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When we encounter a new technology — say, LLM applications — some of us tend to jump right in, sleeves rolled up, impatient to start tinkering. Others prefer a more cautious approach: reading a few relevant research papers, or browsing through a bunch of blog posts, with the goal of understanding the context in which these tools have emerged.
The articles we chose for you this week come with a decidedly “why not both?” attitude towards AI agents, LLMs, and their day-to-day use cases. They highlight the importance of understanding complex systems from the ground up, but also insist on blending abstract theory with actionable and pragmatic insights. If a hybrid learning strategy sounds promising to you, read on — we think you’ll find it rewarding.
Agentic AI from First Principles: Reflection
For a solid understanding of agentic AI, Mariya Mansurova prescribes a thorough exploration of their key components and design patterns. Her accessible deep dive zooms in on reflection, moving from existing frameworks to a from-scratch implementation of a text-to-SQL workflow that incorporates robust feedback loops.
It Doesn’t Need to Be a Chatbot
For Janna Lipenkova, successful AI integrations differ from failed ones in one key way: they are shaped by a concrete understanding of the value AI solutions can realistically add.
What “Thinking” and “Reasoning” Really Mean in AI and LLMs
For an incisive look at how LLMs work — and why it’s important to understand their limitations in order to optimize their use — don’t miss Maria Mouschoutzi’s latest explainer.
This Week’s Most-Read Stories
Don’t miss the articles that made the biggest splash in our community in the past week.
Deep Reinforcement Learning: 0 to 100, by Vedant Jumle
Using Claude Skills with Neo4j, by Tomaz Bratanic
The Power of Framework Dimensions: What Data Scientists Should Know, by Chinmay Kakatkar
Other Recommended Reads
Here are a few more standout stories we wanted to put on your radar.
- From Classical Models to AI: Forecasting Humidity for Energy and Water Efficiency in Data Centers, by Theophano Mitsa
- Bringing Vision-Language Intelligence to RAG with ColPali, by Julian Yip
- Why Should We Bother with Quantum Computing in ML?, by Erika G. Gonçalves
- Scaling Recommender Transformers to a Billion Parameters, by Kirill Кhrylchenko
- Data Visualization Explained (Part 4): A Review of Python Essentials, by Murtaza Ali
Meet Our New Authors
We hope you take the time to explore the excellent work from the latest cohort of TDS contributors:
- Ibrahim Salami has kicked things off with a stellar, beginner-friendly series of NumPy tutorials.
- Dmitry Lesnik shared an algorithm-focused explainer on propositional logic and how it can be cast into the formalism of state vectors.
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?
