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As we wrap up the first month of 2026, it might be a tad too early to detect major changes or emerging themes. One thing is clear, though: our readers are keen to stay on top of industry trends and cutting-edge tools.
Fortunately (and as always), TDS contributors have started the year on a strong note, delivering timely and insightful reads on these — and many other — topics. This week, we’re highlighting our most-read and -shared articles from January, covering LLM context, Claude Code, and the future of giant data platforms, to name a few standout examples.
The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling
“How big can a data company really grow?” Hugo Lu begins his thought-provoking deep dive with a fundamental questioning of the current business model of giant platforms like Databricks and Snowflake. He goes on to unpack the different factors at play, and to offer some bold predictions for the coming year.
How LLMs Handle Infinite Context With Finite Memory
Can you truly do (much) more with (much) less? Moulik Gupta offers a thorough and accessible explainer on Infini-attention.
How to Maximize Claude Code Effectiveness
Eivind Kjosbakken’s handy guide outlines key optimization techniques when using the popular agentic-coding tool.
Other January Highlights
Here are a few more of last month’s most popular stories, with insights on fused kernels, context engineering, and federated learning, among other topics:
Beyond Prompting: The Power of Context Engineering, by Mariya Mansurova
Using ACE to create self-improving LLM workflows and structured playbooks.
Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels, by Ryan Pégoud
Why your final LLM layer is OOMing and how to fix it with a custom Triton kernel.
Why Human-Centered Data Analytics Matters More Than Ever, by Rashi Desai
From optimizing metrics to designing meaning: putting people back into data-driven decisions.
Retrieval for Time-Series: How Looking Back Improves Forecasts, by Sara Nobrega
An introduction to retrieval in time-series forecasting.
Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It), by Samir Saci
My take after 10 years in Supply Chain on why this can be an excellent playground for data scientists who want to see their skills valued.
Federated Learning, Part 1: The Basics of Training Models Where the Data Lives, by Parul Pandey
Understanding the foundations of federated learning.
Authors in the Spotlight
We hope you take the time to read our recent author Q&A, and explore top-notch work from our newest contributors:
- Diana Schneider zoomed in on evaluation methods for multi-step LLM-generated content, like customer journeys.
- Kaixuan Chen and Bo Ma shared their work on building a neural machine translation system for Dongxiang, a low-resource language.
- Pushpak Bhoge devoted his debut article to benchmarking the performance of Meta’s SAM 3 to specialist models.
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!