You open Cursor, Copilot or whatever AI tool you like ... You type: "write a login test" The agent responds. It looks like a test. Imports are there. Structure looks familiar. But you look closer. - Hardcoded credentials. - Wrong file location. - No page objects. - Naming convention are ignored. - And on top of all that, you run it... it fails. ──────────────────────────────────────── 🧠 𝐖𝐡𝐲 𝐭𝐡𝐞 𝐀𝐠𝐞𝐧𝐭 𝐆𝐮𝐞𝐬𝐬𝐞𝐬 𝐖𝐫𝐨𝐧𝐠 Most people at this point blame the model. - "Claude is bad at tests." - "GPT doesn't understand Playwright." - "I need a better model." But the reality is... the model did not fail you. You gave it nothing useful to work with. Think of the agent like a new hire. Smart. Fast. Capable. But they have never seen your project before. ➤ They do not know where your fixtures live. ➤ They do not know how you name test files. ➤ They do not know what credential pattern you use. ➤ They do not know whether you run tests after every change. You told them: "write a login test." So they try to find all that information and make a lot of assumptions. Every assumption is a guess. Every guess is a risk of being wrong. That is an onboarding problem and a lack of proper documentation. ──────────────────────────────────────── 📝 𝐖𝐡𝐚𝐭 𝐚 𝐑𝐞𝐚𝐥 𝐓𝐚𝐬𝐤 𝐒𝐩𝐞𝐜 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞 In the AI coding agents world, that documentation is often called "Task Spec." A task spec is not a longer prompt. It is a precise set of constraints that leaves the agent very little room to guess. Here is the difference. 𝗪𝗲𝗮𝗸 𝗽𝗿𝗼𝗺𝗽𝘁: ``` write a login test ``` 𝗚𝗼𝗼𝗱 𝗧𝗮𝘀𝗸 𝗦𝗽𝗲𝗰: `` Write a login test. Before making any changes, inspect the existing tests in /tests/auth/ and follow the existing suite structure, naming, and conventions. Task: - Add a test for successful login using the existing credentials fixture. - Place it in the appropriate existing auth test suite. - Do not hardcode credentials or duplicate fixture data. - Do not create new files unless no existing test file is appropriate.

Hey QA fam! 👋 Ever been asked in an interview about the difference between smoke and sanity testing? It’s a classic question! They sound similar, but they’re actually different. Here’s the quick breakdown: Smoke Testing 🔥 Think: “Does this thing even turn on?” It’s a broad but shallow check to see if your app’s critical functions work at all. When to run it: • Right after a new build drops • Before starting detailed testing What you’re checking: • Can users log in? • Does the homepage load? • Do critical workflows start? Example: New build deployed → Quick smoke test checks login, navigation, basic functionality → If it fails, reject the build immediately. Sanity Testing 🧠 Think: “Did that bug fix actually work?” It’s a narrow but deeper check on specific functionality after a bug fix or small change. When to run it: • After a bug fix • After a minor code change What you’re checking: • Did the bug get fixed? • Do related features still work? Example: Dev fixed the “Forgot Password” link → Sanity test verifies the fix works and didn’t break related login features. Quick Comparison 📊 Smoke Testing: • Broad and shallow • Tests build stability • After new build • Accept/reject build Sanity Testing: • Narrow and deep • Tests specific functionality • After bug fix • Accept/reject fix The Easy Way to Remember 🏠 Smoke Test: Walking through a house checking if lights work, doors open, water runs. Basic stuff! Sanity Test: Going back to check if that leaky faucet the seller “fixed” actually works now. Bottom Line 🎯 • Smoke testing = “Is this build stable enough to test?” • Sanity testing = “Did that fix actually work?” Use both strategically and you’ll save tons of time! Happy Testing! 🚀 Your turn: Which one do you use most in your projects? Drop a comment! 💬

In Part 2 I covered CLI tools. They work. But for QA automation especially if you're just starting... they're simply the wrong tools. ──────────────────────────────────────── 𝐘𝐨𝐮 𝐒𝐞𝐞 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 ➤ CLI gives you output on a screen. A wall of text. ➤ IDE tools show changes line by line, inside your actual files. Right in front of you. In Cursor specifically, you accept or reject each change individually. One line at a time. That matters for beginners. When something goes wrong, you see exactly what changed and where. You can ask the AI to explain the change while looking at it. Not a printout. The actual code that helps you to actually learn. ──────────────────────────────────────── 🔹 𝐖𝐡𝐚𝐭 𝐂𝐮𝐫𝐬𝐨𝐫 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐈𝐬 Cursor is a fork of VS Code. Fork means: a copy of an existing code, taken in a new direction. VS Code is Microsoft's editor. Cursor took that foundation and rebuilt it for AI from the ground up. Compare that to Copilot. Copilot is a plugin bolted onto VS Code. It was added after the fact. Not designed to be there. That difference shows up in practice. Cursor was built with AI as the core. Copilot was added on top. ──────────────────────────────────────── ⚡ 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐌𝐨𝐝𝐞𝐥𝐬, 𝐎𝐧𝐞 𝐓𝐨𝐨𝐥 Cursor gives you access to models from both Anthropic and OpenAI in one place. Claude Sonnet. Claude Opus. GPT-4o. You pick per task. ⤷ Hard problem or complex refactor? Use Opus or GPT Codex ⤷ Quick fix or small helper? Use something cheaper. That lets you control spending and get the best output without switching tools and having 2 subscriptions. Pricing is also transparent. You know what you're paying. No surprises. ──────────────────────────────────────── 🌐 𝐓𝐡𝐞 𝐁𝐮𝐢𝐥𝐭-𝐈𝐧 𝐁𝐫𝐨𝐰𝐬𝐞𝐫 Cursor has a browser built directly into the IDE. 1. Open any page. 2. Click on elements: buttons, inputs, dropdowns, etc. 3. Ask Cursor to extract the best locators for your test automation. Hunting for locators manually is one of the most tedious parts of UI testing. This feature cuts that work significantly.

👋 Hey there! 𝐖𝐞𝐥𝐜𝐨𝐦𝐞 𝐭𝐨 𝐀𝐈 & 𝐐𝐀 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫. AI is changing Software Development. And it is changing QA with it. QA Engineers who know how to use AI will: ⬩Deliver in days what used to take two weeks ⬩Do work that used to require deep expertise. With AI, basic knowledge can produce senior-level results ⬩Get instant AI feedback on tests, code, and debugging decisions The same applies to Software Developers. AI multiplies their delivery speed. QA becomes the bottleneck. That's why companies are fighting to hire QA Engineers who can match that speed. 💡 In fact, as of early 2026, many companies started adding AI coding tasks to their interview process. QA Engineers who ignore AI won't just fall behind, they risk losing their career entirely. That's not doomsaying. In 2026, tech companies laid off 55,775 people (https://www.trueup.io/layoffs). So, are those layoffs because AI is replacing people? No. AI is not replacing anyone. People who use AI are replacing people who don’t. Unlike the transition from Manual Testing to QA Automation, which took a decade, this shift is happening fast. Capable AI Coding Agents only became real in late 2025. Just a few months later, the entire tech world had changed. That's what this community is about. It's for people who see this shift and understand that right now is not just a pivotal moment for them. It's a short golden window to become one of the first truly AI-Powered QA Automation Engineers / SDETs and set yourself up for a long, safe, and extremely high-paying QA career. ──────────────────────────────────────── 𝐀𝐛𝐨𝐮𝐭 𝐌𝐞, 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐈 𝐚𝐦 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 I'm 𝐌𝐚𝐭𝐯𝐢𝐲, a Vegas-based 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐒𝐃𝐄𝐓 with 𝟏𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. I’ve worked across startups and large enterprises, building QA automation frameworks and testing infrastructure across pretty much all modern stacks and tools. In 2025 I introduced AI coding agents into my team's QA Automation workflows. The team adopted it. Management noticed.