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KDnuggets™ is a leading site on Business Analytics, Big Data, Data Mining, Data Science, and Machine Learning and is edited by Gregory Piatetsky-Shapiro (email to editor1 at kdnuggets) and Matthew Mayo (email to mattmayo at kdnuggets). KDnuggets received over 50 awards/mentions as a leading publication, including In 20 Data Industry Experts You Need to Follow on Twitter, Churchill Frank, Oct 2017. In Top 10 Most Influential Brands on Big Data, Onalytica, May 2017. No. 3 in Top 75 Data Science Blogs by Feedspot, Jan 2017. No. 1 in Agilience Top Authorities in Machine Learning, Nov 2016. No. 1 in Agilience Top Authorities for Data Mining, No. 2 for Data Science, Nov 2016. No. 3 in AI Intelligence & Machine Learning: Top 100 Influencers and Brands, Onalytica, Mar 2016. No. 4 in Big Data 2016: Top 100 Influencers, Onalytica, Feb 2016. In InformationWeek Twitter Top 10 Data Science, Analytics, And BI Feeds among IBM Big Data & Analytics Heroes In top 30 People in Big Data and Analytics, Innovation Enterprise profiled in INFORMS: KDnuggets Serves Analytics and Big Data Fields in Information Management 7 Business Analytics Gurus on Twitter voted the Best Big Data Tweeter by Big Data Republic featured on Forbes, among Top Influencers in Big Data Source

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Scope Trade/B2B, Consumer
Language English
Country United States of America
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Getting Started with the Claude API in Python

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By Bala Priya C
| KDnuggets Verified
In this article, you'll learn how to use the Claude API in Python, make your first request, and handle responses with the official SDK. You want to add Claude to a Python application. Creating an account and making your first API call is straightforward. The official documentation can get you from zero to a working request in a few minutes. The next questions are usually more practical: • What does the response object contain? • How do you stream responses so users can see output as it's generated?
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10 Agentic AI Frameworks You Should Know in 2026

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By Kanwal Mehreen
| KDnuggets Verified
LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, and more. If you're building AI agents in 2026, these are the frameworks worth paying attention to before starting your next project. Agentic AI frameworks are no longer just wrappers around a large language model (LLM) and a few tools. The better options now help developers manage things like state, memory, tool usage, evaluations, and deployment without having to build everything from scratch.
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Humanity’s Last Exam is a Distraction

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By Iván Palomares Carrascosa
| KDnuggets Verified
This article takes a gentle dive into the ultimate AI systems evaluation benchmark, outlining why it was created, curating diverse opinions from groups of experts in the field about it, and wrapping up with a summary of the most widely accepted verdict. Humanity's Last Exam (HLE) is a benchmark designed to measure the reasoning and deep knowledge capabilities of most modern AI systems. Its defining trait: its underlying evaluation is taken to the extreme.
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5 AI Coding Platforms to Build Apps Without the Headache

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By Abid Ali Awan Verified
| KDnuggets Verified
Explore the best AI coding platforms, no-code app builders, and vibe coding tools that help beginners and developers build, test, and deploy full-stack apps using simple prompts. Have you ever thought, "If I had programming skills, I could launch my own startup or build the app idea I have been thinking about"? For many non-technical founders, creators, and professionals, the hardest part is not coming up with ideas. It is turning those ideas into working products.
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Building Local AI Systems: Qwen3.6 + MCPs

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By Shittu Olumide, Qwen-Agent handles
| KDnuggets Verified
Define a tool once as an MCP server and any MCP-compatible client, any model, any framework, can discover and call it with zero custom integration code per model. Every developer building with local AI hits the same wall eventually. The model works. It reasons well, writes solid code, and answers complex questions. But it cannot do everything. It cannot query your database, open a GitHub issue, or call your internal API.
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7 Real-World Python Projects You Can Build in 2026 (With Guides)

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By Abid Ali Awan Verified
| KDnuggets Verified
Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets. Python remains one of the best programming languages for building practical, real-world projects, especially as AI, automation, APIs, dashboards, and data applications continue to grow in 2026.
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Your RAG Pipeline Is Probably Useless. Here’s a Better Alternative

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By Nate Rosidi
| KDnuggets Verified
Learn what to reach for when retrieval-augmented generation fails in production. Retrieval-augmented generation (RAG) emerged as the standard approach for connecting documents with large language models (LLMs). The pattern is simple: embed a corpus, retrieve the most relevant chunks by vector similarity, inject them into a prompt. It works well in demos and many production systems. It also fails in predictable, documented ways that only show up at scale.
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5 AI Coding Subscription Plans That Give Developers the Best Value

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By Abid Ali Awan Verified
| KDnuggets Verified
This is an opinion-based look at the AI coding subscription plans that I think give developers the best value for their money, from token and usage-based plans to full coding-agent ecosystems. For a while, "unlimited" AI coding plans felt like the best deal in developer tools. You paid a fixed monthly fee and used powerful coding agents as much as you wanted. But that model was never going to last forever.
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Fine-tuning Language Models on Apple Silicon with MLX

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By Vinod Chugani
| KDnuggets Verified
Fine-tuning a language model used to mean renting cloud GPUs and watching the meter run. If you own a Mac with an Apple Silicon chip, you can now adapt an open model to your own data locally, at zero cloud cost, using a framework built specifically for the hardware sitting in your laptop. I made the switch from Windows and Dell machines to Mac back in 2014 and never looked back.
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5 Agentic Workflows to Automate Your Data Science Pipeline

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By Shittu Olumide
| KDnuggets Verified
The average data scientist spends roughly 45% of their working time on data preparation and cleaning, not on modeling, not on insight generation, not on the work that requires genuine judgment. That estimate keeps appearing across industry surveys because it keeps being true.
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KDnuggets Journalists

Awan, Abid Ali
Mayo, Matthew