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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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Media Outlet details
| Scope | Trade/B2B, Consumer |
|---|---|
| Language | English |
| Country | United States of America |
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Recent Articles
Search ArticlesBuild an End-to-End Data Science Project with Grok Build and Grok 4.6
Use Grok Build to create a production-ready data science workflow with EDA, scikit-learn, model training, FastAPI, API testing, and cloud deployment. Grok 4.6 is xAI's latest frontier model, built specifically for coding, agentic tasks, and knowledge work. It focuses heavily on long-running agents that can work across a codebase, research problems, test their work, and keep going through complex multi-step tasks.
Run Muse Glimmer for Local Vibe Coding with llama.cpp, DFlash, and Pi
By, KDnuggets Assistant Editor on August 21, 2026 in Artificial Intelligence Run Muse Glimmer locally on an RTX 3090 GPU using llama.cpp, DFlash speculative decoding, and Pi for fast, private, agentic AI coding. Muse Glimmer is gaining attention in the local AI community and is being compared with Qwen's 27B-class models. In many cases, it is performing better, especially for local coding and agentic workflows.
5 Real-World Use Cases for AI Agents Transforming Industries
See how AI agents are autonomously handling support, coding, supply chains, healthcare, and fraud detection today. Agentic AI has officially moved from the research lab into enterprise production. In 2026, the AI narrative has shifted dramatically from conversational chatbots — systems that wait for human prompts to generate text — to autonomous AI agents. These systems can plan, execute, and adapt multi-step tasks across external tools, databases, and APIs without continuous human oversight.
How to Build a Career in AI: 3 Distinct Pathways
Three orientations, three different skill maps, and how to find the one that fits where you actually stand. Over three years of mentoring data science students across more than a thousand sessions, one question comes up before anyone writes a line of code: where do I start with AI? Students arrive motivated but without a map. They read about machine learning engineers, research scientists, and data scientists, and assume the titles describe the same work. They don't.
Top 10 Open-Source Benchmarks for AI Coding Agents in 2026
For years, coding benchmarks mostly measured one thing: could a model write a function that passed the unit tests? While that was useful, it doesn't reflect the reality of software engineering. Modern agentic coding benchmarks evaluate whether AI agents can work inside real repositories, edit existing code, run tests and other commands, debug failures, and maintain code quality as they complete longer and more complex tasks.
How to Answer AI System Design Interview Questions
The interview moved from Design YouTube to Design ChatGPT. Here's the framework. For years, system design interviews meant "Design YouTube," "Design Uber," or "Design WhatsApp." Companies hiring AI Engineers, Applied Scientists, and GenAI Engineers now ask a different set of questions: "Design ChatGPT," "Design a customer support AI," "Design GitHub Copilot," "Design an AI code reviewer," "Design a legal document assistant." Most engineers can call an LLM API.
5 Tools for Building and Deploying AI Agents in Production
This article walks through five tools, one for each layer of the stack from building the agent's logic to running all of it at scale. Building an agent that works in a notebook takes an afternoon. Getting that same agent to survive real traffic, recover from a crash at 3 am, and not leak someone else's data while it runs large language model (LLM)-generated code is a different job entirely, and it's the job most teams underestimate.
Run Qwen3.8-27B as a Local AI Coding Agent in Just 3 Commands
Download Ollama, pull and serve Qwen3.8-27B, and launch it with OpenCode using just three command lines. Running a capable 27B model as a local AI coding agent used to mean setting up inference servers, configuring endpoints, and connecting everything manually. With Qwen3.8-27B, Ollama, and OpenCode, the process is now remarkably straightforward. Qwen3.8-27B is one of the local models I have been most excited to try.
5 Things Vibe Coding Gets Right and 5 Things It Gets Wrong
Is vibe coding the future of programming or a shortcut to bad code? Let’s look at what it gets right and wrong. Vibe coding has become one of those phrases that people either love or hate. Some use it to describe the future of software development. Others use it as shorthand for careless AI-generated code. So instead of asking whether vibe coding is "good" or "bad," the better question is: where does it actually work, and where does it create hidden debt?
What Can I Actually Do with a Small Language Model?
But by keeping these limits in mind, and planning for them, we can effectively use these small, local models for the following broad operations scenarios. The most common objection to running a small language model (SLM) is that it does not know enough, an objection that isn't well calibrated. In reality, no model on the market is a dependable store of facts, and general capability benchmark scores turn out not to predict knowledge reliability at all. The two measures are not correlated.