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

Repo

Location: Leonard/athena-oracle (private, Gitea)
URL: http://localhost:3000/Leonard/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`	Local summarization (Ollama — currently offline)
Schema	`schema.sql`	SQLite store definition
Cron entry	`oracle-pipeline.sh`	Wrapper invoked by Hermes cron

Local inference status

summarize.py uses Ollama only (DeepSeek fallback removed — OpenRouter 402). Ollama is currently unavailable; summarization steps are skipped until it returns. Pipeline ingestion, scoring, and theme-scan continue without it.

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

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.