Follow along @taupirho's latest tutorial to learn how to benchmark the impact of fewer, larger files across three SQL workloads. towardsdatascience.com/i-com…

In an insightful analysis, Yu Dong revisits a timely topic: the shape and extent of AI's impact on data science roles. towardsdatascience.com/ai-ma…

"This has never sat right with me: a bounded yes/no or routing decision was often passed through the same autoregressive decoder used to generate a paragraph." Lambert Leong @leonglambert digs into the architectural constraints that are steering the field towards "decision" models like Jev. towardsdatascience.com/when-…

Getting started in designing safe and trustworthy agentic systems? @Thuwarakesh presents a comprehensive guide to the architectural guardrails you need to be fluent in. towardsdatascience.com/how-t…

We're thrilled to welcome Vasileios Vonikakis to TDS! You should explore his debut article, which unpacks the process of building fair evaluation sets when dealing with imbalanced data. towardsdatascience.com/build…

In his recent guide, Conor O'Sullivan shared practical tips for using AI while working on a PhD thesis — but these insights can be equally applied to many other complex, long-term projects involving massive amounts of data and references. towardsdatascience.com/4-way…

In a new deep dive, Tigran Hayrapetyan introduces Guided Merge Sort, an optimized sorting approach that chooses the best options among ordinary and multi-way merge-sort algorithms. towardsdatascience.com/guide…

What if you could transform an open-source LLM into your own, Jev-like single-pass text classifier? Anubhab Banerjee outlines an approach that can accomplish this result. towardsdatascience.com/how-t…

An HR assistant sandbox that wraps a RAG agent in the checks a production system needs: injection filters, ACL pre-filtering, and human approval on high-risk actions. By Partha Sarkar towardsdatascience.com/shipa…

"At some point, with no new data and nothing visibly different about the setup, the model's performance on the unseen questions jumped from barely better than random to nearly perfect." Utkarsh Mangal unpacks the phenomenon of grokking in machine learning. towardsdatascience.com/the-a…

Learn how you can detect hidden shifts in feature relationships to avoid data drift — @BenjaminNweke11 leverages adversarial validation and scikit-learn in his latest tutorial. towardsdatascience.com/how-t…

Can you leverage the power of Jev in the context of scalable knowledge graphs? Partha Sarkar explains what that looks like in practice in his excellent hands-on guide. towardsdatascience.com/graph…

"Cursor, Claude Code and Copilot all sit in front of the same gap, whatever each of them indexes, because the information is not in any repository they can see." Yonatan Sason zooms in on a structural issue affecting AI-assisted software development. towardsdatascience.com/good-…

AI-generated content has entered training data — and at a massive rate. Abdullahi Dattijo presents his findings from testing three approaches to spot it. towardsdatascience.com/ai-sl…

How do paragraphs "work" as a meaningful unit of content in the context of LLMs? Shuyang Xiang offers an accessible explainer on an often-overlooked topic. towardsdatascience.com/your-…

Not so long ago, building an internal tool at work would take weeks, if not months. @EivindKjos shows how you can cut down the timeline to mere hours with the help of Claude Code. towardsdatascience.com/build…

From stale, contradictory documents to a table divided across a page boundary, Sara Nobrega walks us through some of the adversarial test tests she recommends you run before giving users access to your RAG pipeline. towardsdatascience.com/break…

In a new hands-on guide, Spyros Georgopoulos walks us through five essential scikit-learn defaults you should remember to vet when reviewing AI-generated code. towardsdatascience.com/your-…