#ToWardSDataScience

Most engineers see quantization as shrinking vectors. TurboQuant asks a harder question: can you shrink them without breaking their geometry?

How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python.

What a recent study on ChatGPT, Python, R, and Stata tells us about AI-assisted coding for causal inference

A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API

How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment.

How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents

Good models don't save bad architecture, and most teams learn that the hard way.

Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure

A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since.

Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting.

One of the most important concepts in DAX is lineage. It’s about the information on where something comes from. Let’s see what it is and how we can manipulate it.

A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent

Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking,…

Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted.