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Once we encounter a brand new technology — say, LLM applications — a few of us are likely to jump right in, sleeves rolled up, impatient to begin tinkering. Others prefer a more cautious approach: reading a number of relevant research papers, or browsing through a bunch of blog posts, with the goal of understanding the context wherein these tools have emerged.
The articles we selected for you this week include a decidedly “why not each?” attitude towards AI agents, LLMs, and their day-to-day use cases. They highlight the importance of understanding complex systems from the bottom up, but additionally insist on mixing abstract theory with actionable and pragmatic insights. If a hybrid learning strategy sounds promising to you, read on — we predict you’ll find it rewarding.
Agentic AI from First Principles: Reflection
For a solid understanding of agentic AI, Mariya Mansurova prescribes an intensive 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 comes with robust feedback loops.
It Doesn’t Must Be a Chatbot
For Janna Lipenkova, successful AI integrations differ from failed ones in a single key way: they’re shaped by a concrete understanding of the worth AI solutions can realistically add.
What “Pondering” and “Reasoning” Really Mean in AI and LLMs
For an incisive have a look at how LLMs work — and why it’s essential to know their limitations so as 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 largest splash in our community up to now 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 Really helpful Reads
Listed below are a number of more standout stories we desired to placed 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 Hassle 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 Recent Authors
We hope you’re taking the time to explore the wonderful work from the most recent 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 the way it may well be solid into the formalism of state vectors.
Whether you’re an existing writer or a brand new one, we’d love to contemplate your next article — so should you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
