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.
Things move fast in the world of data science and AI, and that includes the programming know-how today’s roles require. Sure, some Python and SQL tricks remain evergreen. But to stand out in a crowded field, you should stay up-to-date — and we’re here to support you on your learning journey.
To kick off back-to-school season in earnest, we’ve gathered some top-notch, coding-focused tutorials we’ve published recently. Regardless of your current level, you’ll find something here to inspire you to start tinkering.
How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker
Object-detection projects can be frustratingly time-consuming. Yagmur Gulec introduces us to open-source tool Label Studio, and walks us through the necessary steps for building a much more streamlined process of importing pre-annotated visual data.
A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues
We may think of queuing systems as something that simply hums along in the background. Clara Chong invites us to make smarter decisions for cumulative efficiency — especially in the era of complex LLM-based tasks.
Implementing the Hangman Game in Python
For Python beginners, Mahnoor Javed offers an accessible and engaging primer on coding basics — think variables, loops, and conditions — at the end of which you will have created a functional (and playable) Hangman program.
This Week’s Most-Read Stories
The articles our community has been buzzing about in recent days cover cutting-edge LLM tools and career advice:
Everything I Studied to Become a Machine Learning Engineer (No CS Background), by Egor Howell
Using Google’s LangExtract and Gemma for Structured Data Extraction, by Kenneth Leung
Google’s URL Context Grounding: Another Nail in RAG’s Coffin?, by Thomas Reid
Other Recommended Reads
From GenAI’s role in scientific research to prompt optimization, here are a few more recent must-reads we wanted to highlight:
- Why Science Must Embrace Co-Creation with Generative AI to Break Current Research Barriers, by Ugo Pradère
- 3 Greedy Algorithms for Decision Trees, Explained with Examples, by Kuriko Iwai
- Toward Digital Well-Being: Using Generative AI to Detect and Mitigate Bias in Social Networks, by Celia Banks
- Air for Tomorrow: Why Openness in Air Quality Research and Implementation Matters for Global Equity, by Prithviraj Pramanik
- Systematic LLM Prompt Engineering Using DSPy Optimization, by Robert Martin-Short
Meet Our New Authors
Explore excellent work from some of our recently added contributors:
- Sathya Krishnan Suresh, a Singapore-based AI scientist, published a comprehensive guide to Transformers’ positional embeddings.
- Ahmad Talal Riaz, who recently wrote on the fundamentals of LLM monitoring and observability, joins us with a versatile skill set, honed across several AI/ML research and engineering roles.
- Noah Swan is currently pursuing a graduate statistics degree at the University of Chicago; his debut article aims to demystify Bayesian hyperparameter optimization.
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?
