NexAgent AI
Vancouver local team online
Before we start
Leave your contact — our AI consultant answers right away, and a human can follow up when needed.
Turn any topic into a set of black-marker stick-figure doodle cards. The English track lets the model bake short labels right into the art; the Chinese track draws textless bases and overlays handwriting with PIL — locked style, so the whole set looks drawn in one sitting.
10
cards / set
EN+ZH
two tracks
4
credits/img (low)
5
pipeline stages
Share a few details and your topic of interest to download the sample deck, caption template, and skill spec.
To share materials with interested learners, leave your basic info and topics first. The file opens automatically after submission.
From locking facts, to designing cards, to two generation tracks, to QA and publishing.
Multi-source verification, one fact per line with sources
Narrative arc, one fact per card, short labels
Model bakes text, prompt locks the style
Textless base + PIL handwriting overlay
Pick large / overlay, contact sheet, check for garbled text
Social caption + ship to nextagent.ca
Image models spell short English but not Chinese — so the two tracks split, and a locked style keeps the whole set coherent.
Multi-source
Every record on one line with a source; events past the knowledge cutoff must be verified online — never invent a result.
cards.json
One JSON holds every card; the scripts take the English head or Chinese textless head by --lang. Edit copy without redrawing.
Bake vs overlay
English is baked into the image by the model; Chinese is a textless base with handwriting overlaid via PIL.
Before a deck ships, confirm at least: 1. Does every card map to a verified fact? 2. Any model-hallucinated stats baked in (e.g. "possession 62%")? 3. Are Chinese names in the caption, not forced into the base image? 4. Is any headline text garbled? 5. Are all source links present in the caption? 6. Is it marked "AI-generated sample; data belongs to original sources"?
| Time | Duration | Module | Mode |
|---|---|---|---|
| 00:00–10:00 | 10 min | Why one fact per card | Talk |
| 10:00–25:00 | 15 min | Lock facts + design cards.json | Break down |
| 25:00–45:00 | 20 min | English track: bake-text prompts + generate | Hands-on |
| 45:00–65:00 | 20 min | Chinese track: textless base + overlay | Hands-on |
| 65:00–80:00 | 15 min | QA + social caption + publish | Discuss |
For anyone who needs to turn a topic into a full set of visual content fast.
gpt-image-2 can't spell Chinese — it comes out garbled. So Chinese cards are generated as digit-only textless bases, then clean handwritten title and caption are overlaid with PIL.
Not for Chinese — the text is an overlay. Edit cn_title / cn_caption in cards.json and re-run finalize: instant, zero credits. English text is baked in, so changing it means regenerating that card.
Default low quality is 4 credits/image — about 40 for a 10-card set, visually plenty for doodles.
Headline facts are multi-source verified; but the image model sprinkles in decorative micro-text (e.g. "possession 62%") that is NOT real data. Real facts live in the caption and the cards.json fact field — verify before publishing.
The tooling and full bilingual example are open at github.com/NextAgentBC/doodle-cards.
We can wire research, design, generation, and captions into one reusable visual-content line for you.