Issue #312

April 2, 2026

Announcements

We’re excited to announce the official relaunch of the Data For Science website!

At Data4Sci, our goal has always been to bridge the gap between complex data and actionable intelligence. Our revamped site makes it easier than ever to explore how we help teams build reliable, production-ready AI—from RAG and agentic workflows to comprehensive LLM strategy.

Check out the new experience here: 👉 https://data4sci.com/

Whether you're looking for expert consulting, technical training, or our latest deep-dives into AI, we’ve built this for you.

A. Gullí’s "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems" feels like a timely guide for data scientists and machine learning engineers who are ready to move past the hype around AI agents and focus on how these systems are actually built. What makes the book stand out is its practical, pattern-based approach: instead of treating agents like magic, Gullí breaks them into reusable design ideas that help readers think more clearly about architecture, workflows, and implementation. That alone makes it more valuable than many AI books that are heavy on buzzwords and light on substance.

One of the book’s strongest qualities is its hands-on mindset. By working through recognizable frameworks and concrete design patterns, it gives technical readers a clearer path from experimentation to real system design. For ML engineers, that means a stronger grasp of modularity and maintainability; for data scientists, it offers a useful bridge between model knowledge and application building. The book is at its best when it helps readers see agentic systems not as mysterious novelties, but as engineering problems that can be approached systematically.

Its weaknesses are relatively minor but worth noting. Because it leans on current frameworks and tools, some parts may age quickly in such a fast-moving field, and readers looking for a deeper dive into evaluation, benchmarking, or production-scale operations may find it less comprehensive on those fronts. Still, Agentic Design Patterns sounds like the kind of book that can sharpen how technical practitioners think about intelligent systems—and for many readers, that will be reason enough to keep turning the pages.

Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems

1. Closing the knowledge gap with agent skills [developers.googleblog.com]
2. Anatomy of the .claude/ Folder [blog.dailydoseofds.com]
3. TurboQuant: Redefining AI efficiency with extreme compression [research.google]
4. Welcome to FastMCP [gofastmcp.com]
5. Designing AI for Disruptive Science [asimov.press]
6. What Young Workers Are Doing to AI-Proof Themselves [wsj.com]
7. Claude Code For Academics [github.com]
8. Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation [research.nvidia.com]

• A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations (Z. Zada, A. Goldstein, S. Michelmann, E. Simony, A. Price, L. Hasenfratz, E. Barham, A. Zadbood, W. Doyle, D. Friedman, P. Dugan, L. Melloni, S. Devore, A. Flinker, O. Devinsky, S. A. Nastase, U. Hasson)
• Embracing Bayesian Methods in Clinical Trials: FDA’s Long-Awaited Draft Guidance (J. J. Lee, F. E. Harrell Jr, L. M. LaVange, D. J. Spiegelhalter)
• Association between COVID-19 vaccination and sudden death in apparently healthy younger individuals: A population-based case-control study (H. Abdel-Qadir, H. A. Bhatt, S. Swayze, M. Paterson, D. T. Ko, D. N. Juurlink, J. C. Kwong)
• Validating urban scaling laws through mobile phone data: analysis of Brazil’s largest cities (R. S. Alencar, F. L. Ribeiro, H. Samaniego, R. Menezes, A. G. Evsukoff)
• MIRAGE: The Illusion of Visual Understanding (M. Asadi, J. W. O'Sullivan, F. Cao, T. Nedaee, K. Fardi, F.-F. Li, E. Adeli, E. Ashley)
• An Alternative Trajectory for Generative AI (M. Belova, Y. Kansal, Y. Liang, J. Xiao, N. K. Jha)
• Mathematical methods and human thought in the age of AI (T. Klowden, T. Tao)
• Could a Large Language Model be Conscious? (D. J. Chalmers)

Are LLMs a Dead End?

April 2, 2026

Announcements

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