New "podcasts" source that discovers podcast content by scanning transcripts
from LLM-resolved YouTube channels. Finds content invisible to title-based
search — Acquired's "The NFL" episode mentions Taylor Swift 18x, ESPN 117x,
Netflix 102x, none in the title.
Architecture:
- LLM resolves 6-12 podcast channel @handles per topic
- Engine fetches recent episodes via yt-dlp (no video download)
- Downloads auto-captions and greps for topic keywords
- Episodes with 5+ mentions become podcast results with highlights
- Runs in parallel, ~15-20s latency, invisible in 3-min research run
Pipeline integration:
- New source module: scripts/lib/podcast_yt.py
- Registered in pipeline, normalizer, signals, planner, render
- CLI flag: --podcast-channels=AcquiredFM,lexfridman,...
- SOURCE_QUALITY: 0.88 (above YouTube's 0.85)
- Opt-in via INCLUDE_SOURCES=podcasts or --search=podcasts
Zero new API keys. Zero new dependencies. Reuses yt-dlp + transcript pipeline.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The dedup hot path recomputed normalize_text() 4 times per comparison
and recomputed item_text() on every inner-loop iteration. Pre-computing
n-gram sets and token sets into a _PreparedText cache cuts dedup time
by 6x (2.16s to 0.39s on 300 unique items).
Bird handle searches spawned one Node process per handle sequentially.
Now uses ThreadPoolExecutor so N handles run concurrently. Same pattern
applied to YouTube comment enrichment (was serial, Reddit was already
parallel) and the retry-thin-sources phase in the pipeline.
Clustering now pre-computes candidate text and uses prepared_similarity
for the O(n^2) grouping and MMR representative selection loops.
Minor: _is_wsl() cached with lru_cache, Bundle.add_items() uses
extend() instead of list concatenation.
End-to-end: 5.2s -> 3.7s (29% faster) on a typical 4-source query.
Add Xquik (xquik.com) as a new X/Twitter search source that uses a REST
API with full engagement metrics (likes, retweets, replies, quotes,
views, bookmarks). Uses stdlib urllib only -- no new dependencies.
- scripts/lib/xquik.py: source module with search, parse, query expansion
- tests/test_xquik.py: 32 unit tests covering all functions
- env.py: XQUIK_API_KEY config and availability check
- pipeline.py: source registration and retrieve dispatch
- normalize.py: reuses _normalize_x (same item format as Bird)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Matt Van Horn