# Athena — AI Research Intelligence Engine

> Multi-source research ingestion, pattern detection, and hypothesis falsification pipeline.
> Autonomous daily operation: ingest → summarize → theme-scan → flag weak signals.

## Repo

- **Location:** `Tony_tech/athena-oracle` (public, Gitea)
- **URL:** http://localhost:3000/Tony_tech/athena-oracle
- **Branch:** `main`
- **Origin:** mirrors `~/oracle` (local working copy)

## What it does

Athena runs on a daily cron (13:00 UTC) and continuously ingests from 6 sources,
then applies a signal-scoring + falsification loop to surface real AI research
momentum rather than source-expansion noise.

| Component | File | Purpose |
|-----------|------|---------|
| Pipeline | `pipeline.py` | Orchestrates ingest → store → summarize → score |
| Adapters | `adapters/` | arxiv, github, huggingface, hackernews, reddit, rss_feeds |
| Theme scan | `theme_scan.py` | Cross-source trend detection + idempotent falsification |
| Query | `query.py` | Interactive lookup against the store |
| Archive | `archive.py` | Cold-storage rotation |
| Summarize | `summarize.py` | Summarization via any available inference model |
| Schema | `schema.sql` | SQLite store definition |
| Cron entry | `oracle-pipeline.sh` | Wrapper invoked by Hermes cron |

## Inference model strategy

Athena is **model-agnostic** — it uses whatever inference backend is available at
run time, whether free or paid. There is no hard dependency on a single provider.

`summarize.py` currently targets a local Ollama endpoint (`llama3.2:1b`) when
present. The pipeline is designed so the summarization backend can be swapped for
any model we can reach — local GPU, a paid API, or a free-tier endpoint — without
changing the ingestion, scoring, or theme-scan logic. When no inference backend is
reachable, the summarization step is skipped; ingestion, scoring, and theme-scan
continue uninterrupted.

To wire in a different backend, implement the same `summarize(text) -> (summary, model)`
contract that `summarize_with_ollama` satisfies, and add the dispatch in
`process_card`.

## Data handling

- `oracle.db`, `logs/`, `.env`, `__pycache__/` are **git-ignored** (not committed).
- API tokens (`GITHUB_TOKEN`, `HUGGINGFACE_TOKEN`) are read from environment only — never hardcoded.

## Setup

```bash
pip install -r requirements.txt   # if present; else deps are stdlib + requests
export GITHUB_TOKEN=...            # optional, raises rate limit 60→5000/hr
python3 pipeline.py                # manual run
```

## Architecture detail

See `whitepaper.md` for full system design, scoring methodology, and the
verification discipline that keeps adapters honest.