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Intelligence View

⚡ tsecurity.de Intelligence

How I Built Semantic Discussion Clustering Without Embeddings (and Why It Was Good Enough)

The Problem I wanted to monitor discussions around products, bugs and trends across communities. Examples: Reddit Hacker News GitHub Issues Forums Most solutions rely on: OpenAI embeddings Pinecone Weaviate Expensive vector…

Sichere Programmierung dev.to 26.05.2026, 22:40 Uhr 2 Min. Lesezeit Cloud & IT-Enterprise

↗ Quelle (dev.to)

Reagiere als Erste:r — dein Feedback zählt!

The Problem

I wanted to monitor discussions around products, bugs and trends across communities.

Examples:

Reddit

Hacker News

GitHub Issues

Forums

Most solutions rely on:

OpenAI embeddings

Pinecone

Weaviate

Expensive vector search

For a side project, that cost didn't make sense.

My Goal

I wanted something that could:

Run on a cheap VPS

Process thousands of posts

Group similar discussions

Generate summaries

Without paying for embeddings.

The Approach

Instead of embeddings, I used:

Post

↓

Tokenize

↓

TF-IDF Vector

↓

Cosine Similarity

↓

Cluster

The implementation ended up surprisingly simple.

The Hardest Part Wasn't Coding

The hardest problem was choosing a similarity threshold.

I tested:

0.15

0.20

0.25

0.30

0.35

Results:

0.35

too strict

almost every thread became its own cluster

0.15

too loose

unrelated discussions merged together

0.25

good enough

This single number took longer to tune than the rest of the project.

Architecture

Crawler

↓

BullMQ

↓

TF-IDF Engine

↓

Cluster Engine

↓

PostgreSQL

↓

Groq Summary Generator

Stack:

Node.js

PostgreSQL

Redis

BullMQ

Groq

Vanilla JS

Why I Chose Groq

I didn't need GPT-4 quality.

I needed:

fast summaries

low cost

easy API

Groq was sufficient.

What Surprised Me

For discussion monitoring, TF-IDF works better than people expect.

Most conversations contain repeated terms:

rate limits

api quota

billing

token cost

Those keywords alone are often enough to group discussions accurately.

Current Results

Example topic:

OpenAI Rate Limits

Generated clusters:

API throttling complaints

Increase request discussions

Azure OpenAI alternatives

Workarounds and solutions

The summaries are generated automatically.

Future Plans

I'm considering adding:

Hacker News

GitHub Issues

RSS feeds

Telegram alerts

Trend detection

Feature request extraction

Repository

GitHub:

https://github.com/melyx-id/discussion-radar

I'd love feedback from people building monitoring or intelligence tools.

IR-PLAYBOOK-VULN-REMEDIATIONMEDIUM

Schritte anzeigen

SOC Incident Playbook: Vulnerability Remediation & Verification

Asset Inventory

Asset-Datenbank nach betroffenen Komponenten, Versionen und exposed Ports abfragen.

nmap -sV --script vuln -p- <target-network>

Exploit MonitoringSofort

SIEM-Regeln und IDS-Signaturen (Snort/Suricata) auf Angriffsversuche schärfen.

tail -f /var/log/suricata/fast.log | grep <cve>

Patch DeploymentSofort

Patch oder offiziellen Workaround gemäß Hersteller-Empfehlung einspielen.

Check Vendor Security Advisory Bulletin

1-Click Detection Engineering: Multi-Dialect SIEM Rules5 Formate

Regeln anzeigen

Syntax validiert (0 Fehler)

title: Detect Exploitation - How I Built Semantic Discussion Clustering Without Embeddings (and Why It Was Good Enough)
id: e83f4a0c-271e-4565-86f8-1bf74002e84c
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access

Syntax validiert (0 Fehler)

rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "How I Built Semantic Discussio" ascii wide
    condition:
        any of them
}

Syntax validiert (0 Fehler)

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("How I Built Semantic Discussion Clusteri")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count

Syntax validiert (0 Fehler)

message: "*How I Built Semantic Discussion Clusteri*"

Syntax validiert (0 Fehler)

CommonSecurityLog
| where Message has "How I Built Semantic Discussion Clusteri"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

🎯

MITRE ATT&CK Matrix Navigator 14 Taktiken

Reconnaissance

Resource Development

Initial Access

Execution

Persistence

Privilege Escalation

Defense Evasion

Credential Access

Discovery

Lateral Movement

Collection

Command and Control

Exfiltration

Impact

💡 Tipp: Klicken Sie auf eine TTP, um Gegenmaßnahmen und SIEM-Erkennungsregeln anzuzeigen.Enterprise Matrix v14.1

🚀 Auf tsecurity.de entdecken

◀ VORHERIGER BEITRAG 1 Min

USN-7972-2: OpenCC vulnerability

Unix & Linux Server

NÄCHSTER BEITRAG 10 Min

An Intelligence Briefing for the Port of Rotterdam, from a Single Prompt

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Blättern: Pfeiltasten [ ← ] [ → ] · Tasten j / k · mobil: Wischen

Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich How I Built Semantic Discussion Clusteri.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

⚡ Empfohlene Sofortmaßnahmen

1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.

🔗 Semantisch verwandte Zero-Days MariaDB 11.7 VEC

Schnell-Prompts für Analysten

⚠️ DEFANGED SANDBOX VIEW (tsecurity.de): Alle Netzwerkindikatoren und URLs wurden neutralisiert (2 Indikatoren entschärft). Nur zur defensiven Analyse!

Format: python

#!/usr/bin/env python3
# Exploit Title: PoC for Target
import socket, sys
target = '192[.]168[.]1[.]10'
url = 'hxxp://target[.]local/vuln'
s = socket.socket()
s.connect((target, 80))
s.send(b'GET / HTTP/1.1\r\nHost: target.local\r\n\r\n')

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