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Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called chain-of-thought (CoT), where the model iteratively generates intermediate

Table of Content Introduction: Why I Wrote This The Evolution of Tool Integration with LLMs What Is Model Context Protocol (MCP), Really? Wait, MCP sounds like RAG… but is it? In an MCP-based setup In a traditional RAG system Traditional RAG Implementation MCP Implementation Quick recap! Core Capabilities of an MCP Server Real-World Example: Claude

Have you ever wanted to pause an automated workflow to wait for a human decision? Maybe you need approval before provisioning cloud resources, promoting a machine learning model to production, or charging a customer’s credit card. In many data science and machine learning workflows, automation gets you 90% of the way — but that critical last

Not another explanation You’ve probably heard of data leakage, and you might know both flavours well: Target Variable and Train-Test Split. But will you spot the holes in my faulty logic, or the oversights in my optimistic code? Let’s find out.  I’ve seen many articles on Data Leakage, and I thought they were are all

Flick through any popular Macroeconomic textbook of the last 50 years and you will quickly land on a page that mentions the Phillips curve. And then another page, and then another… For example, the Macroeconomics textbook, creatively titled “Macroeconomics” by C. Jones and part of Cambridge university’s BA Economics curriculum, references the “Phillips curve” a

When I built a GPT-powered fashion assistant, I expected runway looks—not memory loss, hallucinations, or semantic déjà vu. But what unfolded became a lesson in how prompting really works—and why LLMs are more like wild animals than tools. This article builds on my previous article on TDS, where I introduced Glitter as a proof-of-concept GPT

Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I recently worked on a project analyzing the energy consumption of training AI models, using data

I really enjoyed reading this paper, not because I’ve met some of the authors before, but because it felt necessary. Most of the papers I’ve written about so far have made waves in the broader ML community, which is great. This one, though, is unapologetically African (i.e. it solves a very African problem), and I