## LLM Strategy & Workflow

### Fallback Strategy (After 3 attempts / 10 minutes):
When initial attempts fail, sequentially try these LLMs:
1. **DeepSeek-V3-0324** (good architect, adventure, reliable, let little errors just to be fixed by gpt-*)
1. **gpt-5-chat** (slower, let warnings...)
1. **gpt-oss-120b**
1. **Claude (Web)**: Copy only the problem statement and create unit tests. Create/extend UI.

### Development Workflow:
- **One requirement at a time** with sequential commits
- **On unresolved error**: Stop and use add-req.sh, and consult Claude for guidance.  with DeepThining in DeepSeek also, with Web turned on.
- **Change progression**: Start with DeepSeek, conclude with gpt-oss-120b
- If a big req. fail, specify a @code file that has similar pattern or sample from official docs.
- **Warning removal**: Last task before commiting, create a task list of warning removal and work with cargo check.
- **Final validation**: Use prompt "cargo check" with gpt-oss-120b
- Be humble, one requirement, one commit. But sometimes, freedom of caos is welcome - when no deadlines are set.
- Fix manually in case of dangerous trouble.
- 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.
- Transform good articles into prompts for the coder.
- Switch to libraries that have LLM affinity (LLM knows the library, was well trained).
- Ensure 'continue' on LLMs, they can EOF and say are done, but got more to output.