I got tired of re-explaining context to my AI every single prompt. It forgets what it did 5 minutes ago. It stops after one step and waits. You end up babysitting it more than coding yourself. So I built Long-Horizon — a skill that t…

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I got tired of re-explaining context to my AI every single prompt. It forgets what it did 5 minutes ago. It stops after one step and waits. You end up

babysitting it more than coding yourself.

So I built Long-Horizon — a skill that turns any AI coding tool into an autonomous agent with a persistent graph memory.

What it does

You give it one task — "Build a REST API with auth"

It decomposes into subtasks and executes them in a loop — no prompting between steps

It writes knowledge to disk as a connected graph — decisions, lessons, patterns, all linked

It resumes perfectly after any interruption — reads its own state and picks up exactly where it stopped

The graph brain

Every decision, lesson, and pattern becomes a node. Nodes connect via typed edges (leads_to, caused_by, learned_from). Over time, the AI builds a web

of project knowledge it can traverse for context.




           ┌─────────┐


      ┌────│  ROOT   │────┐


      │    │(project)│    │


      ▼    └─────────┘    ▼


  ┌────────┐         ┌────────┐


  │decision│─leads─▶ │  task  │


  │"Use JWT"│        │"Build  │


  └───┬────┘         │ API"   │


      │              └───┬────┘


      ▼                  ▼


  ┌────────┐         ┌────────┐


  │ lesson │◀─related─│pattern │


  └────────┘         └────────┘

Live viewer

Real-time cyberpunk visualization that shows the brain growing as the AI works:

Neon glowing nodes with particle trails along edges

Unlimited zoom/pan — scroll to zoom, drag to navigate

Sound effects when new nodes appear

Runs on localhost:3333

Zero dependencies, ~38KB

No vector DB. No cloud. No API keys. Pure Node.js, filesystem only. Works offline.

Works with everything

One command installs for your tool:

npx long-horizon init


  npx long-horizon adapt cursor    # or: windsurf, claude, aider, codex, all

Also ships an MCP server with 11 tools for direct AI integration.

It's always-on

Once installed, you don't need to say "use Long-Horizon" or prefix your prompts. The AI reads the skill file and operates autonomously by default. Just

talk normally:

Build a user dashboard with charts and dark mode.

The AI loops until done.

Links

GitHub: github.com/justnishh/long-horizon

Install: npx long-horizon init

License: MIT

Would love feedback. What would make this more useful for your workflow?

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 - I built a "brain" for AI coding agents — it never forgets and never stops
id: 83811def-070b-4b2f-a2eb-2a1b5745d9b3
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 = "I built a \"brain\" for AI codin" ascii wide
    condition:
        any of them
}

Syntax validiert (0 Fehler)

index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("I built a brain for AI coding agents  it")
| 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: "*I built a brain for AI coding agents  it*"

Syntax validiert (0 Fehler)

CommonSecurityLog
| where Message has "I built a brain for AI coding agents  it"
| 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

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IT Security Nachrichten

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Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I built a "brain" for AI coding agents —.... 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.
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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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