Score against original user intent on Reddit, remove the artificial low-end relevance floor, and make Polymarket semantics dominate generic market quality signals.
Also apply the relevance filter to Polymarket and update the affected cross-source tests.
Validation: uv run python -m unittest
- Remove duplicate detect_query_type from query.py (divergent 5-type version);
canonical 7-type version lives in query_type.py
- Fix reddit.py import to use query_type.detect_query_type
- Clean unused STOPWORDS/SYNONYMS/tokenize imports from youtube_yt, instagram,
tiktok, scrapecreators_x, bird_x after relevance consolidation
- Fix _relevance_filter default from 0.7 to 0.0 (items without relevance
should not silently pass the filter)
- Remove --dateafter from yt-dlp (returns 0 results for evergreen topics)
- Remove restrictSearchableAttributes from HN search (misses Ask/Show HN)
- Lower HN points filter from >5 to >2 (avoids filtering niche posts)
- Add error logging to select_openai_model HTTP failures
- Remove mise.toml and internal planning doc from repo
- Update module docstrings to describe current purpose, not migration history
- Update tests to import from canonical relevance module
- hackernews: use extract_core_subject instead of raw topic, add
points>5 filter and restrictSearchableAttributes=title to reduce
noise from URL-match and low-signal posts
- youtube: add --dateafter parameter to yt-dlp for server-side date
filtering (Python soft filter still handles fallback)
- reddit: skip opinion/review query variant for how_to/comparison
queries where it adds noise
- bird_x: add OR-group retry with compound terms before falling back
to word-dropping (uses X OR operator for multi-concept queries)
- query.py: add detect_query_type() and extract_compound_terms()
- bird_x: parse_bird_response now accepts query param and computes
token_overlap_relevance against tweet text
- reddit: _normalize_post computes relevance from query vs title+selftext
- hackernews: blends 60% Algolia rank + 40% token overlap + engagement
This makes the 45%-weight relevance factor in score.py actually
differentiate results instead of being a constant.
Replace duplicated _extract_core_subject() in bird_x, reddit, youtube_yt,
tiktok, instagram, bluesky, and scrapecreators_x with thin wrappers that
delegate to query.extract_core_subject() with platform-specific noise sets.
Each module preserves its current behavior exactly:
- bird_x: max_words=5, strip_suffixes=True, full noise set
- youtube_yt: keeps tips/tricks/tutorial/guide/review (content types)
- reddit: preserves original smaller noise set
- tiktok/instagram: same small noise set
- bluesky/scrapecreators_x: minimal noise set
Existing tests pass without modification since _extract_core_subject()
still exists as a callable on each module.
- New scripts/lib/reddit.py: multi-query expansion, global search,
subreddit discovery, targeted subreddit search, comment enrichment
- 68 results in 17s vs ~15 results in 60-90s (OpenAI)
- Cost: ~$0.02/search vs $0.03-0.10 (15-50x cheaper)
- Real engagement data (score, comments, dates) from API
- No more 429 rate limits on comment enrichment
- Falls back to OpenAI if SCRAPECREATORS_API_KEY missing
- Registered as last30daysbeta for parallel local testing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Matt Van Horn