ROI in AI testing — this episode breaks it down.
Try it for yourself and see how AI testing fits into your pipeline.
Get personal demo: https://links.testguild.com/Thunders

Is traditional performance testing becoming obsolete?
In this episode, performance engineering expert Akash Thakur shares why AI is fundamentally transforming load testing, scripting, observability, and shift-left strategies.
With 17 years of real-world enterprise experience, Akash explains how AI-augmented tools are already reducing scripting time by 30%, improving analysis speed, and helping teams move from reactive performance testing to predictive intelligence.
You'll learn:
How AI is accelerating performance scripting and analysis
Why shift-left performance testing is finally becoming realistic
The role of structured data in predictive QA models
How to test AI applications (LLMs, GPUs, inference throughput) differently than traditional web apps
What the future role of performance engineers looks like — architect, not script writer
If you're a performance tester, SRE, QA leader, or DevOps engineer wondering how AI will impact your role — this episode gives you practical, actionable insights you can apply immediately.

Most teams find defects after the damage is done — during regression, late-stage testing, or production incidents. That's expensive, stressful, and completely avoidable.
Try Spec2Test AI now: https://testguild.me/spec2testdemo
In this episode, Joe Colantonio sits down with Missy Trumpler, CEO of AgileAILabs, to explore how Spec2TestAI helps teams prevent defects before code ships by applying AI directly to requirements.
You'll learn:
Why traditional test automation still misses critical risk
How predictive, requirements-based AI testing works in practice
What "shift-left" actually looks like beyond the buzzword
How to reduce escaped defects without writing more tests
Why secure, explainable AI matters for QA and enterprise teams
This conversation is especially valuable for software testers, automation engineers, and QA leaders who want earlier visibility into risk, faster feedback, and higher confidence releases.
Don't miss Automation Guild 2026 - Register Now: https://testguild.me/podag26

Performance testing often fails for one simple reason: teams can't see where the slowdown actually happens.
In this episode, we explore Locust load testing and why Python-based performance testing is becoming the go-to choice for modern DevOps, QA, and SRE teams. You'll learn how Locust enables highly realistic user behavior, massive concurrency, and distributed load testing — without the overhead of traditional enterprise tools.
We also dive into:
Why Python works so well for AI-assisted load testing
How Locust fits naturally into CI/CD and GitHub Actions
The real difference between load testing vs performance testing
How observability and end-to-end tracing eliminate guesswork
Common performance testing mistakes even experienced teams make
Whether you're a software tester, automation engineer, or QA leader looking to shift-left performance testing, this conversation will help you design smarter tests and catch scalability issues before your users do.