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Vietnamese news trends tracked by LLM and HDBSCAN system

By PulseAugur Editorial · [1 sources] ·

A developer has created a system called "Tin tức hôm nay có gì?" to track trends in Vietnamese news using embeddings, HDBSCAN, and an LLM. The system ingests articles from approximately 95 Vietnamese RSS feeds, converts their titles and descriptions into vectors using Jina-Embeddings-v3 or LaBSE, and then clusters these vectors with HDBSCAN to identify related stories. An LLM is employed to label these topics and provide insights, with the goal of understanding which stories are gaining or losing traction. AI

IMPACT Provides a novel approach to analyzing news trends by leveraging LLMs and clustering algorithms for topic identification and lifecycle tracking.

RANK_REASON Developer built a tool using existing technologies to solve a specific problem.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vietnamese news trends tracked by LLM and HDBSCAN system

How we ranked this

Signal score
17 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Developer built a tool using existing technologies to solve a specific problem.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Mike Vu ·

    Tracking Vietnamese news trends with embeddings, HDBSCAN and an LLM

    <p>A big event is usually covered by dozens of outlets at once, under different headlines. Reading article by article, it's hard to see the overall picture. The more useful question isn't "what's in the news today", but: <strong>which stories are heating up, which are cooling off…

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