any time in the data engineering world, you’ve likely encountered this debate at least once. Maybe twice. Ok, probably a dozen times😉 “Should we process our data in batches or in real-time?” And if you’re anything like me, you’ve noticed that the answer usually starts with: “Well, it depends…” Which is true. It does depend. But “it …
Batch or Stream? The Eternal Data Processing Dilemma Read More »
# Introduction JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: braces, quotes, commas, and repeated field names on every row. TOON, short for Token-Oriented Object Notation, is a newer format …
Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines Read More »
(not that long ago) when being a data scientist meant living in a notebook, tweaking hyperparameters as if your life depended on it, and in a lot of cases, the whole project did, indeed, depend on it. Do you remember those overnight grid searches? Or building feature engineering pipelines that felt more like art than …
# Introduction Anyone who has spent a fair amount of time doing data science may sooner or later learn something: the golden rule of downstream machine learning modeling, known as garbage in, garbage out (GIGO). For example, feeding a linear regression model with highly collinear data, or running ANOVA tests on heteroscedastic variances, is the …
Across 64 English authorities and six 2026 scenarios, even the strongest scenario shock was only 13% of the median uncertainty band. In plain English: the model’s assumptions moved the result less than historical forecast error did. The most aggressive challenger surge I could parameterise sits inside the noise the model has produced in past elections. …
# Introduction Every organization loves to call itself “data-driven.” It’s become the gold standard of credibility, the thing you say to shut down dissent in a meeting. But here’s something worth sitting with for a second: the phrase “according to data analytics” can come from two very different places. One is genuine curiosity. The other …
Baptists and Bootleggers: The Hidden Coalition Behind ‘Data-Driven’ Decisions Read More »
AI Agents When building an AI agent, the design choice matters. A single agent may be enough for straightforward tasks, while more complex workflows may need multiple specialised agents working together, with each one responsible for a specific part of the process, such as retrieval, writing, verification, coding, testing or review. This post explains the …
Single Agent vs Multi-Agent: When to Build a Multi-Agent System Read More »
Image by Editor # Introduction Voice-enabled applications are everywhere, from virtual assistants to customer service chatbots. But for developers, building natural-sounding speech into apps has often meant relying on expensive cloud APIs or dealing with robotic, unnatural voices. Mistral AI aims to change that with Voxtral TTS. It is a powerful, open-weight text-to-speech (TTS) model …
Authors: Ahsaas Bajaj and Benjamin S Knight ? We ran 134,400 simulations grounded in real production ML models to find out. The answer depends on what you’re optimizing for, and on a single diagnostic you can compute before fitting a model. If you’ve ever trained a linear model in scikit-learn, you’ve faced this question: RidgeCV, …
Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations Read More »
Image by Editor # Introduction A harsh truth to begin with: textbook data science usually becomes a lie in the real world. Concepts and techniques are taught on finely curated, beautifully bell-curved data variables, but as soon as we venture into the wild of real projects, we are hit with lots of outliers, unduly skewed …
The “Robust” Data Scientist: Winning with Messy Data and Pingouin Read More »
