Th

ThreatCluster – Automatically cluster cybersecurity news

Hacker News

ThreatCluster – Automatically cluster cybersecurity news

I built ThreatCluster after getting frustrated with how scattered cybersecurity intelligence is. When a major breach or vulnerability hits, you end up reading the same story across 10+ different security blogs, each with slightly different details, but no way to see the complete timeline or understand the full scope. ThreatCluster automatically groups related cybersecurity articles using semantic clustering, so instead of reading fragments, you get one comprehensive view of each threat. It tracks everything from APT campaigns to vulnerability disclosures to ransomware attacks. You can try it without signing up – just visit https://threatcluster.io/trending to see current clustered threats. The free tier lets you browse all clusters, see threat scores (based on recency, source credibility, and severity), and follow the timeline of how stories develop. Key features you can test: - Browse automatically clustered threat intelligence - See threat scores and similarity percentages - Follow story timelines as new articles get added - Filter by entity types (APT groups, companies, malware families) - Follow entities and add them to custom feeds - AI-generated summaries of threats - CVEs, IPs, Domain, and File Hash intelligence The technical challenge was building clustering that works in real-time as articles come in, ensuring related articles are correctly clustered, while handling the noise and duplicate content that’s common in cybersecurity news. Currently processing 400+ articles daily from security vendors, researchers, and news sources. Would love feedback from anyone in cyber security or just curious about how threat intelligence works. Thanks!

Share card

Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
23%23% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

WhiteRabbitNeo
WhiteRabbitNeo27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Uncensored AI for Cybersecurity

Product Hunt+66
Cy
Cyberintel.info Cybersecurity News Aggregation Using LLM and NER39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cyberintel.info Cybersecurity News Aggregation Using LLM and NER

Hacker News2
Binarly
Binarly42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI & Cybersecurity Platform for Firmware

Indie Hackerscommitment-full-time
Au
Automatically Controlling a Dehumidifier with a Nest57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically Controlling a Dehumidifier with a Nest

Hacker News1
Au
Automatically Thank Patreon Backers with OpenFaaS38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically Thank Patreon Backers with OpenFaaS

Hacker News11
So
Sorts Evernotes, Automatically57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sorts Evernotes, Automatically

Hacker News3
TempHub
TempHub41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically call your temps, one by one

Indie Hackers1b2b
Fews
Fews

Get news in a nutshell

BetaList
Ha
HackerNews News Recommender59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

HackerNews News Recommender

Hacker News97
Sh
Show HN : Distributed News63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Show HN : Distributed News

Hacker News2