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Showing posts with label AI Automation. Show all posts
Showing posts with label AI Automation. Show all posts

Tuesday, 13 October 2026

Steps for API Testing in Playwright: A Complete Step-by-Step Guide

 Playwright guide

Steps for API Testing in Playwright

A complete, practical walkthrough for building fast, reliable API tests—from your first request to authenticated CRUD flows and CI-ready reporting.

Meta description
Learn how to perform API testing in Playwright with setup, HTTP methods, assertions, authentication, fixtures, and best practices for reliable automated tests.

Introduction

Modern applications are built on APIs, and ensuring those APIs work correctly is just as important as testing the UI. While Playwright is widely known as a powerful end-to-end browser automation tool, it also ships with a built-in APIRequestContext for API testing.

You can validate backend services, mock data for UI tests, or build a full API test suite—all without installing a separate HTTP client library. This guide walks through the complete process, from installation to robust, production-ready test suites.

What is API testing?

API testing verifies that an application programming interface meets expectations for functionality, reliability, performance, and security. Instead of interacting with a UI, API tests send requests directly to REST, GraphQL, or other endpoints and validate status codes, headers, response bodies, and timing.

Why use Playwright for API testing?

Playwright includes a dedicated request context that lets you:

  • Send GET, POST, PUT, PATCH, and DELETE requests without a browser.
  • Reuse authentication state between API calls and UI tests.
  • Combine API setup and teardown with UI tests in one file.
  • Run requests in parallel with Playwright's test runner.
  • Use built-in retries, timeouts, tracing, and reporting.

Step-by-step: API testing in Playwright

Prerequisites

  • Node.js v16 or later
  • Basic JavaScript or TypeScript knowledge
  • A code editor (VS Code recommended)
  • An API endpoint to test

Step 1: Install Playwright

Create a new project, or install the test runner in an existing project:

npm init playwright@latest
# or
npm install -D @playwright/test

Step 2: Understand the request fixture

The built-in fixture provides an APIRequestContext. No browser or page is needed.

import { test, expect } from '@playwright/test';

test('get list of users', async ({ request }) => {
  const response = await request.get(
    'https://jsonplaceholder.typicode.com/users'
  );
  expect(response.status()).toBe(200);
});

Step 3: Write your first API test

Create tests/api.spec.ts and assert both the response and its data:

const BASE_URL = 'https://jsonplaceholder.typicode.com';

test('GET /users returns a list', async ({ request }) => {
  const response = await request.get(`${BASE_URL}/users`);
  expect(response.ok()).toBeTruthy();

  const users = await response.json();
  expect(Array.isArray(users)).toBeTruthy();
  expect(users.length).toBeGreaterThan(0);
});

Run the test with npx playwright test.

Step 4: Handle HTTP methods

The request object exposes all common HTTP verbs:

// GET
await request.get(`${BASE_URL}/posts/1`);

// POST
await request.post(`${BASE_URL}/posts`, {
  data: { title: 'Playwright API Testing', body: 'Learning APIs', userId: 1 }
});

// PUT
await request.put(`${BASE_URL}/posts/1`, {
  data: { id: 1, title: 'Updated Title', body: 'Updated body', userId: 1 }
});

// DELETE
await request.delete(`${BASE_URL}/posts/1`);

Step 5: Validate responses with assertions

Validate status codes, headers, and body content together for expressive, useful failures.

const response = await request.post(`${BASE_URL}/posts`, {
  data: { title: 'foo', body: 'bar', userId: 1 },
});

expect(response.status()).toBe(201);
expect(response.headers()['content-type']).toContain('application/json');

const body = await response.json();
expect(body.title).toBe('foo');
expect(body).toHaveProperty('id');

Step 6: Handle authentication

Set default headers on a context so every request includes the required token:

const apiContext = await request.newContext({
  baseURL: 'https://api.example.com',
  extraHTTPHeaders: {
    Authorization: `Bearer ${process.env.API_TOKEN}`,
  },
});

const response = await apiContext.get('/protected-resource');
expect(response.status()).toBe(200);
await apiContext.dispose();
Security note: Keep tokens and credentials in environment variables or your CI secret store—not in test files.

Step 7: Use fixtures and custom contexts

For larger suites, create an authenticated context once and dispose of it after use:

type MyFixtures = { apiContext: APIRequestContext };

export const test = base.extend({
  apiContext: async ({}, use) => {
    const context = await request.newContext({
      baseURL: 'https://api.example.com',
      extraHTTPHeaders: {
        Authorization: `Bearer ${process.env.API_TOKEN}`,
      },
    });
    await use(context);
    await context.dispose();
  },
});

Step 8: Chain requests

Model real workflows by creating, updating, verifying, and deleting a resource:

const createRes = await request.post(`${BASE_URL}/posts`, {
  data: { title: 'New Post', body: 'Content', userId: 1 },
});
const { id } = await createRes.json();

const updateRes = await request.put(`${BASE_URL}/posts/${id}`, {
  data: { title: 'Updated Post' },
});
expect(updateRes.status()).toBe(200);

const getRes = await request.get(`${BASE_URL}/posts/${id}`);
expect(getRes.status()).toBe(200);

const deleteRes = await request.delete(`${BASE_URL}/posts/${id}`);
expect(deleteRes.status()).toBe(200);

Step 9: Test errors and negative cases

Do not test only the happy path. Check invalid input, missing authentication, and unknown resources:

test('returns 404 for a missing resource', async ({ request }) => {
  const response = await request.get(`${BASE_URL}/posts/999999`);
  expect(response.status()).toBe(404);
});

test('returns 401 without authentication', async ({ request }) => {
  const response = await request.get('https://api.example.com/protected');
  expect(response.status()).toBe(401);
});

Step 10: Generate and review reports

Playwright's HTML reporter works for API tests too:

npx playwright test
npx playwright show-report

Configure multiple reporters in playwright.config.ts:

export default {
  reporter: [['html'], ['junit', { outputFile: 'results.xml' }]],
};

Best practices

  • Isolate API and UI tests with separate project configurations when useful.
  • Use environment variables for URLs, tokens, and credentials.
  • Create reusable fixtures for authentication and setup/teardown.
  • Validate schemas with libraries such as zod or ajv.
  • Clean up records created during tests.
  • Run independent API tests in parallel in CI.
  • Use API calls to set up data quickly before UI assertions.

Common pitfalls to avoid

  • Forgetting dispose() on manually created request contexts.
  • Hardcoding tokens or secrets in test files.
  • Parsing a body without first checking response.ok().
  • Relying only on status codes instead of validating content.
  • Running tests against production instead of a dedicated test environment.

Conclusion

Playwright's built-in API capabilities provide an all-in-one way to validate both the UI and backend. Start with one GET request, then expand into CRUD flows, authentication, schema checks, and negative scenarios. You will quickly have a maintainable regression suite that catches issues before production.

Ready to get started?
Set up a Playwright project, choose one endpoint, and write your first automated test today.

FAQs

Do I need a browser to run API tests?No. APIRequestContext sends HTTP requests directly, making tests fast and lightweight.
Can I combine API and UI testing?Yes. Use API calls for quick data setup or authentication, then switch to browser-based testing in the same test file.
Does Playwright support GraphQL?Yes. Test GraphQL endpoints with request.post() and place the query or mutation in the JSON payload.
How do I handle flaky API tests?Use retries, isolate test data, and avoid depending on unstable external services.
Can Playwright validate JSON schemas?Integrate ajv or zod alongside Playwright assertions for deeper schema validation.

Tuesday, 29 September 2026

How AI Can Help Playwright Automation | A learner's guide

How AI Can Help Playwright Automation

A step-by-step guide for learners

How AI Can Help Playwright Automation

Use AI as a thoughtful partner to write, expand, maintain and debug robust browser tests, with Playwright at the centre.

Test automation has come a long way, and Playwright has quickly become one of the most popular frameworks for automating modern web applications. It is fast, reliable, and supports multiple browsers and languages out of the box. Yet learners and teams still face familiar challenges: fragile selectors, scripts to write from scratch, UI changes to accommodate, and flaky failures to debug.

This is where Artificial Intelligence (AI) can help. AI can reshape how QA engineers and developers write, maintain and scale Playwright suites, without replacing the judgement that makes a test valuable.

Why combine AI with Playwright automation?

Traditional test automation asks a tester to perform several repetitive yet important activities.

AI can assist with nearly every step, reducing repetitive effort and helping learners ramp up faster, while human review keeps each test grounded in product intent.

6 ways AI enhances Playwright automation

01

AI-assisted test script generation

AI coding assistants can turn a plain-English scenario, such as “log in with valid credentials and verify the dashboard loads”, into a working draft of Playwright code to refine.

02

Self-healing locators

When the DOM changes, AI-powered approaches can analyse page structure and suggest alternative locators. This may reduce maintenance overhead, but healed locators should always be reviewed.

03

Smart test case generation

Given a user story, application flow or relevant logs, AI can suggest edge cases and negative scenarios that are easy to overlook.

04

Visual regression testing with AI

AI-driven visual testing can distinguish meaningful visual differences from insignificant rendering variation, helping teams focus on real regressions.

05

Intelligent maintenance and debugging

AI can analyse failed runs alongside errors and recent changes, then suggest whether the likely cause is a product bug, flaky behaviour or an outdated selector.

06

Codegen + AI refinement

Playwright’s built-in Codegen records browser actions and generates code. An AI assistant can help clean up that starting point, add assertions, parameterise data and explain each step.

The 8-step guide: getting started

1

Set up your Playwright environment

  1. Install Node.js.
  2. Initialise a project with the following command.
  3. Run the sample tests to verify the installation.
npm init playwright@latest
2

Record a baseline test with Codegen

Run Codegen, perform a basic flow and save the generated code as your starting point.

npx playwright codegen https://your-app-url.com
3

Refine the script with an AI assistant

Provide the Codegen output and ask AI to add meaningful assertions, replace brittle selectors with getByRole or getByTestId, parameterise hardcoded data, and explain the code. Treat the result as a first draft.

4

Expand coverage with AI-suggested scenarios

Describe the user flow and ask for negative and edge cases. Convert only the relevant suggestions into new tests.

5

Implement robust locator strategies

Prefer Playwright’s resilient getByRole, getByLabel and getByText locators over brittle CSS or XPath selectors. If a self-healing framework is used, audit its changes regularly.

6

Add AI-powered visual testing

Optionally integrate a visual testing tool, capture baselines for key pages or components, and review flagged differences rather than accepting every change automatically.

7

Run tests and triage failures

Run the suite and share sanitised logs or stack traces with an AI assistant to help classify failures. Apply a proposed fix and re-run to confirm it.

npx playwright test
8

Continuously iterate

Repeat the Codegen and refinement cycle as features evolve. Ask AI to identify redundant tests, missing coverage or opportunities to simplify, but keep the final decisions yours.

Best practices

  • Review generated code: use generated code as a first draft, never as an unquestioned final product.
  • Validate assertions and logic: AI can misunderstand intent.
  • Audit self-healing: confirm healed locators still target the intended elements.
  • Keep sensitive data out of prompts: never share production credentials, personal data or confidential business logic with third-party tools.
  • Keep human judgement in the loop: test strategy and domain knowledge remain essential.

Limitations to keep in mind

  • Suggestions are only as good as the context provided.
  • Self-healing and visual AI can produce false positives or negatives.
  • Compatibility varies across JavaScript, TypeScript, Python, Java and .NET setups.
  • AI cannot replace foundational testing knowledge. Understanding what to test and why still matters.

Conclusion

AI can make Playwright automation more collaborative and efficient: generating scripts, broadening coverage, maintaining locators and accelerating failure triage. Start with Codegen, layer in AI refinement and iterate continuously. With careful validation, learners can build robust, maintainable suites faster while developing the testing skills that matter most.

Ready to begin? Set up a Playwright project, record your first Codegen script and bring an AI assistant along for the ride.
Learner’s guide to AI-assisted Playwright automation

Thursday, 10 September 2026

How AI Can Help UI Automation

 

How AI Can Help UI Automation: A Beginner’s Guide for Manual Testers

Manual testers have a strong foundation for automation: they understand user journeys, business rules, risks, edge cases, and expected behavior. The challenge is often technical—learning code, selecting tools, writing locators, and maintaining scripts.
AI can make that transition easier. It does not replace testing judgment, but it can work as a helpful assistant while you learn UI automation.

What Is UI Automation?

UI automation uses a tool to perform actions that a user would perform in an application, such as:
  • Opening a web page
  • Entering text into a form
  • Clicking a button
  • Checking an error message
  • Verifying that a dashboard is displayed
For example, instead of manually testing login on every release, an automated test can enter credentials, select Login, and verify that the user reaches the correct page.
Common UI automation tools include Playwright, Selenium, Cypress, and Appium. While these tools often require some programming knowledge, AI can help manual testers get started faster.

1. AI Can Convert Manual Test Cases into Automation Scenarios

Your existing manual test cases are a great starting point.
Consider this manual test case:
Verify that a registered user can log in with valid credentials.
  1. Open the login page.
  2. Enter a valid email address.
  3. Enter a valid password.
  4. Click Login.
  5. Verify that the dashboard is displayed.
You can ask AI to turn it into an automation-ready outline or starter script.
Example prompt:
Convert this manual test case into a beginner-friendly Playwright test in JavaScript. Add comments explaining each step.
AI can generate a first draft, saving time and helping you see how manual steps map to code. You should still review the result carefully, because the AI may make incorrect assumptions about URLs, element names, or expected behavior.

2. AI Can Explain Code in Simple Language

Code can be intimidating when you are new to automation. AI can explain individual lines, error messages, and automation concepts in plain language.
For example, this Playwright statement:
await page.getByRole('button', { name: 'Login' }).click();
means:
  • Find a page element with the role of a button.
  • Find the button named “Login.”
  • Click it.
You can ask AI questions such as:
  • “Explain this test script line by line.”
  • “What is the difference between a locator and an assertion?”
  • “Explain this error message for a beginner.”
  • “Why did this test fail?”
This turns AI into a learning partner rather than simply a code generator.

3. AI Can Generate Starter Scripts for Common Test Flows

Many UI tests follow familiar patterns. AI can help create starter scripts for scenarios such as:
  • Login and logout
  • Registration forms
  • Search functionality
  • Form validation
  • Add-to-cart flows
  • Password reset
  • Navigation checks
  • Profile updates
For example:
Create a Playwright TypeScript test that verifies an error message is shown when a user submits a registration form with an empty email field. Use clear comments for a beginner.
The generated script may not be ready for production immediately, but it can give you a useful starting point. You can then run it, adjust the page details, and learn from each change.

4. AI Can Help You Create Better Locators

A locator is how an automation tool finds an element, such as a button, text field, or message.
Poor locators make tests fragile. For example, a long CSS selector based on page layout may break when the UI changes. AI can suggest more stable locator strategies.
A good locator preference is usually:
  1. Accessible roles and names, such as a button named “Submit”
  2. Form labels, such as “Email address”
  3. Stable test IDs, such as data-testid
  4. Meaningful unique attributes
  5. CSS or XPath only when necessary
You can provide an HTML snippet and ask:
Suggest reliable Playwright locators for these elements. Prefer accessible roles, labels, and test IDs. Explain why each locator is appropriate.
AI suggestions should be validated against the real application and your team’s standards.

5. AI Can Help Diagnose Test Failures

Automated tests can fail because of a real product defect, but they can also fail because of timing issues, missing test data, environment problems, or unstable locators.
Suppose you see this error:
Timeout exceeded while waiting for locator('text=Welcome')
AI can help you investigate possible causes:
  • Is the expected text correct?
  • Did login actually succeed?
  • Is the element visible only after a page load or API response?
  • Is the locator incorrect?
  • Is the test looking in the wrong frame or modal?
  • Is the environment slow or unavailable?
Do not blindly apply AI-suggested fixes. For example, adding a long fixed wait may hide the real problem and make tests slower. Instead, make the test wait for a meaningful condition, such as a visible message, expected URL, or completed action.

6. AI Can Suggest Test Data and Edge Cases

Manual testers are already skilled at thinking about unusual user behavior. AI can help expand your coverage by suggesting cases for:
  • Required fields
  • Invalid formats
  • Boundary values
  • Special characters
  • Duplicate records
  • Error messages
  • Permission-based behavior
  • Accessibility checks
For a registration page, you might ask:
Suggest positive, negative, boundary, and accessibility test scenarios for a registration form with name, email, password, and confirm-password fields.
Use only safe, fictional test data in AI prompts. Do not share customer data, production credentials, confidential source code, or sensitive business information unless your organization has approved the AI tool and data-sharing process.

7. AI Can Help Reduce Repeated Code

As your automation suite grows, you may repeat steps such as logging in before every test. AI can help identify repeated code and suggest reusable functions.
For example:
async function login(page, email, password) {
  await page.goto('/login');
  await page.getByLabel('Email').fill(email);
  await page.getByLabel('Password').fill(password);
  await page.getByRole('button', { name: 'Login' }).click();
}
This can be reused in multiple tests. If the login page changes, you update the shared function instead of changing every test separately.
Keep your test design simple at first. Ask AI to explain any suggested reusable structure before adding it to your project.

8. AI Can Help Create Documentation

Automation projects need understandable documentation. AI can assist with:
  • README files
  • Setup instructions
  • Test execution steps
  • Test naming conventions
  • Comments for complex scripts
  • Defect summaries
  • Test result summaries
For example:
Write a simple README section for beginners explaining how to install Playwright, run tests, and view the test report.
Clear documentation makes it easier for you and your teammates to maintain the automation suite.

What AI Cannot Replace

AI can accelerate your work, but it cannot replace a tester’s judgment. You still need to decide:
  • Which scenarios are valuable to automate
  • Whether requirements are complete and clear
  • Whether a generated test checks the correct business outcome
  • Whether a failure is a product defect or a test problem
  • Whether test data is safe to use
  • Whether generated code follows team security and quality standards
Think of AI output as a draft, not as a final answer. Review it, run it, and verify that it tests the intended behavior.

A Simple Learning Path for Manual Testers

You do not need to automate an entire regression suite on day one. Start small.

Step 1: Select one repetitive, stable test

Choose a simple scenario such as login, search, or required-field validation.

Step 2: Use the automation tool chosen by your team

If your organization already uses Playwright, Selenium, Cypress, or another framework, begin there. Following existing team standards will make learning easier.

Step 3: Ask AI to explain generated code

Do not only ask for scripts. Ask what each line does, why a locator was selected, and what the assertion verifies.

Step 4: Run and validate the test

Confirm that the test performs the expected user journey. Also confirm that it fails when the behavior is intentionally changed.

Step 5: Add scenarios gradually

After one positive test works, add a negative test. Then improve locators, add reusable functions, and learn reporting and continuous integration over time.

Final Thoughts

AI can help manual testers begin UI automation by converting test cases into starter scripts, explaining code, suggesting locators, diagnosing failures, and generating documentation. It lowers the barrier to entry, but it does not eliminate the need for testing knowledge, review, and practice.
Start with one small test case you already understand. Use AI to learn from the automation draft, validate every step, and improve it gradually. Your manual testing expertise is not being replaced—it is becoming even more valuable when combined with automation skills.

Friday, 4 September 2026

How AI is Finally Fixing Our Worst API Testing Nightmares

Beyond Broken Endpoints: How AI is Finally Fixing Our Worst API Testing Nightmares

Beyond Broken Endpoints: How AI is Finally Fixing Our Worst API Testing Nightmares

If you’ve ever wanted to pull your hair out over a failing POST /users test at 4:45 PM on a Friday, you are in good company. API automation testing is supposed to be the "easy" part of the testing pyramid. No complex UI locators to break, no browser rendering issues to debug, just clean JSON payloads and predictable status codes. Except, it’s rarely that simple. In the real world, API automation is a constant battle against dynamic data, shifting schemas, and brittle test suites. But things are changing. AI is quietly stepping in to take over the tedious, repetitive parts of the job, turning API testing from a maintenance headache into something almost… effortless. Let’s look at the biggest pain points in API testing today, and how AI-powered tools (both free and paid) are solving them.


The Real-World Obstacles: Why API Testing Breaks

Before we look at the fixes, let’s be honest about what makes API automation so exhausting.

1. Schema Drift (The "Who Changed the JSON?" Problem)

You write a flawless suite of tests. Overnight, a developer updates a microservice and changes a response key from user_id to userId. Suddenly, fifty tests fail. The API still works, but your tests are dead in the water.

2. The Dynamic Data & Auth Token Chase

Managing dynamic states—like generating a fresh OAuth token, passing it to a helper function, grabbing an ID from a GET response, and feeding it into a DELETE request—requires a lot of boilerplate code. If one step timing-out or returning slightly different data occurs, the whole chain collapses.

3. Assertion Fatigue

Writing assertions is boring. To properly test an endpoint, you need to verify status codes, headers, response times, data types, and specific value ranges. Writing these manually for dozens of endpoints is a recipe for developer burnout, which often leads to cutting corners (e.g., only asserting status === 200 and calling it a day).


How AI Actually Helps (Without the Hype)

AI isn't going to replace the human understanding of business logic, but it is incredibly good at handling the heavy lifting of API testing.

  • Self-Healing Tests: When a field name changes slightly, AI can analyze the historical context of the payload, realize that userId is the same as the old user_id, update the test logic on the fly, and flag it for your review instead of failing the build.
  • Auto-Generating Payloads: Instead of manually writing mock JSON objects, you can feed an AI your API schema, and it will automatically generate edge-case payloads (like empty strings, SQL injection attempts, and massive integers) to stress-test your endpoints.
  • Automated Assertions: Instead of writing twenty lines of assertion code, you can ask an AI assistant to analyze a sample response and write the assertion block for you in seconds.

The Toolbox: Paid vs. Free AI Testing Tools

If you want to start leveraging AI for your API testing, you don't need a massive budget. Here is a breakdown of the best tools currently available.

The Paid Heavy Hitters

These platforms are built for teams and enterprise workflows, offering robust, out-of-the-box AI integrations.

1. Postman (with Postbot)

  • What it is: Postman is already the industry standard for API development, but its built-in AI assistant, Postbot, takes it to the next level.
  • How it helps: You can highlight a response payload and tell Postbot in plain English: "Write tests to verify all fields are present and response time is under 200ms." It writes the JavaScript code instantly. It can also generate mock data and fix broken test scripts on the fly.
  • Pricing: Postbot is available as an add-on to Postman plans (starting at around $9/user/month), though there is a limited free tier to try it out.

2. Katalon Platform

  • What it is: A comprehensive quality management platform that combines UI, mobile, and API testing.
  • How it helps: Katalon uses AI to auto-generate test code from your API documentation (like Swagger/OpenAPI specs) and offers self-healing capabilities that prevent test suites from breaking when minor changes occur in API responses.
  • Pricing: Free tier available for basic use; premium plans start at $167/month for professional teams.

The Free and Open-Source Game Changers

If you prefer open-source software or are working with zero budget, these tools are incredibly powerful.

1. Keploy

  • What it is: An open-source, developer-focused API testing tool that uses AI/ML to automate the entire test generation process.
  • How it helps: Keploy runs in the background while you run your application. It records actual API traffic (including database calls and external dependencies) and automatically generates test cases and mocks. It completely bypasses the need to write manual boilerplate API test code.
  • Pricing: 100% Free and Open Source.

2. Local AI + Playwright / REST Assured (The DIY Route)

  • What it is: Running a local, open-source Large Language Model (like Llama 3 via Ollama) directly on your machine.
  • How it helps: If your company has strict data privacy rules and won't let you send API payloads to external servers (like OpenAI), you can use a local LLM. You can feed your Swagger file or API controller code into the local model and ask it to: "Generate a complete suite of Playwright API tests covering positive, negative, and boundary cases."
  • Pricing: Completely free.

The Verdict: Don't Code Harder, Code Smarter

API testing doesn't have to be a repetitive cycle of fixing broken assertions and updating outdated mocks. The smartest approach today is a hybrid one. Let AI write the boilerplate code, generate your edge-case payloads, and draft your assertions. Save your brainpower for the high-level architecture: designing the integration flows, understanding the security implications, and ensuring the business logic actually makes sense.

Have you started using AI in your API testing pipeline yet? What’s your go-to tool? Let me know in the comments below!

Thursday, 20 August 2026

Top 10 YouTube channels for AI testing and QA automation

 The top 10 YouTube channels for AI testing and QA automation include a mix of dedicated software quality assurance (QA) creators, specialized platforms, and core engineering hubs that focus heavily on evaluating, validating, and testing artificial intelligence models

YouTube creators do not usually share personal phone numbers or direct personal email addresses on public profiles to prevent spam. Instead, you can reach out to them via their official websites, business contact forms, or primary professional networks

Top 10 YouTube Channels for AI Testing

#Channel NameCore Focus in AI Testing & AutomationChannel URLPrimary Contact / Verification Method
1Naveen AutomationLabsDeep dives into Agentic AI for software testing, modern QA tools, and building smart automation workflows.Naveen AutomationLabsContact via Naveen AutomationLabs Website
2Automation Step by StepStep-by-step beginner guides for applying AI within software testing framework design and implementation.Automation Step by StepContact via Automation Step by Step Platform
3The Testing AcademyComprehensive series on AI tools every QA tester must learn, LLM validation, and test optimization.The Testing AcademyContact via The Testing Academy Official Site
4LambdaTestIndustry webinars and execution guides on AI-driven cross-browser visual testing and test intelligence.LambdaTestContact via LambdaTest Business Support
5Software Testing by Daniel KnottHands-on reviews focusing directly on free and premium AI testing applications and automation practices.Daniel Knott QAContact via Daniel Knott Blog & Portal
6Matthew BermanRigorous hands-on benchmarking and systematic testing of new large language models (LLMs) and local AI agent tools.Matthew BermanContact via Matthew Berman on LinkedIn
7DeepLearning.AIFounded by Andrew Ng; critical for understanding fundamental evaluations, prompt testing, and AI agent validation layers.DeepLearning.AIContact via DeepLearning.AI Contact Form
8Automate With AmitPractical tutorials showcasing how to use AI-powered assistant extensions and smart tools for functional testing.Automate With AmitContact via business queries on his YouTube 'About' page.
9SDET - QA Automation TechieTechnical execution pathways for shifting from standard API automation into AI-based test suites and validations.SDET TechieContact via business inquiries on his YouTube portal.
10Andrej KarpathyEssential masterclasses for learning how to build and evaluate neural network models from the ground up.Andrej KarpathyContact via Andrej Karpathy GitHub Profile

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