Project Roadmap & Future Tasks

🧭 Current Decision Point

We are at a crossroads. The current app is stable, and plans for expansion are ready.

Focus on minor unimplemented features.

Micro-interactions (Priority C):
- Tab switching animations (fade/slide)
- Dialog entrance animations
- Badge unlock celebrations
Coach Mark Fixes: Verify/Fix if tutorial overlay persists incorrectly.
Image Compression: Refactor to use image package instead of simple file copy.

Establish the data bunker and security.

Phase 2A: Container Manager Setup:
- Setup posimai-db (Postgres) container.
- Setup ai-proxy (FastAPI) container.
- Setup cloudflared tunnel for secure remote access.
Phase 2B: Automation:
- Implement nightly batch processing (e.g., AI Recommendations).

Build the "Posimai Core" platform.

Short Answer: Not recommended for Image/Vision tasks.

Reason: Standard Synology NAS devices (DS220+, DS923+, etc.) lack powerful GPUs (Graphics Processing Units).
Performance: Running a "Vision LLM" (like Llama 3.2 Vision) on a CPU-only NAS would take 30-120 seconds per image, compared to 1-3 seconds with Gemini API.
Exception: Unless you have a specific AI-focused device (e.g., Synology DVA series or a NAS with a PCIe GPU added), it is not practical for user experience.

Strategy 1: Use the Free Tier (Recommended)

Gemini 1.5 Flash offers a generous free tier:
- 15 requests per minute (RPM).
- 1,500 requests per day (RPD).
- This is sufficient for personal use and small-scale testing.

Strategy 2: Caching (Architecture)

Implementation: Store the AI Analysis result in the local DB (Postgres on Synology).
Logic: Before sending an image to Gemini, check if this exact image hash has been analyzed before. (Only works for exactly identical files).
Note: For new photos, you cannot avoid the first analysis.

Strategy 3: Local Proxy Limits

The current ai-proxy already implements a "Rate Limit" (10/day). This prevents runaway token usage/cost.