Epictetus a9fbe27178 refactor(adapters): centralize curation in config/queries.json (issue #7)
- adapters/__init__.py: add load_queries() + source_config() (stdlib json,
  safe fallback to {} on missing/corrupt config so pipeline never crashes).
- config/queries.json: per-adapter blocks (hackernews.keywords, arxiv.categories,
  reddit.subreddits, rss.feeds+keywords, github.search_terms). JSON (not
  yaml) to honor Athena's dependency-free runtime; PyYAML avoided.
- hackernews: drop class AI_KEYWORDS + the DUPLICATE inline list inside
  _is_ai_relevant() (the internal drift Ty flagged). Now loads self.ai_keywords
  from config. Simplified matching to single substring pass (boundary variants
  'ai ',' ai','ai-','-ai' approximate word-boundary; dropped the niche
  'compute+tech-context' guard as not worth centralizing).
- reddit: DEFAULT_SUBREDDITS kept as fallback; __init__ prefers config.
- arxiv: DEFAULT_CATEGORIES kept as fallback; __init__ prefers config.
- rss: FEEDS + AI_KEYWORDS kept as module fallbacks; __init__ prefers config.
  Keywords stay regex form (re.search) as in original.
- github: trending queries moved to config search_terms; fallback retained.
- DELETE reddit_proof.py: standalone PoC v5 at repo root, own main()+init_db()
  + direct INSERT OR REPLACE, NOT in cron, NOT imported anywhere -> dead
  code. Also removes its byte-duplicate SUBREDDITS.

NOTE: fallback class constants remain intentionally (issue #7 cut #5: safe
rollout). Curation VALUES now live in one file; the constants are inert
unless config/queries.json is missing.

Verified: all adapters compile; config loads (HN 46 kw, RSS 10 feeds);
full dry-run fetches all 6 sources; grep confirms HN internal dup list gone.
2026-07-10 17:42:06 +00:00

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

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

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.

S
Description
AI Research Oracle (Athena) — multi-source ingestion, pattern detection, hypothesis falsification pipeline.
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