Reflections

🚀 Vibe Coding: Building a Home Library with Antigravity AI Software development is entering a fascinating new era where AI-powered tools are accelerating how applications are designed and built . Recently, I watched an insightful Malayalam livestream from Brototype that demonstrates this new approach through a concept which we are all familiar with called  “Vibe Coding.” In the session, the developers build a home library management application called Vaayana using modern AI-driven development tools. This post summarizes the key ideas and lessons from that livestream. Watch the full livestream on Youtube What is “Vibe Coding”? “Vibe Coding” is an emerging development approach where AI tools collaborate with developers to generate software quickly . Instead of manually writing every line of code, developers: Describe the application idea. Use AI tools to generate project structure and components. Review, refine, and integrate the generated code. This approach dramatically reduces ...

Understanding Large Language Models: A Practical Learning Path Recently, I have been spending time exploring Artificial Intelligence and Large Language Models (LLMs) . There are countless videos, tutorials, and courses available today. While this abundance of material is exciting, it can also be overwhelming when trying to understand where to begin and how to learn systematically . Among the many resources I came across, the following video resonated with me the most because it provides a clear and structured roadmap for learning LLMs properly . 🎥 Video: Learning Path for Large Language Models 🔗 Video Link: https://www.youtube.com/watch?v=U07MHi4Suj8 Why This Video Stood Out Many people today are using tools like ChatGPT, Claude, Gemini, and other AI systems , but understanding how these systems actually work requires a deeper learning path. This video explains how to truly understand LLMs , not just how to use them. It answers questions many learners struggle with: Should you start ...

  Running AI Locally: My First Experiment As part of my journey to understand Artificial Intelligence and Large Language Models , I wanted to go beyond simply using cloud-based AI tools. Instead, I decided to try something more hands-on: Running an AI model locally on my own machine. Running AI locally offers several advantages: More control over the models Better privacy No dependency on external APIs Ability to experiment freely Deeper understanding of how AI systems actually work This post summarizes the tools I used and the steps I followed to run AI locally. Tools and Technologies Used To set up a local AI environment, I used the following tools. 1. WSL (Windows Subsystem for Linux) Since I was working on Windows , I first installed WSL . WSL allows you to run a Linux environment inside Windows , which is extremely useful because most AI tools are designed to run on Linux systems. Benefits include: Linux development environment Better compatibility with AI frameworks Easier in...

 I worked on UI Automation using Cucumber, Java and Selenium in a company ( company name hidden due to confidentiality agreement). After creating the framework completely, when it was time to deploy to pipeline, I found we don't have devops team in the company. The devs were helping out by doing devops work in addition to their normal duties. They explained that they can help with C# code ( basically .Net core deployments into agents), but could not help maintaining agents which run JVM ( for Java code). It was time to switch to C#. I found Specflow is a C# implementation of Cucumber and we need Visual Studio ( Professional at least) to continue the work. The management at the highest level were very supportive and they were willing to spend the money and give me all the support to rewrite the entire framework in C#. It was time to make Automation a success in the company. And it was going to be more thrilling than any movie we can imagine with all the twists and turns.

  When working on the Mac and coding in IntelliJ, sometimes you see an error in the console, and it gives the file name. If you want to open the specific file (instead of searching a particular text in all files), use this shortcut instead. Alternatively, if you are in the finder window and want to go to a particular folder, you can always press "Cmd + shift + G" and paste the path in the dialog box.

As a tester, you might be testing several systems and each have different commands. A common interface might be the bash shell. How do you get all the previous commands you typed in bash before? Once you close the bash command? I struggled with this for a few days, and even wrote down commands in notepad, so that i can use them later. Later, I wondered how easy my work would become if i could type the first word of my command and i could search through my bash history. The problem Lets say, i used the following command a few days ago to encrypt a file using gpg. $ gpg --encrypt -r < > REGISTER.ABCD.01102018.917.csv   Today, I want to encrypt a new file. I know I used gpg before, but I don't remember the exact syntax.  So I want to type gpg in my command prompt and then use my arrow keys "up" or "down" to search through my bash history. Solution Add the following two lines to your  ~/.inputrc "\e[A": history-searc...