AI-related business decisions are common. Give it a few names whether you deploy a new model, adopt a new tool, launch a new LLM product or not. But what about ai-Power Business decisions? This is where things get interesting!
This week, our highlights will focus on the wider range of situations where AI can help data, business teams and individual practitioners make smarter, more informed and better research decisions. Let's dive in.
Building research agents for technical insights
Using the Tech News example, Ida Silfverskiöld demonstrates how to harness the power of research agents to aggregate millions of texts, use filter data based on personas, and find patterns and themes that can act.
How to build an AI budget planning optimizer for your 2026 CAPEX review: Langgraph, Fastapi, and N8N
Learn how to turn your budget requests into an optimized Capex portfolio. SamirSaci tutorials offer detailed walkthroughs that leverage Langgraph, Fastapi, and N8N.
Building a unified intent recognition engine
Shruti Tiwari and Vadiraj Kulkarni from Dell Technologies introduce a Unified Intention Recognition Engine (UIRE).
Most Read Stories of the Week
Don't miss out on articles that have been making our community a buzz over the past week:
Implementing coffee machines in Python by Mahnoor Javed
A quick end-to-end data scientist playbook by Sarah Nobrega
Applications to Hungarian algorithms and computer vision, vyacheslav efimov
Other recommended readings
Graph fraud detection, classroom AI, tool masking, and more: several other standout articles that I recently published.
- Is your training data representative? A guide to checking PSI in Python by Junior Jumbong
- My experiment with Notebooklm for education by Parul Pandey
- Not looking ahead: Erika G. Gonçalves' time-held graph fraud detection
- Tool Masking: Layer MCP by Frank Witcamp forgot
- When differences actually make a difference, by Mena Wang
Meet our new authors
Explore outstanding works from recently added contributors.
- Salman Toor is an associate professor at Uppsala University and CTO of ML startups. He devotes new articles to the security risks inherent in federal learning.
- Paul Fröhling specializes in computer vision, but for his debut TDS article, he turned to mathematics and wrote an intriguing deep dive on the spatial filling curve.
- Sudheer Singamsetty is a veteran data management expert who recently published alongside us in the emerging field of context engineering.
We love publishing articles from new authors, so if you recently wrote an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it?
