The class in brief
Sydney's third night moved from coded, structure-first visuals into full video craft: keyframes as living stills, the six-part video prompt structure, an audio-prompt companion, and the night's biggest idea, character consistency, demonstrated start to finish on a single woodcut mouse named Baxter. The closing thesis was editing: AI output is a first draft, not a finish. After this page you can write a video prompt in six deliberate slots, build a character turnaround sheet before animating anything, and protect a flat illustration style from drifting toward realism mid-motion.
The night at a glance
Why this matters · 6:25 PM
Describe the structure, not the finished look, and the result stays editable.
Coded generation flips the usual instinct: instead of describing what a finished image looks like, you describe what's underneath it, the core information, the hierarchy, the rules . A generated image is flattened and hard to change; coded output stays in editable parts you can revise through conversation .
The demo was a fully interactive perfume bottle prototype, built by chaining Claude for process logistics, Midjourney for ideation, and ChatGPT for tech specs, material, and lighting, into one working browser demo you could rotate and relight.
The craft · 6:53 PM
A keyframe is pinned to a timestamp. A reference just guides the mood.
Keyframes are living stills: frozen moments that still carry motion potential, and they can start, continue, or resolve a sequence . A reference guides palette and mood and may never appear in the final output; a keyframe is pinned and locks composition, pose, and camera before the AI animates between two of them .
Prompting for keyframes runs on three principles: create with intention, consider what happens before and after the frame, and verbs matter . Load the still with elements whose physics imply movement, helium balloons, powdery snow, a walking gait, so the video has somewhere to go.
The framework · 7:05 PM
A TikTok video, an establishing shot, a timelapse, a Go-Pro clip, expired film. Sets the frame everything else lives in.
A cowboy, a candle, a carousel, a carnival, a puppy, an apple tree. Whoever or whatever the video is about.
Floating in space, at the lake, in the kitchen. The scene around the subject.
Flickering, dolly in, Dutch tilt, crash zoom, dancing. Say "static camera" explicitly if you want none.
Dark and moody, high key, film noir, vintage camcorder. Same slot as the image framework's style row.
16:9, 4k, low motion, 24 fps, 1:1, loop. The technical spec the model needs to hit.

Three pitfalls got named right after the framework: overloaded prompts confuse the AI with too many ideas at once, static keyframes yield almost no movement to animate, and unclear timing leaves the AI unable to stitch events in order .
The craft · 8:09 PM
Models hold WHO better than HOW.
Character consistency was the night's biggest idea, demonstrated live on a woodcut mouse named Baxter, chained through four tools. Gemini analyzed the illustration style from an early Midjourney design to bank reusable prompt language , then ChatGPT built the character turnaround sheet, front, side, back, as the master reference .
Locking the subject took multiple reference angles up front, five white animals shot the same way, so the model couldn't invent features later , then combining references to test what carried over .
The last step tailored the prompt to the render model itself: Claude was handed the mouse keyframe and asked to write the Seedance-specific prompt, formula-style, subject plus action plus environment plus camera plus lighting plus style .
The exercise · 8:24 PM
Build your reference set in ChatGPT, Copilot, or Gemini first, then generate the two clips .
Deliver a gif or a link, plus notes on what held and what drifted between the two scenes.
Environment, what's making the sound, texture, and mix, the same order as the visual framework . Three habits on top of it: visuals come first, label your audio ("Music:", "Sound Effects:"), and put spoken dialogue in quotes.
"AI generations are infinitely better when we edit them, take them apart, and put them back together in ways only we as humans can" . Demonstrated on a full retexture (photo to stylized doll) and a logo composited onto a shirt, then animated .
Traditional upscaling preserves and predicts pixels; generative upscaling reimagines detail that was never there . Sydney's own pick is Topaz, which now lives inside Photoshop too.
Methods and prompts
The video companion to the image and text frameworks. Motion is the new slot: name what moves and what the camera does, or say "static camera" if you want neither.
Working prompt
Format: [TikTok video, establishing shot, timelapse]. Subject: [the hero]. Details: [what's happening around it]. Motion: [what moves, what the camera does, or "static camera"]. Style: [vibe, colors, textures]. Parameters: [aspect ratio, fps, loop].
You will know it worked whenthe output moves the way your Motion field described, or holds a static camera if that's what you specified, with nothing else drifting.
Lock a character's front, side, and back as one master reference sheet before generating a single scene. Everything downstream points back to this one file.
Working prompt
Here's my character design [attach]. Analyze its style first: line weight, color, texture, era. Then build a full turnaround sheet, front, side, and back view, same style and proportions throughout. This becomes my master reference for every scene after this.
You will know it worked whenthe front, side, and back views hold the same line weight, color, and proportions as each other, not three slightly different characters.
Hand your keyframe and your goal to a smart model like Claude or ChatGPT, and let it write the render-model-specific prompt. Different video models want different formulas.
Working prompt
Here's my keyframe [attach] and what I want to happen next: [describe the action]. Write me a prompt formatted for [Seedance/Kling/Runway], following its formula: subject, action, environment, camera, lighting, style. Flag anything that risks breaking character consistency.
You will know it worked whenit names a specific risk to character consistency, not just a generic caution, before handing over the formatted prompt.
Environment, what's making the sound, texture, mix. Visuals get designed first; the audio prompt describes what should support them, not lead them.
Working prompt
Environment: [ambient baseline]. What's making the sound: [footsteps, a door, wind]. Texture: [joyous, muffled, sharp, gritty]. Mix: [foreground or background, loud or subtle]. Music: [label it]. Dialogue: "[quote exactly what's spoken]."
You will know it worked whenthe generated audio matches what you specified in each field, especially the exact words in Dialogue rather than a paraphrase.
Before asking AI to fix a rough output, name what's actually wrong with it. The output is a first draft; your read on what needs editing is the part only you can supply.
Working prompt
Here's my raw output: [attach]. I answer first, then you check me. My read on what needs fixing: [color, composite, retexture, trim, pacing]. Tell me if I'm right, then help me execute it as an edit, not a full regeneration.
You will know it worked whenit confirms or corrects your read on what's broken, then executes a targeted edit instead of regenerating the whole piece from scratch.
Where it broke · all night
Claude's own Seedance prompt for the woodcut mouse came back with a caveat built into the output: style persistence drift, keep camera movement minimal, the linocut border is fragile . Video models are trained mostly on photographic and 3D motion, so a flat 2D style drifts toward realism as soon as it starts animating, and the only real fix is holding the camera static and pre-flighting the prompt in a smart model before you generate.
Sydney named the honest state of the medium plainly: "we're still in the six-finger era of video," the same early, glitchy stage AI images went through before the errors washed out.
Try this prompt
Quiz me on the video prompt structure and the character-consistency pipeline. Then give me an illustrated character and make me plan the reference set and the camera constraints before I generate a single frame.
You will know it worked whenit quizzes you on the video prompt structure and the character-consistency pipeline first, then makes you plan the reference set and camera constraints before you generate anything.
The shelf
104 captures, in order. Click any one to see it full size.







































































































