CC Fest · Creative AI / ML camp · free & virtual
A field manual for taking generative systems apart — how they write, imagine, and move. Serious enough for educators, artists, and researchers to bring real questions; accessible enough that beginners can enter and skeptics can opt out of direct use.
Not a prompt-engineering webinar — an investigation into what the machine is actually doing. Browser tools, session plans, and worksheets — no coding, no accounts, no live AI needed.
Three Saturdays · July 11 / 18 / 25 · 9–11 am PT · dates & logistics →
Recorded · async-friendly · global time zones welcome — can't make 9am PT? the async route is a full path →
Tool principle — every tool makes something invisible visible.
a free creative AI camp — text, images, video — for educators, artists, students & curious learners.
Text is sequential.
Images are spatial.
Video must stay coherent.
Open the facilitation guide, pick a pathway, and use the text tools without requiring student accounts.
Facilitation guide → UseBreak text into tokens, adjust temperature, and see prediction become something you can point at.
Open the Tokenizer → Critical / No-AIParticipate through critique, consent, observation, and unplugged activities without direct generation.
See the pathway →The same investigation loop moves through each medium: predict, change one thing, compare what moved, then name the human decision.
Filter by modality, then inspect the visible thing: a token distribution, a denoising path, a drifting frame, or a documented claim.
The small set of tools featured live in the Zoom sessions. Mechanics curious? The row below names the under-the-hood tools for tokenization, diffusion, CFG scale, and latent space.
Not “play with the AI.” Investigate it. The loop is the same in every session, across every modality.
Before running anything, write down what you expect to happen.
Adjust one setting, prompt, or input. Hold everything else still.
What actually appeared? How does it match your prediction?
Default, failure, or pattern — say it precisely.
Revise, reject, document — or turn it into a project or lesson, and loop back.
The same five verbs used everywhere in the camp — the quick starts above are three of them. Opting out of direct AI use never means opting out: see the No-AI pathway for the full route.
Try a tool directly and document what happens.
Analyse pre-generated examples without logging into a tool.
Create a lesson, worksheet, or facilitation plan.
Make an explainer or a small interactive tool.
Write a critique, consent checklist, model card, or unplugged activity.
Three Saturdays · July 11 / 18 / 25 · 9–11 am PT · virtual · recorded and async-friendly. Join the waitlist / mailing list for recordings and the next cycle.
AI use in development
Learning Machines was developed with AI assistance for planning, coding, copy drafting, research, and critique. AI-assisted workflows also helped organize session notes, transcripts, chat exports, and aggregate program data. Public recaps, quotations, attributions, and findings are reviewed by the facilitator against source records. Most interactive instruments on this site are authored browser simulations, not live machine-learning models. Genuine model outputs are labeled with the source information that was preserved; incomplete provenance is named rather than guessed. External services process submitted material under their own terms and account settings. The colophon explains the full labeling standard.
What is not AI. Most Learning Machines instruments do not run machine learning. They use authored data, deterministic JavaScript, rules, illustrations, and human input.
Some parts of the site use ordinary web services: Google Forms collects submissions, GoatCounter provides basic traffic counts, and a few activities retrieve Wikipedia content. Notes and preferences may also be stored locally in the browser. None of these features generates content with AI; each has its own data and privacy considerations.
Classroom origins
Learning Machines grew out of Generative AI as a Creative Collaborator, a course developed by Saber Khan and Danny Gámez at Campbell Hall School.
Consent & context
Public sharing should follow the Consent Protocol. For the full frame, read the Project Brief or visit the original CC Fest Coding Camp.