# Introduction Everyone is building agents. Far fewer people can explain, precisely, why their agent loops forever, ignores a tool it was given, or confidently reports success on a task it never finished. The gap between shipping an agent and understanding one is where these five resources live, and every one of them is completely …
a marketing mix model to a senior director. The slide showed two beta coefficients side by side: Linear TV at +2.4, Digital TV at +1.8. He nodded, satisfied. Then he asked the question I was dreading. “Let’s assume we run this with last week’s refreshed data. Same channels, same model, same everything, but one extra …
Why Your Betas Explode: The Hidden Geometry of Multicollinearity Read More »
# Introduction An agent that calls a cloud API for every decision is renting its intelligence. It needs a key, it racks up a bill per token, and every request leaves your machine before you get an answer back. An agent built to run locally skips all of that. No API key, no per-call cost …
7 Python Frameworks for Orchestrating Local AI Agents Read More »
In my latest post on structured outputs, the three main approaches for getting machine-readable responses from an LLM. Those are JSON Mode, Function Calling, and OpenAI’s Structured Outputs. If you haven’t read that post yet, it’s worth a quick read before this one, since we’ll be building directly on top of it. So, today, we’ll …
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs Read More »
# Introduction SQL is still one of the most important skills for data analysts, data scientists, business intelligence analysts, and analytics engineers. But learning SQL syntax is only the first step. To stand out, you need to show that you can use SQL to solve real business problems. That is where portfolio projects help. A …
5 Real-World SQL Projects to Build Your Data Portfolio Read More »
I’ve worked on, conversation state tends to grow quickly over time. It’s common to resend large portions of the history on each turn—including older tool outputs, repeated RAG retrievals, and context that’s no longer relevant. As this accumulates, prompts can become significantly larger, which may increase inference cost and latency, and in some cases affect …
# Inroduction This article is part of my noob series where we write about the questions people Google most but may not understand well because of complex math and everything. So, if you are here, you might have heard fine-tuning somewhere in the context of large language models (LLMs) especially. This concept already existed in …
Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills) Read More »
latest wave of articles, opinion pieces, and forecasts from consultants and AI vendors, all confidently predicting the imminent takeover of human work by agentic AI, I recently found myself thinking about my first year at Imperial College London, back in 2009. Our lecturer for the introductory programming course would often tell us to sketch out …
# Introduction Every analysis starts the same way: you load a dataset and try to figure out what’s actually in it. How many rows? Which columns are numeric? How much is missing? Is anything wildly skewed? Most of us answer those questions by copy-pasting the same df.describe(), df.isna().sum(), and df.groupby(…).agg(…) snippets we’ve typed a thousand …
7 Steps to Automating Descriptive Statistics with Python Read More »
Image by Pexels Coming out of the pandemic, statisticians have run into a slew of geo-political complications further augmenting the inability to forecast business variables with accuracy. Did Ukraine drive retail prices higher in 2022 or was it the quantitative easing in 2021 that was the culprit? Some models say one thing and some say …
