AI-driven insight & ML solutions from Data Points to Business Decisions.

  Introduction: In modern finance, alpha isn't just in the numbers; it’s in the speed of interpreting text. While price data is structured, news is messy. MarketPulse is a Retrieval and Sentiment Analysis pipeline designed to automate the correlation between financial headlines and market volatility. The Problem: The "Context Gap" in Market Data Standard stock trackers tell you what happened, but rarely why . To build a system that understands the "why," I needed to bridge two different worlds: the Python data-science ecosystem and the TypeScript full-stack environment. Architectural Overview: The Ingestion-to-Insight Flow The system is built on a Producer-Consumer architecture, organized within a unified src/ directory to ensure maintainability: Automated Data Ingestion (The Producer): Using Python and GitHub Actions, I implemented hourly "Cron-Jobs" that fetch data from yfinance and various news feeds. This ensures the database stays fresh w...

Introduction: In today's fast-paced development landscape, quickly understanding new codebases is paramount. From accelerating new team member onboarding to rapidly grasping external project architectures, the ability to query a repository's documentation semantically can be a game-changer. This post details the technical journey of building GitQuery AI , a Retrieval-Augmented Generation (RAG) application that allows developers to interactively ask questions about any public GitHub repository. The Problem: Information Overload & Onboarding Tax Traditional methods of codebase understanding—cloning, grep ing, and exhaustive manual documentation reading—are time-consuming and inefficient. The goal for GitQuery AI was to create a natural language interface that could provide instant, accurate answers by understanding the context of the codebase. Architectural Overview: The RAG Pipeline Our solution is centered around a robust RAG pipeline: Data Ingestion: Fetching and prep...

As data volumes explode, ensuring the integrity and reliability of our data assets has become paramount. Many data teams still grapple with reactive approaches, fixing issues after they've impacted dashboards or critical reports. I recently tackled this challenge head-on by building 'Data Quality Guard Pro,' a high-fidelity metadata monitoring engine designed to simulate and manage enterprise-level data quality (DQ) checks. This project was more than just a coding exercise; it was an exploration into architecting a scalable, observable, and extensible data quality solution. The Core Problem: Reactive Data Quality Imagine a scenario where a critical e-commerce metric suddenly dips, or a BI dashboard shows inconsistent numbers. Often, the root cause lies in upstream data quality issues – null values, duplicate entries, or unexpected data distributions. Catching these issues proactively and at scale is where most systems fall short. My goal with Data Quality Guard Pro was t...