AI-Powered Software Testing for Beginners - ZERO Code

Learn how to run a large language model locally on your machine to protect privacy, avoid cloud limits, and use offline tools like OLAMA, LM Studio, and GPT for all.

Learn how to run a large language model locally with tools like olama, install and download models, and compare cloud and local deployments while considering model parameters and sizes.

Run a private, local large language model with Ollama's graphical user interface, download the 20 billion parameter model, and chat offline with thinking and search features.

Learn to run Ollama from the command line, download gpt oss or qn3 models, and generate code prompts like Selenium c sharp dotnet login, with output differing by model size.

Connect to a cloud model with Ollama to run kimk2.5 cloud on the Ollama cloud, enabling fast testing on machines without a gpu using the free tier.

Master prompt engineering to guide large language models with structured prompts; learn role, task, context, format, constraints, and how good prompts reduce tokens and cost while improving test generation.

Demonstrates good versus bad prompts via a test-expert demo that generates 10 forget-password with two-factor authentication test cases for a banking app, including positive and negative scenarios and tabulated evidence.

Explore different prompt types—zero short prompts, one short prompt, few short prompts, role-based prompt, and chain of thoughts prompts—and learn prompt engineering to design test prompts for software testing.

Use chain of thought prompts to select release-critical tests from a 50-test suite, leveraging zero short, one short, and few short prompts with role-based prompts.

Explore using prompt engineering to iteratively generate and refine behavior driven development test cases in plain language, with BDD patterns for features like user authentication and employee management.

Learn how ai agents empower language models to act on external tools like file systems, browsers, and github, autonomously for extended periods and enable zero-code agent creation for testing workflows.

MCP, the model context protocol introduced by Anthropic, enables AI assistants to access data sources and tools via a client–server standard, a universal bridge for AI agents and servers.

Explore how MCP servers act as model context protocol bridges that securely access files, databases, APIs, and tools, and learn where to find them for AI.

Explore setting up an MCP server on a cloud desktop, using connectors and tools like file system, Playwright, and web search to enable external operations.

Demonstrate file system automation using the mcp server and file system connector to list directories and read logs through ai tool calls, with human-in-the-loop permissions.

Install and configure the Playwright MCP server on cloud desktop, choose between Microsoft or custom MCP server, and enable user interface automation without coding.

Showcases how the playwright mcp server enables automation testing without code, guiding an ai agent to navigate, login, create an employee, and verify creation in a browser.

Learn to configure and run the Playwright MCP server in Visual Studio Code, integrate GitHub Copilot, manage MCP servers, view configurations, and run automated tests with AI agents.

Explore how wipe coding and wipe testing use natural language prompts to generate code and test software with tools like VS Code, Copilot, Cursor, Windsurf, Cloud Code, and Warp terminals.

Explore cursor ide, configure the mcp server with 22 tools, including playwright mcp, and simulate chat operations like visual studio code with github copilot using gpt 5.2 codecs.

Harness the GitHub Copilot CLI and Playwright to configure test sprite and Playwright MCP server, run automated tests, and navigate web apps without code.

Explore wipe testing in action using AI agents to generate requirement docs and automated test cases without coding. Cross-check AI scaffolds from MCP servers against requirements to capture edge cases.

Generate product requirements, test plans, and tests with AI and Playwright MCP. Create markdown PRD and test plan documents via zero code prompts, covering CRUD and edge cases.

Learn to generate all BDD scenarios with AI and Playwright MCP server, categorize them into feature files by functionality, and export test cases like login, logout, register, and more.

Generate automated Playwright tests from manual BDD scenarios and product requirements, building a TypeScript framework and running tests with Playwright MCP server to deliver fast, parallel test execution and reports.

Generate an automated test execution report with ai, review passing and skipped tests, and observe fully automated tests and reports without writing any code.

Explore ai skills as no-code extensions that empower ai agents with specialized workflows. Define skill metadata, like name and description, and control allowed tools for consistent testing outputs.

Understand AI agent skills, how to discover them on skills.sh and cloud docs, and install and use them in projects with metadata and folder structures.

Create an ai agent skill from prompts, save and name the skill file in the skills folder, specify allowed tools including playwright test, and follow the skill specification.

Learn to create and invoke custom ai agents using skills and .agents folders on your local machine, crafting a skill.md to generate product requirements and bdd features with Playwright tests.

Automate test creation with one single command using a global skill location AI agent, generating Playwright tests from product requirements and a test plan, then run tests and view reports.

Explore custom agents in VS Code to tailor AI personas for security reviewer, planner, solution architect, and other roles, with handoffs and single click workflow.

Learn to build a custom VS Code agent using a .agent.md file, configure prompts and tools, and run it to auto-generate tests in your workspace.

Learn to create and deploy AI agent skills from plain English prompts, use custom chat modes in VS Code, and refine prompts into testing skills.

Discover n8n, an open source, drag-and-drop workflow platform for orchestrating APIs, microservices, and AI agents. Build basic and AI-enabled workflows and explore cloud vs. Docker hosting.

Sign up for n8n and explore a live demo of workflows, including form submissions, conditional branching, email notifications, and updating Google Sheets, plus ai-powered site monitoring with OpenAI models.

Configure your n8n mcp server on cloud desktop to trigger workflows that auto-fill forms (name, email, phone, inquiry type) and save results to an excel sheet, then email alerts.

Create and test an n8n workflow from scratch using a form trigger, add steps, map inputs and outputs, and route data with a conditional node to handle coupon code inquiries.

Use the Gmail node in n8n to automatically send an email when a coupon code triggers, with a dynamic message and subject, plus optional Google Sheets logging.

connect with Google Sheet using n8n, sign in with Google, select the sheet, append or update rows, map name, email, phone, and description, and troubleshoot column to match on errors.

Publish your n8n workflow as an MCP server, then access it via MCP and copy the connection details to set up the MCP instance on your cloud desktop.

Demonstrates building an n8n ai agent workflow that monitors website status codes (200, 404, 500), analyzes failures, and outputs email and google sheet alerts with a configurable json workflow.

Deploy n8n in the cloud or on your local machine using docker or npx for unlimited workflow execution with open-source, zero-code automations.

Explore ai-based codeless testing tools and use cases powered by ai agents and large language models. See how Playwright, Postman, Cypress, and Test Sprite enable testing from plain English.

Explore Playwright AI agents: planner, generator, and healer that transform test planning into markdown plans, generate Playwright test files, and automatically repair failing tests, showcasing AI-powered test automation.

Explore Cypress Studio AI, featuring AI assertion recommendations and DOM snapshots, note its paid features, and reflect on the broader shift toward AI-powered testing tools like Playwright.

Explore TestSprite, an AI-powered test automation tool that runs autonomous testing agents for web apps and shows MCP server integration with IDEs to generate and run frontend and backend tests.

Learn how the test sprite MCP server enables testing local applications by discovering your app from code and installing the server in Visual Studio Code with API key configuration.

Configure and run the TestSprite MCP server with GitHub Copilot, bootstrap the frontend, and import PRD documents to generate and execute about 29 frontend tests.

Explore how Testbrite MCP server and Google antigravity empower autonomous AI agents to test, fix, and validate software without coding, with generated test plans and specs.

Configure the testpride MCP server AI agent in the GitHub Copilot CLI to run codeless, AI-powered tests from the command line.

Discover TestGrid's co-tester 2.0, an enterprise-grade AI agent for software testing. Test web and mobile apps with context-aware automation, auto test creation, self-healing, guardrails, vision language intelligence, and bug logging.

Accelerate Android mobile automation with TestGrid’s CoTester AI, crafting end-to-end regression tests from plain English prompts, uploading an APK, and running no-code tests with autopilot mode and Appium-backed execution.

Identify the next steps to apply AI-driven testing beyond traditional tools, exploring AI agents, RAGs, and MCP servers, and advancing from zero-code tests to code-enabled workflows.