Ran 15-way blinded comparison (5 topics x 3 versions). CROSS won all 5 topics
(4.74/5.0 avg vs HN 4.10, Base 3.73). Then improved CROSS further:
- dedupe.py: hybrid similarity (token+trigram Jaccard) at 0.40 threshold,
cross-source links went from 3 to 13 items across 5 topics
- render.py: [xref: HN5, HN4] -> [also on: HN, Reddit] for human-readable tags
- youtube_yt.py: SYNONYMS dict so "hip hop" matches "rap" (0.33 -> 0.71 score)
- SKILL.md: instruction #7 tells Claude to lead with cross-platform signals
Validation: improved CROSS scores 4.38/5.0 vs original 3.98 (+0.40), wins 4/5
topics. Biggest gains in specificity (+0.8) and format compliance (+1.0).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube videos now get real relevance scores based on token overlap
between the search query and video title (was hardcoded at 0.7).
Uses ratio overlap with stopword removal, floored at 0.1.
Cross-source linking annotates items that discuss the same story
across different platforms (e.g., Reddit + HN + X). Items get
bidirectional cross_refs displayed as [xref: R3, HN5] in compact
output so Claude can triangulate multi-platform coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add HN search via free Algolia API (no key needed). Two-phase approach:
search for stories, then enrich top ones with comments. Integrated into
the full pipeline (normalize, score, dedupe, render) running in parallel
with Reddit/X/YouTube. Source priority: Reddit > X > HN > YouTube > Web.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube search and transcript extraction runs automatically when yt-dlp
is installed. Searches for topic videos from the last N days, fetches
auto-generated transcripts for top results, and feeds them through the
same scoring pipeline (relevance + recency + engagement) as Reddit/X.
New files:
- youtube_yt.py: search, transcript extraction, VTT cleanup
Modified files:
- schema.py: YouTubeItem dataclass, updated Report
- normalize.py: normalize_youtube_items()
- score.py: YouTube engagement scoring (views-dominated)
- dedupe.py: YouTube deduplication
- render.py: YouTube section in compact output
- env.py: is_ytdlp_available() check
- ui.py: YouTube progress messages
- last30days.py: _search_youtube(), parallel execution with Reddit/X
- SKILL.md: YouTube in stats box, citation priority
- README.md: YouTube docs, yt-dlp requirement, Peter shoutout
Inspired by Peter Steinberger's yt-dlp + summarize toolchain approach.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Research topics across Reddit + X from the last 30 days using
OpenAI and xAI APIs. Features:
- Auto model selection (GPT-5.x, Grok-3)
- Popularity-aware scoring (relevance + recency + engagement)
- Reddit thread enrichment with real metrics
- Near-duplicate detection
- Multiple emit modes (compact, json, context, path)
- 24h caching with --refresh bypass
- NUX for API key setup
- 87 passing unit tests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Matt Van Horn