Thank you for your advice.
16 karma · joined February 21, 2026
News articles about the same event tend to share named entities (people, places, organizations), numbers, and factual structure even across languages. "EU approves AI regulation" is a factual statement that embeds similarly regardless of language. This is very different from, say, opinion pieces or cultural commentary where idioms and local framing would diverge more.
That said, similarity alone isn't enough. The real reliability comes from non-semantic constraints layered on top:
- Time gap ≤ 18 hours between article and story — prevents "same topic, different month" false merges
- Story age ≤ 36 hours — old stories stop absorbing new articles
- Two-pass design — matching against refined story embeddings (average of recent articles) is more stable than raw article-to-article comparison
Where it does break: regional stories with heavy local context. A Japanese domestic politics article and an English wire service summary of the same event sometimes land just below threshold because the framing is so different. I accept some missed merges there rather than lowering the threshold and getting false positives.
No per-language thresholds so far — the embedding model (Qwen3) seems to normalize well across the languages I cover. But I wouldn't be surprised if that changes when adding languages with less training data representation.
Happy to answer questions about the pgvector setup, Cloudflare Workers constraints, or the clustering algorithm tuning.
The idea is simple: every day, AI scans 30,000+ articles from 180+ sources across 17 languages, scores them on actual global significance (not clicks), and picks 5–7 that matter most. You read them, see "You're all caught up," and you're done. The whole thing takes about 3 minutes.
A few things I spent the most time on:
- The scoring model. Each story gets a 0–10 significance score based on global impact, novelty, disruption potential, and long-term importance. A geopolitical shift that nobody's tweeting about will rank higher than a viral celebrity story. I'm still tuning this — would love feedback.
- Event narratives. Big stories don't start from scratch daily. When something like a trade war or election develops over weeks, the system builds a 4-part briefing (situation → latest → background → outlook) so you get context without re-reading everything.
- Multilingual generation. Summaries aren't translated — they're generated natively in each language. Currently supports EN, ZH, JA, KO, ES, PT, FR, DE, AR, RU, and 7 more. Same stories, same quality across all.
Stack: Next.js on Cloudflare Pages, Cloudflare Workers for the backend, D1 (SQLite) for storage. Fully serverless.
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