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A good month on TDS is one in which we get to share a wide range of incisive articles with our readers, covering cutting-edge tools, foundational data and ML skills, thoughtful takes on the state of AI, and career (and other) insights from our top authors.
By this measure, October was one for the books. This week, we’re thrilled to celebrate our most-read and farthest-reaching stories of the past month: they tackled Python’s most recent release, the latest in both context and prompt engineering, and a lot more. Let’s dive right in.
Python 3.14 and the End of the GIL
What’s new in Python-world? A lot, it seems. Thomas Reid‘s comprehensive explainer is here to help us unpack the “exciting enhancements” included in the major release unveiled a few weeks ago, focusing in particular on the promise of a free-threaded (or GIL-free) Python.
Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI
The limitations of retrieval-augmented generation are well known by now. Steve Hedden looks at what lies ahead for LLM-boosting workflows, and at RAG’s evolution into “governed, context-aware systems.”
Prompt Engineering for Time-Series Analysis with Large Language Models
Can a well-designed prompt be the game-changer for one of data scientists’ most ubiquitous tasks? Sara Nobrega outlines several powerful strategies for leveraging LLMs to improve time-series analysis results.
Other October Highlights
Don’t miss our other top reads from the past month, tackling statistics, agentic AI, and robotics, among other topics.
- Implementing the Fourier Transform Numerically in Python: A Step-by-Step Guide, by Junior Jumbong
- How to Build An AI Agent with Function Calling and GPT-5, by Ayoola Olafenwa
- How to Build a Powerful Deep Research System, by Eivind Kjosbakken
- How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k, by Maria Mouschoutzi
- A Beginner’s Guide to Robotics with Python, by Mauro Di Pietro
- How I Used ChatGPT to Land My Next Data Science Role, by Yu Dong
- Data Visualization Explained (Part 4): A Review of Python Essentials, by Murtaza Ali
Meet Our Returning Authors
Just as we’re thrilled to welcome new contributors to TDS, we’re always happy to see familiar voices appearing in our publication after a hiatus. Case in point:
- Barr Moses published a new article full of sharp insights on the macro trends shaping the future of AI.
- Jingyi Jessica Li (with coauthor Pan Liu) offered an accessible writeup of their latest research at the intersection of statistics, bioinformatics, and medical data science.
- Robert Constable presented an accessible, hands-on introduction to building a geospatial lakehouse with open source tools and Databricks.
Whether you’re an existing author or a new one, we’d love to consider your next article — so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
