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From afar, new LLMs and the applications they power seem shiny, or even magical. The unrelenting pace of product launches and media coverage adds to their aura, and generates extreme levels of FOMO among ML practitioners and business executives alike. The overall effect? The feeling that AI is inevitable, and its value unquestionable.
The articles we’ve selected for you this week don’t ignore the potential usefulness of all this innovation, but approach it with a healthy dose of skepticism. They examine the walls we run into when we don’t understand the tools we’re so eager to adopt or the tradeoffs we’ve accepted along the way. If you’re interested in insightful takes on AI’s current blockers and limitations — and the ways we might be able to overcome them — read on.
Can We Save the AI Economy?
“Why is this AI mania so powerful at the current moment, with seemingly no regard for the customer’s actual pain points?” Stephanie Kirmer presents a thoughtful deep dive on the tensions and conflicting interests shaping AI product development. She points to the (many, many) ways business decision-making currently appears off-balance, and suggests that a productive way out would require a change of perspective — into a “thoughtful, careful, and conservative” approach to integrating AI into user-facing products.
Human Won’t Replace Python
Is traditional programming on its way out? The vibe-coding conversation of the past few months made many believe that’s the case. In a thought-provoking piece, Elisha Rosenberg and Eitan Wagner say “now so fast!” as they unpack the limits of natural-language-based coding.
Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI
Steve Hedden’s latest article shows how tools and workflows we considered cutting-edge just a couple of years ago — in this case, RAG — can become stale unless they evolve and adapt with the times.
This Week’s Most-Read Stories
Don’t miss the articles that made the biggest splash in our community in the past week.
Prompt Engineering for Time-Series Analysis with Large Language Models, by Sara Nobrega
A Beginner’s Guide to Robotics with Python, by Mauro Di Pietro
Stop Feeling Lost : How to Master ML System Design, by Egor Howell
Other Recommended Reads
Agent-building, project frameworks for data scientists, the inner workings of vision LLMs, and more: here are several additional stories we wanted to put on your radar.
- Things I Learned by Participating in GenAI Hackathons Over the Past 6 Months, by Parul Pandey
- How to Build An AI Agent with Function Calling and GPT-5, by Ayoola Olafenwa
- Conceptual Frameworks for Data Science Projects, by Chinmay Kakatkar
- How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k, by Maria Mouschoutzi
- How to Use Frontier Vision LLMs: Qwen3-VL, by Eivind Kjosbakken
Meet Our New Authors
We hope you take the time to explore the excellent work from the latest cohort of TDS contributors:
- Kirill Кhrylchenko introduces us to transformer-based recommender systems and explains how they can improve on traditional approaches.
- Yassin Zehar walks us through a project-management-focused workflow that leverages machine learning to predict delays.
- Marco Letta zooms in on hidden data leakage and how to preemptively avoid some of its most nefarious effects.
We love publishing articles from new authors, 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?
