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AI-related business decisions are common: whether or not to deploy a new model, adopt a new tool, or launch a new LLM product, to name a few. But what about AI-powered business decisions? This is where things get interesting!
This week, our highlights focus on the ever-expanding range of situations where AI can help data and business teams, as well as individual practitioners, make smarter, more informed, and better researched decisions. Let’s dive in.
Building Research Agents for Tech Insights
Using the example of tech news, Ida Silfverskiöld demonstrates how you can harness the power of research agents to aggregate millions of texts, filter data based on a persona, and find patterns and themes you can act on — a promising workflow for which professionals in numerous fields can find use cases.
How to Build an AI Budget-Planning Optimizer for Your 2026 CAPEX Review: LangGraph, FastAPI, and n8n
Learn how you can turn budget requests into optimized CAPEX (capital expenditure) portfolios: Samir Saci’s tutorial provides a detailed walkthrough that leverages LangGraph, FastAPI, and n8n.
Building a Unified Intent Recognition Engine
Shruti Tiwari and Vadiraj Kulkarni, from Dell Technologies, present the Unified Intent Recognition Engine (UIRE) — a focused and actionable framework for enterprise AI systems that can simplify and scale customer-intent classification (and break down silos along the way).
This Week’s Most-Read Stories
Don’t miss the articles our community has been buzzing about in the past week:
Implementing the Coffee Machine in Python, by Mahnoor Javed
The End-to-End Data Scientist’s Prompt Playbook, by Sara Nobrega
The Hungarian Algorithm and Its Applications in Computer Vision, by Vyacheslav Efimov
Other Recommended Reads
Graph fraud detection, AI in the classroom, tool masking, and more: here are a few other standout articles we’ve published recently.
- Is Your Training Data Representative? A Guide to Checking with PSI in Python, by Junior Jumbong
- My Experiments with NotebookLM for Teaching, by Parul Pandey
- No Peeking Ahead: Time-Aware Graph Fraud Detection, by Erika G. Gonçalves
- Tool Masking: The Layer MCP Forgot, by Frank Wittkampf
- When A Difference Actually Makes A Difference, by Mena Wang
Meet Our New Authors
Explore excellent work from our recently added contributors:
- Salman Toor is an Associate Professor at Uppsala University as well as a CTO at an ML startup; he devotes his new article to the security risks inherent to federated learning.
- Paul Fröhling specializes in computer vision, but for his debut TDS article he turned to math and wrote a compelling deep dive on space-filling curves.
- Sudheer Singamsetty is a seasoned data-management expert who recently published with us on the emerging field of context engineering.
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?
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