Tools

Multi-agent pipelines often fail not with errors, but with valid-looking outputs built on corrupt data. Here's an architecture to catch it.

A structured guide to the seven stages of an AI project lifecycle, covering problem definition, data, modeling, evaluation, deployment, and monitoring.

A developer moves a working local data pipeline to AWS EC2 and discovers every hidden assumption baked into single-machine setups.

Learn how to combine a scikit-learn churn prediction pipeline with an LLM-powered agentic AI system into a single autonomous Python workflow.

Learn how to run Muse Glimmer 30B locally using llama.cpp with DFlash speculative decoding and the Pi coding agent for agentic workflows.

LLMs have shifted how software engineers spend their time. Here are three techniques to manage projects more effectively with AI agents.

A rule-based document parsing dispatcher gives enterprise RAG pipelines explicit, inspectable control over which methods run and why.

Prompt engineering helps you ask better questions. Specification engineering defines what a correct answer actually looks like.

Anthropic tested 14 frontier AI models in high-stakes simulations where model goals conflicted with human instructions. Here's what they found.

MILP works for small routing problems, but ALNS scales further. This article applies Adaptive Large Neighborhood Search to a complex pickup-and-delivery problem

Graph engineering treats AI applications as explicitly designed workflows. Learn the core components and build a reliable LangGraph research workflow.

Claude Code isn't the only option for agentic CLI coding. These seven alternatives offer more model flexibility, lower costs, and stronger harnesses.