<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Projects on Stephanie Rebecca</title><link>https://stephanierebecca.com/categories/projects/</link><description>Recent content in Projects on Stephanie Rebecca</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Mon, 22 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stephanierebecca.com/categories/projects/index.xml" rel="self" type="application/rss+xml"/><item><title>Introducing datasources</title><link>https://stephanierebecca.com/posts/introducing-datasources/</link><pubDate>Mon, 22 Jun 2026 00:00:00 +0000</pubDate><guid>https://stephanierebecca.com/posts/introducing-datasources/</guid><description>&lt;p&gt;Datasets are the absolute foundation of machine learning development. I built a data-discovery and routing layer that lets AI agents know which real-world data source to use, how to access it, whether it is usable, and what it can be joined to. Existing public-data directories are human/topic-centric, the aim of this repo is to be agent-centric. It asks: given a research question, which dataset should an agent reach for, how does it authenticate, what can it join on, and is there an API/MCP/tooling path?&lt;/p&gt;</description></item></channel></rss>