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Description
Summary
We've built scripts/posters/generate-tag-icons.py to generate 1:1 icons from tag words using minimal prompts. Through experimentation, we've learned that archetype-based prefixes work better than abstract meta-instructions.
Key Insight
Instead of telling the model that it's wrong, tell it which human mistake it's making:
- ❌ "Semantically incorrect interpretation of..." (too abstract)
- ✅ "Stock photo misunderstanding of..." (encodes the mistake)
Preferred Prefixes (so far)
Based on testing, these work well:
Stock photo misunderstanding of {tag}- very reliableOverconfident intern interpretation of {tag}- good humorIdiomatic literal interpretation of {tag}- strong for verbsVisually misread meaning of {tag}- especially for "social"Literal but wrong interpretation of {tag}- decent fallback
Available Prefixes
icon, stock, linkedin, childrens, headline, idiomatic,
wikipedia, intern, pun, celebrity, boomer
Old meta-instruction prefixes are commented out in the code but preserved.
Future: Feedback System
Problem: Hard to systematically track which prompts produce good results.
Idea: Fork one of our Discord bots to create a feedback loop:
- Post generated images to a channel
- React with 👍/👎 to rate
- Track which prefix + tag combinations work best
- Build a "best archetype → tag" mapping over time
Could also:
- Generate a prompt matrix (safe / spicy / unhinged tiers)
- A/B test which misreads perform best
- Auto-retry failed tags with different prefixes
Related Files
scripts/posters/generate-tag-icons.py- main scriptscripts/posters/tag-icons/- output directoryscripts/posters/README-posters.md- documentation
Usage
# Single prefix
python scripts/posters/generate-tag-icons.py "security" -p stock
# Random rotation
python scripts/posters/generate-tag-icons.py --from-facts -p random
# List all prefixes
python scripts/posters/generate-tag-icons.py --list-prefixesMetadata
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enhancementNew feature or requestNew feature or request