Zotero is my go-to tool for managing academic papers. It’s open-source with a fantastic ecosystem around it, and over time my library has grown into a useful personal knowledge base. When Anthropic released the Model Context Protocol late last year, I saw an opportunity in giving AI assistants direct access pull items from my Zotero library.

zotero-mcp is a small Python server that implements MCP for Zotero. The scope is intentionally narrow with just three tools: search your library, get an item’s metadata, and retrieve the full text of an item. The idea is that an assistant like Claude can chain these together naturally, searching for relevant papers and then pulling up the details it needs.

This post covers the creation of a multi-layer neural network written in Go. We will walk through the basics of what neural networks are and how they work, specifically looking at some of the earliest types of feed-forward neural networks. We will then walk through the implementation of a Multi-Layer Perceptron (MLP). Our goal in this process is to create a network that performs well at recognizing handwritten digits on the MNIST dataset.