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Fall 2026 Registration Now Open

Gray Area’s 2026 Education Program offers workshops and intensive courses focused on creative agency through technology.

FALL 2026 CLASSES

This season, build the future of the web, compose electronic music, design installations with projection mapping, and expand your hardware skills with a new modular synthesizer workshop.

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LEARN AT YOUR OWN PACE

Get online audit access to recordings of courses so you can view and listen from home on your own schedule.

Course Category: Ongoing

Program Highlights

 

Creative Code Intensive

Our Creative Code Intensive is a newly redesigned 12-week in-person course in creative technology, offering a comprehensive introduction to foundational and advanced tools for programming digital media. In an interdisciplinary environment of artists, designers, engineers, and other creative professionals, participants will create studio projects and discuss work in group critiques, culminating in an exhibition of work in a public showcase.

Beyond Big Tech: Building Alternative Futures for Personal Data and Infrastructure

A six-week online course exploring how artists, organizers, archivists, designers, and cultural practitioners can build alternatives to centralized tech infrastructure. Evolved from Gray Area’s earlier DWeb for Creators curriculum, the course combines critical discussions of data sovereignty, decentralized governance, and community infrastructure with hands-on introductions to tools for self-hosting, archiving, running web services, and building independent networks. Beginners are welcome, with technical demos balanced by questions of power, stewardship, and what it means for communities to control their own data and digital spaces.

 

Handmade Datasets: Strategies for working creatively with small data and Artificial Intelligence

This beginner-friendly intensive explored how small, handmade datasets can be used to train custom image, text, and audio models. Taught by artist, educator, designer, and coder Aarati Akkapeddi, the course introduced techniques including LoRA fine-tuning, Retrieval-Augmented Generation (RAG), and RAVE while examining how training data is assembled and how those choices shape model outputs. Participants experimented across visual, text, and audio approaches, ultimately creating their own dataset and custom model while developing a more critical, hands-on understanding of machine learning.

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