Automation Insights 101

AI is a fantastic tool for coding—until it isn't. It promises to save time, automate tasks, and help developers move faster. But if you trust it  too much , you might just end up doing extra work instead of less. How do I know? Because the other day, I did exactly that. The Day AI Made Me File My Own Bug I was working on a personal project, feeling pretty good about my progress, when I asked AI to generate some code. It looked solid—clean, well-structured, and exactly what I needed. So, in a moment of blind optimism, I deployed it  without testing locally first. You can probably guess what happened next. Five minutes later, I was filing my own bug report, debugging like a madman, and fixing issues on a separate branch. After some trial and error (and a few choice words), I finally did what I should have done in the first place:  tested the code locally first.  Only after confirming it actually worked did I roll out the fix. Sound familiar? If you've ever used AI-gene...

The idea for this project started with a simple question: How can I create a personal workout AI that won't judge me for skipping leg day? I wanted something that could recommend workouts based on my mood, the time of day, the season, and even the weather in my region. This wasn't just about fitness—it was an opportunity to explore AI, practice web app engineering, and keep myself entertained while avoiding real exercise. Technologies and Tools Used To bring this chatbot to life, I used a combination of modern technologies and services (no, not magic, though it sometimes felt that way): Frontend: HTML, CSS, and JavaScript for the user interface and chatbot interaction (because making it look cool is half the battle). Backend: Python (Flask) to handle requests and AI-powered workout recommendations (it's like a fitness guru, minus the six-pack). Weather API: Integrated a real-world weather API to tailor recommendations based on live conditions (because nobody...

Once upon a time, in a land filled with legacy test frameworks and stale documentation, a brave automation tester (me) decided to embark on an epic quest: Setting up Playwright. Did I have experience with Playwright? Nope. Did I care? Also nope. Did I have AI by my side? Absolutely. Why Even Try? Look, as an automation tester, I tend to stick with what works. I mean, if a tool runs my tests, why mess with it? But every now and then, an opportunity arises to experiment with something new—whether out of necessity, curiosity, or sheer boredom. This time, Playwright caught my attention, and with AI as my trusty sidekick, I was off to the races. Step 1: Let AI Do the Heavy Lifting Back in the olden days (aka pre-AI times), setting up a test automation framework meant: ☠️ Digging through outdated documentation 💀 Copy-pasting error messages into Google ⚰️ Watching my soul leave my body as I debugged for hours But this time? I outsourced my brainpower to AI. Here’s what I asked it to d...