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AI’s footprint is growing rapidly across roles and industries. As generative-AI tools move from the margins into core workflows, practitioners increasingly ask themselves a deceptively easy query: what does being good at one’s job mean as of late?
There’s nobody answer, in fact, however the articles we’ve chosen for you this week point to a key insight: it is perhaps time to redefine what “following best practices” mean, and to focus our understanding of performance around skills by which humans proceed to carry an edge over their LLM-based assistants.
Before we jump right in, a fast reminder: the TDS Reader Survey is now open, and we’re desirous to hear your insights. It’s going to only take a number of minutes of your time — thanks prematurely for weighing in along with your feedback!
The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons
It’s been unattainable to miss the thrill across the model context protocol in recent months. Hailey Quach highlights the risks that this open-source framework poses, and the mitigating steps data and ML professionals should take to make sure its integration doesn’t develop into a security nightmare.
Reducing Time to Value for Data Science Projects: Part 4
Kristopher McGlinchey stresses that nothing is more essential for data scientists than “being a very good software developer”—even with the rise of coding agents.
Things I Wish I Had Known Before Starting ML
“when you try to maintain up with , you’ll find yourself maintaining with .” Pascal Janetzky offers insights on what it takes to attain success in a highly competitive field.
This Week’s Most-Read Stories
Atone for the articles our community has been buzzing about in recent days:
Context Engineering — A Comprehensive Hands-On Tutorial with DSPy, by Avishek Biswas
Agentic AI: On Evaluations, by Ida Silfverskiöld
Generating Structured Outputs from LLMs, by Ibrahim Habib
Other Really useful Reads
Inquisitive about noisy data, topic modeling, and the Agents SDK, amongst other timely themes? Don’t miss a few of our other standout articles from the past few days:
- The Machine, the Expert, and the Common Folks, by Lars Nørtoft Reiter
- Superb-Tune Your Topic Modeling Workflow with BERTopic, by Tiffany Chen
- Does the Code Work or Not?, by Marina Tosic
- Hands-On with Agents SDK: Multi-Agent Collaboration, by Iqbal Rahmadhan
- Estimating from No Data: Deriving a Continuous Rating from Categories, by Elod Pal Csirmaz
Meet Our Recent Authors
Explore top-notch work from a few of our recently added contributors:
- Aimira Baitieva is an experienced research engineer, whose work currently focuses on anomaly detection and object-detection problems.
- Daniel Gärber joins TDS with multidisciplinary expertise across data science and engineering, and recently wrote about winning the Mostly AI Prize.
- Carlos Redondo is an ML/AI engineer who’s spent the past few years working at several startups.
We love publishing articles from latest authors, so when you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
