Nicheloom

Market intelligence for builders — see what's gaining traction before it's crowded.

Real-time system that tracks how news spreads across 200k websites

Details

External ID
46053076
Source
HN
Company
—
Product
Real-time system that tracks how news spreads across 200k websites
Website domain
yandori.io
Launched
Nov. 26, 2025
Cohort
—
Upvotes
256
Upvotes percentile
0.9672489082969432
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I built a system that monitors ~200,000 news RSS feeds in near real-time and clusters related articles to show how stories spread across the web.It uses Snowflake’s Arctic model for embeddings and HNSW for fast similarity search. Each “story cluster” shows who published first, how fast it propagated, and how the narrative evolved as more outlets picked it up.Would love feedback on the architecture, scaling approach, and any ways to make the clusters more accurate or useful.Live demo: https://yandori.io/news-flow/

Enrichment

Theme
Hacker News clients, datasets, and tools
Vertical
Media & entertainment
Function
Analytics & BI
Audience
B2B
AI stance
Not AI
Project type
Commercial product
Normalized one-liner
news spread tracking across websites
Manually corrected
False

Could you build this?

No Ingesting, parsing, embedding, and clustering 200,000 live RSS feeds in near real-time requires substantial distributed data streaming infrastructure and high-throughput vector pipeline management.

What it would actually take: A production system requires a distributed crawler (e.g., Go or Rust with worker pools) capable of polling hundreds of thousands of feeds while respecting rate limits, coupled with Kafka or Redpanda for message streaming. The embedding and clustering pipeline needs batched GPU inference (Arctic embeddings), incremental HNSW graph indexing in Milvus or Qdrant, and real-time graph traversal to calculate story propagation velocity. Operating this requires senior data engineering and distributed systems reliability expertise.

Discussion

20 comments analyzed.

Competitors mentioned: Associated Press news aggregation, Private versions at large organizations, websitelaunches (creator's own project)

Concerns raised: AI-generated content detection issues, Timezone handling complexity with dynamic data, Inaccurate published dates from sources, High cost of Twitter/social media API access, Spam and non-news content (e.g., Black Friday deals) getting through

Feature requests: Network graphs or timeline visualizations of news propagation, Establish equivalency between duplicate articles instead of filtering, Hot-linked titles and copy-paste functionality, Social media data integration, Multi-language support

Competitors

Other products that read as similar to this one — 70 launches clear the similarity bar, closest 8 shown.

Attention rank: #6 of 71 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 25 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a analytics & bi tool for Legal yet.