[EZRA BURN-MODE] Deep Dive scaffold directory guide
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# Deep Dive Scaffold
> Parent: the-nexus#830
> Created: 2026-04-05
This directory contains phase-by-phase implementation skeletons for the Deep Dive automated intelligence briefing system.
## Directory Structure
```
scaffold/deepdive/
├── phase1/ # Source aggregation (ZERO blockers, can start now)
│ ├── arxiv_aggregator.py ← Run this today
│ ├── blog_scraper.py (stub)
│ └── config.yaml
├── phase2/ # Relevance engine (needs Phase 1)
│ ├── relevance_engine.py (stub)
│ └── embeddings.py (stub)
├── phase3/ # Synthesis (needs Phase 2)
│ ├── synthesis.py (stub)
│ └── briefing_template.md
├── phase4/ # TTS pipeline (needs Phase 3)
│ ├── tts_pipeline.py (stub)
│ └── piper_config.json
└── phase5/ # Delivery (needs Phase 4)
├── telegram_delivery.py (stub)
└── deepdive_command.py (stub)
```
## Quick Start
### Phase 1 (Today)
```bash
cd the-nexus/scaffold/deepdive/phase1
python3 arxiv_aggregator.py
```
**Requirements**: Python 3.8+, internet connection, no API keys.
**Output**: `data/deepdive/raw/arxiv-YYYY-MM-DD.jsonl`
## Sovereignty Preservation
| Component | Local Option | Cloud Fallback |
|-----------|-------------|----------------|
| Embeddings | nomic-embed-text via llama.cpp | OpenAI |
| LLM | Gemma 4 via Hermes | Kimi K2.5 |
| TTS | Piper | ElevenLabs |
**Rule**: Implement local first, add cloud fallback only if quality unacceptable.
## Next Steps
1.**Phase 1**: Run `arxiv_aggregator.py` to validate fetch pipeline
2.**Phase 2**: Implement `relevance_engine.py` with embeddings
3.**Phase 3**: Draft `synthesis.py` with prompt templates
4.**Phase 4**: Test `tts_pipeline.py` with Piper
5.**Phase 5**: Integrate `telegram_delivery.py` with Hermes gateway
See `docs/deep-dive-architecture.md` for full technical specification.