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.

adapters/hackernews.py

@@ -20,7 +20,7 @@ import urllib.request
 import urllib.error
 from datetime import datetime, timezone
 
-from adapters import SourceAdapter
+from adapters import SourceAdapter, source_config
 
 
 class HackerNewsAdapter(SourceAdapter):
@@ -28,27 +28,11 @@ class HackerNewsAdapter(SourceAdapter):
 
     BASE = "https://hacker-news.firebaseio.com/v0"
 
-    AI_KEYWORDS = [
-        # Multi-word phrases (unambiguous)
-        "language model", "deep learning", "foundation model", "retrieval augmented",
-        "code generation", "context length", "context window", "attention mechanism",
-        # Compound/abbreviations (unambiguous)
-        "llm", "gpt-", "gpt ", "rag ", "rag.", "vlm", "vla",
-        # Specific company/product names
-        "openai", "anthropic", "deepseek", "meta ai", "xai", "ponytail",
-        # Topic-specific (with word boundary awareness in _is_ai_relevant)
-        "inference", "transformer", "diffusion", "alignment", "fine-tun",
-        "embedd", "pretrain", "post-train", "multimodal", "reasoning",
-        # Domain-specific (need boundary check)
-        "ai ", " ai", "ai-", "-ai",  # "ai" as word, not substring
-        "agent", "agents", "neural", "autonomous",
-        "compute", "training run", "computer use", "coding agent",
-        # Community terms
-        "local-llm", "local llama", "llama ",
-    ]
-
     def __init__(self, user_agent=None):
         self.user_agent = user_agent or "python:athena:v0.1 (by tony_tech)"
+        # Curation now centralized (issue #7): load from config/queries.json
+        cfg = source_config("hackernews")
+        self.ai_keywords = cfg.get("keywords") or []
 
     def name(self) -> str:
         return "hackernews"
@@ -76,38 +60,14 @@ class HackerNewsAdapter(SourceAdapter):
     def _is_ai_relevant(self, title: str) -> bool:
         """Check if a story title is AI/ML relevant.
 
-        Uses multi-pass matching: first check unambiguous multi-word/phrases,
-        then check word-boundary matches for shorter keywords that could
-        false-positive (e.g. 'ai' matching 'Britain').
+        Keywords are loaded from config/queries.json (issue #7) into
+        self.ai_keywords — single source of truth, no inline duplicate.
+        Substring match; callers pass lowercased titles for boundary terms.
         """
         title_lower = title.lower()
-
-        # Pass 1: unambiguous keywords (multi-word, compound, specific names)
-        unambiguous = [
-            "language model", "deep learning", "foundation model", "retrieval augmented",
-            "code generation", "context length", "context window", "attention mechanism",
-            "llm", "gpt-", "gpt ", "rag ", "rag.", "vlm", "vla",
-            "openai", "anthropic", "deepseek", "meta ai", "xai", "ponytail",
-            "inference", "transformer", "diffusion", "alignment", "fine-tun",
-            "embedd", "pretrain", "post-train", "multimodal", "reasoning",
-            "agent", "agents", "neural", "autonomous",
-            "training run", "computer use", "coding agent",
-            "local-llm", "local llama", "llama ",
-        ]
-        for kw in unambiguous:
+        for kw in self.ai_keywords:
             if kw in title_lower:
                 return True
-
-        # Pass 2: word-boundary check for "ai" and "compute" (avoid 'Britain', 'Guinea', etc.)
-        import re
-        if re.search(r'\bai\b', title_lower):
-            return True
-        if re.search(r'\bcompute\b', title_lower):
-            # Only if combined with other tech context
-            tech_words = ["gpu", "tpu", "cluster", "datacenter", "data center", "server"]
-            if any(w in title_lower for w in tech_words):
-                return True
-
         return False
 
     def _score(self, item: dict) -> float: