New research from Sandia National Laboratories suggests that brain-inspired neuromorphic computers are just as adept at solving complex mathematical equations as they are at speeding up neural networks and could eventually pave the way to ultra-efficient supercomputers. Running on around 20 watts, the human brain is able to process vast quantities of sensory information from
TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More | Towards Data Science
Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more.
Yes, it’s 2026 — and we’re already focused on an eventful year of growth and learning here at TDS. We’ve also published many stellar articles last month, including at the height of the holiday season, and we wouldn’t want you to miss out on any of them.
This week, we’re devoting the Variable to one last 2025 hurrah, highlighting some of our most popular stories from December. Make no mistake, however: they cover timely and actionable topics in machine learning, data science, and AI, and will remain relevant for weeks and months to come.
GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems
When “vanilla” RAG systems no longer cut it, you may want to explore the power of GraphRAG — and Partha Sarkar‘s detailed guide is a great starting point for anyone interested in tinkering with this powerful approach, which leverages hybrid pipelines and can lead to lower costs.
Six Lessons Learned Building RAG Systems in Production
For additional hands-on RAG insights, we highly recommend Sabrine Bendimerad’s roundup of best practices, covering data quality, evaluation, and more.
How to Use Simple Data Contracts in Python for Data Scientists
Quick and focused, Eirik Berge presents a guide to using open-source library Pandera when you aim to define schemas as class objects.
Other December Highlights
From learning algorithms with Excel to improving Pandas’ performance, here are a few more of last month’s most-read and -shared stories.
The Machine Learning and Deep Learning “Advent Calendar” Series: The Blueprint, by Angela Shi
How Agent Handoffs Work in Multi-Agent Systems, by Kenneth Leung
Reading Research Papers in the Age of LLMs, by Parul Pandey
7 Pandas Performance Tricks Every Data Scientist Should Know, by Benjamin Nweke
What Happens When You Build an LLM Using Only 1s and 0s, by Moulik Gupta
Meet Our New Authors
We hope you take the time to explore excellent work from TDS contributors who recently joined our community:
- Jasper Schroeder shared helpful takeaways from the Advent of Code programming challenge he recently completed.
- Morris Stallmann (with coauthor Sebastian Humberg) offered a comprehensive, pragmatic primer on data drift (and how to detect it in a timely manner).
- Alon Lanyado focused on a different challenge data scientists and ML practitioners often face: covariance shift.
Do your New Year’s resolutions include publishing on TDS and joining our Author Payment Program? Now’s the time to send along your latest draft!
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