This article covers five prompt optimization strategies such as: prompt optimization, prompt engineering, LLM output quality, few-shot prompting, chain-of-thought, structured outputs.

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 ...

It's no secret that AI agents burn massive amounts of tokens on search results and file retrievals. They pull in dozens of full-length files, logs, and comment blocks, and just reading through those ...

Stop sending every AI request to your most expensive model. See how intelligent routing can cut cost and latency without sacrificing much quality. Most production AI agents still send every LLM call ...

SWE-bench is still the benchmark most people think of first when evaluating agentic coding systems. It gives an AI agent a real GitHub issue and a snapshot of a real repository, then asks it to ...

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 ...

Learn how to install Python on Windows using the Python Install Manager, WinGet, uv, Miniconda, or the official Python installer, and choose the best setup for beginners and Python development.

This guide walks through what contributing to open source projects actually covers, how to pick a project that will actually respond to you, the exact git mechanics, and more. GitHub added 36 million ...

Ten AI influencers who are actually shaping 2026, from safe superintelligence to AI-native search. Here is who to follow and why. AI is no longer a novelty; it's the foundational layer of modern ...

Running a 70B model in production can be expensive, slow, and, for many tasks, unnecessary. If you're building a focused pipeline like a document classifier or a multilingual support responder, a well ...

Look at the architecture diagrams for generative AI (GenAI) applications built just two years ago, and they resemble a tangled web of dependencies. The standard stack required a massive vector ...

Gone are the days when you had to write Python code for every step of data cleaning, analysis, and visualization. Today, a new generation of AI-powered data tools is making the entire process faster ...