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## LLM Strategy & Workflow
### Fallback Strategy (After 3 attempts / 10 minutes):
When initial attempts fail, sequentially try these LLMs:
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1. **DeepSeek-V3-0324** (good architect, adventure, reliable, let little errors just to be fixed by gpt-*)
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1. **DeepSeek-V3.1** (slower)
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1. **gpt-5-chat** (slower, let warnings...)
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1. **gpt-oss-120b**
1. **Claude (Web)** : Copy only the problem statement and create unit tests. Create/extend UI.
1. **Llama-3.3-70B-Instruct** (alternative)
### Development Workflow:
- **One requirement at a time** with sequential commits
- **On error**: Stop and consult Claude for guidance
- **Change progression**: Start with DeepSeek, conclude with gpt-oss-120b
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- If a big req. fail, specify a @code file that has similar pattern or sample from official docs.
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- **Final validation**: Use prompt "cargo check" with gpt-oss-120b
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- Be humble, one requirement, one commit. But sometimes, freedom of caos is welcome - when no deadlines are set.
- Keep in the source codebase only deployed and tested source, no lab source code in main project. At least, use optional features to introduce new behaviour gradually in PRODUCTION.