Who Pays for AI Data Centers?
The fight is usually framed as whether data centers get built. The harder question underneath it is "who pays and who decides?" The costs are intertwined.
KDnuggets14 min read
KDnuggets
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5 Best Practices for Building Robust Python AI Libraries
This article covers building robust Python AI libraries specifically, Python AI SDK best practices, and what separates a production-ready AI package from one that only survives in its own demo.
KDnuggets15 min readMeta Muse Explained: What It Is, How It Works, and What It Can Do
On September 8, 2026, Meta launched Muse, a personal AI agent designed to do more than just answer questions. Muse can browse websites, connect to your apps, send emails, make purchases, fill out forms, manage longer-running goals, and continue working even after you close the app.
KDnuggets10 min read3 Statsmodels Tricks for Time Series Analysis & Forecasting
A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it.
KDnuggets5 min readPython Foundations for Engineering: A KDnuggets Cheat Sheet
This is the Python material that does not get left behind. Almost none of it is replaced by a framework later. Learn it, and keep this cheat sheet close by as a handy reference.
KDnuggets3 min readForward Deployed Engineer: AI’s Hottest New Career, or Consulting With a Better Title?
The forward deployed engineer: is this the hot new AI career, or has the hype cycle rebranded?
KDnuggets11 min read5 Proven Techniques for Token Compression and Prompt Optimization
Reduce costs, improve response quality, and build leaner AI applications with these prompt engineering strategies.
KDnuggets6 min readBrowserAct AI Web Scraper in 2026: Build Once, Run Repeatedly
Describe your data needs in plain language and turn websites into a continuous source of fresh data.
KDnuggets6 min readFrom Messy Documents to Structured Data with Docling
Docling takes documents in whatever inconsistent format they arrive in, and converts them into one unified, structured representation that both people and AI systems can work with reliably, rather than everyone downstream having to guess at what a wall of extracted text actually meant.
KDnuggets16 min readHow to Use Marimo for Interactive Data Analysis
Learn how to use Marimo for interactive data analysis with Python, Pandas, and Altair, and turn a reactive notebook into a simple shareable dashboard.
KDnuggets6 min read10 Python One-Liners That Will Make Your Code Cleaner and Faster
Get work done, and have seconds added back to your life since you won't be writing second, third or — *gasp!* — fourth line follow-ups to your impeccable typed line numero uno.
KDnuggets6 min readDid AI Just Solve One of Mathematics’ Biggest Problems?
OpenAI’s agents reached a proposed solution in 88 hours. But the human research that came before, and the controversy that followed, raise a harder question: what actually counts as an AI discovery?
KDnuggets8 min readOllama for Managing Local Language Models: A KDnuggets Cheat Sheet
Ollama pulls model weights, keeps an HTTP server on port 11434, and hands any client an OpenAI-shaped endpoint pointed at your own machine. Learn how to manage, configure, and optimize using Ollama right here.
KDnuggets3 min readHow to Turn Excel Data Into PowerPoint Presentations With AI
Learn how to use Julius AI to analyze Excel data, verify key findings, and turn them into an editable PowerPoint presentation.
KDnuggets7 min readSuperWhisper s1-mini: The 600M Parameter Model Built Just for Transcription
This is a summary of, and insights into, what I found digging into the recently-released Superwhisper S1 family of voice-to-text models.
KDnuggets8 min read5 Free Courses to Learn AI Engineering
Learn LLM fundamentals, AI engineering, RAG, MLOps, fine-tuning, and deployment with five practical courses designed to help you become a stronger AI and machine learning engineer.
KDnuggets7 min readGemini 3.5 Transcribe vs OpenAI’s GPT-Transcribe
Here's how each got to where it is, a real use case and working code for both, and a side-by-side on the numbers that actually matter.
KDnuggets6 min read3 Numba Tricks for Python Runtime Optimization
When Numba code disappoints, it's nearly never the compiler, and usually ends up being the boundary around the compiled code: not crossing it, not making it wide enough, or crossing it during every run.
KDnuggets7 min read
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