shoko
ecfd0b1160
- Hermes Detective Agency: Open-ended mystery investigation game - Roles: Chief (human), Witness (Kimi), Detective (Hermes) - 5 difficulty levels, community cases, open-ended solving - Scoring: Alignment %, Evidence %, Time - Features: Retry, Journal, Observe mode - Tech: Kimi Vision + Hermes Agent + Pollinations Changelog: - Research phase: Kimi capabilities, Hermes agent, image APIs - Brainstorming: 14 ideas explored - Comparison matrix: Detective selected as winner - Concept finalized with all design decisions
80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
# Idea 001: Visual Narrative Agent
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**Date:** 2026-04-19
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**Status:** Idea
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**Tags:** hermes-agent, kimi-vision, storytelling, image-generation
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## Concept
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An agentic storytelling system where Hermes orchestrates a narrative loop with Kimi's visual analysis and built-in image generation skills to produce coherent visual stories.
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## User Flow
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1. User provides text prompt (e.g., "A lone astronaut discovers an ancient alien garden on Mars")
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2. Hermes plans story structure (scenes, pacing, visual style)
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3. For each scene:
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- Hermes generates image prompt
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- Generate image (Hermes built-in skill: manim / ascii)
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- Kimi analyzes generated image
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- Kimi's feedback refines next scene's prompt
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4. Return compiled visual story to user
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## Key Differentiator
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Most story-to-image tools: **Generate → Done**
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This concept: **Generate → Analyze → Refine → Loop**
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Kimi serves as the **visual reasoning engine** — tells Hermes if the generated image matches the intended scene, catches inconsistencies, and informs prompt refinement for the next scene.
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## Tech Stack
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| Component | Source | Role |
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|-----------|--------|------|
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| Hermes Agent | Nous Research | Orchestration, planning, decision loop |
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| Kimi Vision | Moonshot AI (via gateway) | Image analysis, visual feedback |
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| Image Generation | Pollinations AI | Free tier, multiple models (Flux, etc.) |
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### Image Generation Options
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| Provider | Free Tier | Quality | Use Case |
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|---------|-----------|---------|----------|
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| **Pollinations** ✅ | ✅ Yes | Good | Primary (simple, free) |
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| **Flux (local)** | ✅ Free | High | If GPU available |
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| **Hermes skills** | ✅ Free | Niche | Fallback/ASCII aesthetic |
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### Pollinations API (Primary)
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- **Endpoint:** `https://gen.pollinations.ai/image/{prompt}`
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- **Models:** flux, zimage, wan-image, qwen-image, etc.
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- **Cost:** Free tier (pollen credits), ~$1/1 Pollen paid
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- **Auth:** Optional for free tier
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## Strengths
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- ✅ Combines Hermes + Kimi + Pollinations natively
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- ✅ Agentic visual feedback loop is unique
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- ✅ Visual coherence check via Kimi ensures quality
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- ✅ Free tier = low barrier to test
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- ✅ User controls output format (default: image)
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## Weaknesses
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- ⚠️ Pollinations quality vs DALL-E/Midjourney (may need to test)
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- ⚠️ Kimi requires gateway access (no direct API key)
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- ⚠️ Loop adds latency (generate → analyze → refine)
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- ⚠️ Need to verify Pollinations reliability
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## Uniqueness Score
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**7/10** — Agentic visual feedback loop is novel, but need to verify if built-in image generation is compelling enough
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## Next Steps
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- [ ] Explore Hermes built-in image skills (manim, ascii)
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- [ ] Define output format options
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- [ ] Sketch technical architecture
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## Related Ideas
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- See: `002-xxx.md`, `003-xxx.md` for alternatives
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