-
Build Your First MCP Server in Python (Stateless Spec Edition)
MCP just got simpler. Learn how to build a stateless MCP server in Python and expose tools, resources, and prompts over HTTP.
-
10 Free AI Tools That Replace Expensive Software for Data Scientists
Build a production-grade data science toolkit without spending a dollar, using open-source AI tools that match, and sometimes exceed, their paid alternatives.
-
Bridging Algorithmic Design and Regulatory Standards in Enterprise AI
Your models can be both cutting-edge and compliant if you stop treating governance as the final hurdle and start building it into every stage of the ML pipeline.
-
Building AI Agents with Docker Agent
Docker Agent is an open-source, Apache 2.0-licensed CLI plugin built by Docker Engineering, installed and run as docker agent. Its own tagline states the goal plainly: run AI agents like containers.
-
7 Best Resources to Learn About Self-Evolving AI Agents
The next step for AI agents? Self-improvement. Here are 7 resources to get started.
-
OpenAI Dots: The Data Scientist’s Reality Check
Dots promises a lot. Before you hand it the keys to your workflow, here's what practitioners need to know.
-
-
I Tested 5 AI Coding Assistants for a Month: Here’s What I Actually Found
Compare five AI coding assistants across real tasks to find which tool fits your workflow.
-
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.
-
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.
-
-
3 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.
-
Python 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.
-
Forward 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?
-
5 Proven Techniques for Token Compression and Prompt Optimization
Reduce costs, improve response quality, and build leaner AI applications with these prompt engineering strategies.
-
BrowserAct 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.
-
From 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.
-
How 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.
-
10 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.
-
Did 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?
-
Ollama 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.
-
How 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.
-
SuperWhisper 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.
-
5 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.
-
Gemini 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.
-
3 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.
-
Batching by Length Instead of Looping Item by Item for SLM Optimization
We finish off our short series on SLM optimization with the third entry, focused on batching by length instead of looping item by item.
-
7 Advanced Python Tricks to Level Up Your Coding Skills
Leveling up rarely means new syntax. It means learning what the language already promised you.
-
MCP Explained in 5 Minutes
A visual guide to MCP that explains how it works, how to use it with Claude Code, Tavily, GitHub, and Playwright, and what is new through simple diagrams that make the whole concept easy for anyone to understand.
-
What I’ve Learned About DeepSeek Harness
KDnuggets team member Shittu Olumide tested out DeepSeek Harness. Here's what he found.
-
Everything Claude Opus 5.5 Actually Ships With
This article pulls together every verifiable number and detail from Anthropic's announcement, the platform documentation, the system card, and independent coverage, so you have one place to check the facts.
-
-
High-Performance Data Processing with Polars: A KDnuggets Cheat Sheet
Polars is a DataFrame library written in Rust on the Apache Arrow memory format, and the speed comes less from the language than from the model. The model? Describe your work as expressions, and the Polars query engine plans them out.
-
Bravely AI Browsing with Leo
Learn about private AI browsing for data professionals.
-
7 Open-Source Alternatives to ChatGPT You Can Run Locally
Explore seven open-source ChatGPT alternatives, from lightweight local chat interfaces and document assistants to agent platforms, multi-user setups, and complete self-hosted AI workspaces.
-
What Everyone Is Getting Wrong About TypeSafe AI’s Jev
A closer look at TypeSafe AI’s Jev, what it actually does, what is genuinely new, and where the hype goes too far.
-
How to Turn a Python Script Into an AI Agent
Learn how to build a Python AI agent with the OpenAI Agents SDK, using tool calling and function tools to automate multi-step workflows.
-
3 Polars Tricks for High-Performance Data Manipulation
Almost every slow Polars script lacks in terms of one of these two: its expression engine written and executing in Rust across every core at its disposal, and its query optimizer that rewrites your work before any of it runs.
-
Reusing the Prompt Prefix with a Key-Value Cache for SLM Optimization
In this second article in our short series on SLM optimization techniques we focus on the reuse of the prompt prefix with a key-value cache.
-
5 Prompt Optimization Strategies That Actually Improve LLM Output
This article covers five prompt optimization strategies such as: prompt optimization, prompt engineering, LLM output quality, few-shot prompting, chain-of-thought, structured outputs.
-
What’s So Good About ChatGPT Work? Here’s What I Found
This article walks through how ChatGPT Work specifically earns its reputation, where the underlying models genuinely hold up against the competition, and where the honest limits are.
-
5 Free Zoomcamps From Data Pipelines to AI Agents
Explore five free hands-on workshops covering data engineering, machine learning, MLOps, LLMs, AI agents, and AI development through practical lessons, homework, projects, and community-based learning.
-
Estimators in Scikit-LLM: A KDnuggets Cheat Sheet
Scikit-LLM wraps language models in the scikit-learn estimator API, so it drops into a Pipeline or a cross-validation loop natively.
-
How to Build Effective Evals for AI Agents
Learn how to build effective evals for AI agents, from designing clear tasks and choosing the right graders to building reliable eval harnesses and tracking changes over time.
-
Orchestration and Execution: How JONI Approaches the Agent Layer
Explore how JONI approaches AI agent orchestration with persistent runtimes, multi-model routing, execution capabilities, and reliability beyond simple content generation.
-
How I’m Using Google Opal for Even More AI Automations
Opal is Google Labs' no-code tool for turning natural language into working AI mini-apps, built on top of an internal framework called Breadboard. Here's how I learned to use it best.
-
5 Free Microsoft GitHub Courses to Learn Data Science and Artificial Intelligence
Explore five free Microsoft GitHub courses covering data science, machine learning, artificial intelligence, generative AI, LLMs, RAG, fine-tuning, and AI agents.
-
7 Python Best Practices Senior Developers Follow (That Beginners Often Miss)
Senior Python practice, watched up close, is mostly surprise reduction. These seven habits surface the surprises before production does.
-
Why DeepSeek-V4.1-Flash Is Such an Exciting Open Model Release
DeepSeek-V4.1-Flash shows how Causal Encoder-Decoder architecture, MoE, KV cache compression, CSA2, cheaper prefill, and efficient decoding can make powerful open-source AI models far more efficient to run.
-
From Spaghetti Code to Clean Python: A Beginner’s Guide
Learn how to refactor messy Python code into clean, maintainable functions.
By Bala Priya C, KDnuggets Contributing Editor & Technical Content Specialist on September 11, 2026 in Python
-
5 Python Techniques for Efficient Resource Orchestration
This article explains 5 Python techniques for efficient resource orchestration and sticks to what's stable today, 3.11 and later for the core techniques, with one 3.14-specific tool called out explicitly as requiring that version
-
A Candid Abacus AI Review: The All-in-One AI Platform for Professionals & Enterprises
If you’re paying for ChatGPT, Claude, and another AI tool simultaneously, this review is for you. It covers what an AI platform like Abacus AI actually includes, how the credit system works in practice, and whether it genuinely replaces your current stack or just adds to it.
-
Feature Engineering in Scikit-Learn: A KDnuggets Cheat Sheet
Once feature engineering lives inside a Pipeline, each step is fitted on training data only, and the model is scored what it actually earned. And that is the idea behind this new cheat sheet.
-
7 Steps to Become a Forward Deployed Engineer in 2026
FDEs are becoming some of the most in-demand engineers in AI. Here’s the 7-step roadmap to becoming one in 2026.
-
5 Useful Python Scripts to Automate CSV Processing
Automate common CSV tasks with these 5 Python scripts for cleaning, validating, transforming, and processing CSV files using the standard library.
By Bala Priya C, KDnuggets Contributing Editor & Technical Content Specialist on September 10, 2026 in Python
-
Build an AI Data Analyst That Thinks Like a Senior Analyst
A six-stage pipeline that checks its numbers before calling anything an answer.
-
7 Approaches to Efficient LLM Training on Limited Hardware
Learn seven engineering techniques to train large language models on consumer GPUs without running out of memory.
-
From RAG to Agentic AI: Building the Next Generation of Intelligent Enterprise Systems
Over the past several years, I have worked through three successive generations of intelligent retrieval systems, each solving problems the previous generation could not. Here is what I have learned.
-
Is ArrowJS Really the UI for the Agentic Era? Here’s What I Found
The way we build interfaces is changing. As AI agents write more of our code, the tools we use to render that code may need to change too.
-
5 Ways I Access Coding Models for Free
Explore five free ways to access AI coding agents, proprietary coding models, and open-weight models without paying for expensive subscriptions or GPUs.
-
Switchyard: NVIDIA’s Open Source Routing Library
Stop sending every AI request to your most expensive model. See how intelligent routing can cut cost and latency without sacrificing much quality.
-
5 Free LLM API Providers You Can Use in 2026
Explore five free AI API providers for accessing large language models, fast inference, multimodal AI, and agentic applications without paying for API usage.
-
-
5 Free Courses to Go From LLM Beginner to Practitioner
A curated, linear pipeline of high-signal free resources that takes you from backpropagation basics to deploying production-grade LLM applications.
-
Quantifying User Behavior Patterns to Build Better Predictive Features
Simply knowing that a 35-year-old male in Seattle clicked 12 times last month tells you almost nothing about his intent.
-
This Python Library Can Run Pandas Workloads Up to 20x Faster
Discover how FireDucks can speed up pandas workloads with lazy execution, compiler optimization, and multithreaded processing, delivering up to 20x faster DataFrame performance in our benchmark.
-
5 Real-World Applications of Agentic AI in Enterprise Automation
Deploy agentic AI across SRE, finance, legal, migration, and security with deterministic safety constraints.
-
Free Transcription with Speakr
How to set up, use, and get the most out of a private, self-hosted transcription platform with full control over where your audio goes
-
7 Common Python Mistakes to Avoid in AI Workflows
A clean run proves the process executed. It says nothing about what the pipeline learned, from which rows, in what state, or whether the saved result can be trusted anywhere else.
-
Speed Up LLM Inference with DSpark Speculative Decoding
Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.
-
7 Python Mistakes Beginners Make (And What to Do Instead)
It's about the mistakes that make a running program wrong. Below are seven of them. For each one you get the hidden cause, plus the first thing worth checking.
-
The Local AI Stack for Productive SLMs
A practical framework for choosing the right tools at each layer of your local AI setup, from model serving to context retrieval.
-
Quantization and Pruning Methods to Make Your LLM Leaner
This article walks through what each technique actually does, why skipping them costs real money and real latency, and then gets hands-on with five specific methods people are running in production right now.
-
What We Can Learn From Google Engineers’ Indispensible Prompts
Hey, Google Engineers: What prompt do you personally refuse to work without, and why?
-
Understanding the Impact of AI on Job Markets
Explore five distinct ways AI is reshaping jobs, from automating routine tasks to thinning entry-level hiring.
|
