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Image generated using Midjourney The past years have been an absolute rollercoaster (or joyride) of rapidly evolving generative AI technologies. In the twenty-five years I’ve counted myself a software developer, I cannot recall a tectonic shift of a similar magnitude, one that is already fundamentally changing how software is being written.  It would be shortsighted

In this post, I’ll show you step by step how to build and deploy a chat powered with LLM — Gemini — in Streamlit and monitor the API usage on Google Cloud Console. Streamlit is a Python framework that makes it super easy to turn your Python scripts into interactive web apps, with almost no front-end work. Recently, I

Coding has been a part of my life since I was 10. From modifying HTML & CSS for my Friendster profile during the simple internet days to exploring SQL injections for the thrill, building a three-legged robot for fun, and lately diving into Python coding, my coding journey has been diverse and fun! Here’s what

Introduction I’m a huge fan of interactive visualizations. As a computer vision engineer, I deal almost daily with image processing related tasks and more often than not I am iterating on a problem where I need visual feedback to make decisions. Let’s think of a very simple image processing pipeline with a single step that

The Universal Approximation Theorem states that a neural network with a single hidden layer and a nonlinear activation function can approximate any continuous function.  Practical issues aside, such that the number of neurons in this hidden layer would grow enormously large, we do not need other network architectures. A simple feed-forward neural network could do the trick.

You see a math formula you don’t immediately understand. Your instinct? Stop reading. Don’t. That’s exactly what I told myself when I started reading Probabilistic Machine Learning – An Introduction by Kevin P. Murphy. And it was absolutely worth it. It changed how I think about machine learning. Sure, some formulas might look complicated at

Prompt engineering alone won’t get us to production. Fine-tuning is expensive. And reinforcement learning? That’s been reserved for well-funded labs with massive datasets until now. New research from Microsoft and academic collaborators has overturned that assumption. Using Reinforcement Learning with Verifiable Rewards (RLVR) and just a single training example, researchers achieved results on par with

If you’ve followed me for a while, you probably know I started my career as a QA engineer before transitioning into the world of data analytics. I didn’t go to school for it, didn’t have a mentor, and didn’t land in a formal training program. Everything I know today—from SQL to modeling to storytelling with