How /last30days Scores Research Results by Real User Engagement Instead of SEO
Problem
Google ranks by SEO signals, backlinks, and editorial curation. For what’s happening right now — this month, this week, today — SEO-centric ranking misses the real signal: what real people are actually talking about and engaging with. A blog post nobody read can rank #1 because it has good SEO. A Reddit thread with 1,500 upvotes might not appear on the first page.
The Scoring System
/last30days scores every result by real user engagement signals. Each platform contributes its native signal:
| Platform | Scoring Signal | What It Measures |
|---|---|---|
| Upvote count on posts and top comments | Community consensus | |
| X | Likes, retweets, replies | Real-time engagement |
| YouTube | View count, like ratio | Watch-time attention |
| TikTok | View count, engagement rate | Viral cultural signal |
| Hacker News | Points, comment count | Tech community interest |
| Polymarket | Odds percentage backed by real money volume | Money-backed belief |
How Scoring Works
The scoring pipeline has three stages:
1. Native Scoring. Each result is scored within its own platform. A Reddit post with 2,300 upvotes gets a high native score. A YouTube video with 3.6M views gets a high native score. These are raw, unnormalized signals.
2. Cross-Platform Fusion. The fusion engine (weighted_rrf — Reciprocal Rank Fusion) combines scores across platforms. This normalizes the different scales: a Reddit upvote is not the same as a YouTube view, so the fusion algorithm weights them appropriately. The fusion.py module implements this.
3. AI Re-Ranking. An optional AI judge re-ranks by relevance to the query. The final ranking favors three things in order: higher engagement, more recent, and more relevant to the topic.
Raw results from each platform │ ▼┌─────────────────────┐│ Native platform │ Upvotes, likes, views, odds│ scoring │ per-platform normalization└──────────┬──────────┘ │ ▼┌─────────────────────┐│ Cross-platform │ weighted Reciprocal Rank Fusion│ fusion (fusion.py) │ combines different signal types└──────────┬──────────┘ │ ▼┌─────────────────────┐│ AI re-ranking │ Optional judge re-ranks│ (rerank.py) │ by relevance to query└──────────┬──────────┘ │ ▼ Final ranked resultsWhy Engagement Beats SEO
A Reddit thread with 1,500 upvotes is a stronger signal than a blog post nobody read. A TikTok with 3.6M views tells you more about cultural relevance than a press release. Polymarket odds backed by $66K in volume beat a pundit’s guess.
The synthesis ranks by social relevancy, not SEO relevancy. This matters most for current topics — breaking news, product launches, controversies, trends — where editorial curation hasn’t had time to catch up.
Summary
In this post, I explained how /last30days’s scoring system surfaces what real people are actually engaging with. The key point is that social relevancy beats SEO relevancy for understanding what’s happening right now — engagement signals from real users are harder to game than backlinks.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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