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We built a searchable database of 25K verified engineering fixes — here's why

Every developer has spent hours debugging an error that someone else already solved. Stack Overflow is fragmented. GitHub issues are buried. AI models hallucinate fixes. We built FIXGRAPH to fix this. What it is FIXGRAPH is a…

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Every developer has spent hours debugging an error that someone else already solved. Stack Overflow is fragmented. GitHub issues are buried. AI models hallucinate fixes.



We built FIXGRAPH to fix this.






What it is



FIXGRAPH is a searchable database of 25,000+ verified engineering fixes, indexed by error message and technology stack. You search for your error — Prisma connection refused, Docker OOM, Vercel deploy failed, Redis ECONNREFUSED — and get step-by-step solutions that actually worked, with trust scores.



Live: https://fixgraph.netlify.app






Why we built it



Two reasons:



1. Human debugging is repetitive. The same 500 errors account for 80% of engineering time lost. Every team rediscovers them from scratch.



2. AI agents need verified fixes, not hallucinated ones. When a coding agent hits an unknown error, it guesses. We wanted to give it a lookup table of what actually works.






The MCP server



The most useful part: we published FIXGRAPH as an MCP (Model Context Protocol) server so AI coding agents can query it mid-task.




npx fixgraph-mcp






Wire it into Claude, Cursor, or any MCP-compatible agent. When it hits a known error, it looks up the verified fix instead of hallucinating.




{
"mcpServers": {
"fixgraph": {
"command": "npx",
"args": ["-y", "fixgraph-mcp"]
}
}
}









What's in the database





  • 25,009 issues covering Prisma, Docker, Redis, Vercel, PostgreSQL, Next.js, AWS, and more


  • 25,047 fixes with step-by-step solutions


  • 4,962 verifications — community-confirmed working fixes

  • Trust scores on every fix

  • Full-text + semantic search






How to use it



As a developer: Search by error message at https://fixgraph.netlify.app



As an AI agent: npm install fixgraph-mcp and add to your MCP config






What's next




  • Rate-limited API for high-volume agent usage

  • Dataset exports for teams training on engineering knowledge

  • Submission pipeline so teams can contribute their own fixes



If you've ever spent 3 hours on an error that had a 5-minute fix somewhere on the internet — this is for you.



Try it: https://fixgraph.netlify.app

MCP package: npm install fixgraph-mcp

1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Remote Code Execution (RCE) Defense
1 Warnungen
title: Detect Exploitation - We built a searchable database of 25K verified engineering fixes — here's why
id: 21e404c7-660c-48ad-9027-5a791f153627
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 = "We built a searchable database" ascii wide
    condition:
        any of them
}
Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("We built a searchable database of 25K ve")
| 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: "*We built a searchable database of 25K ve*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "We built a searchable database of 25K ve"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

2. Cyber Threat Intelligence & Forensik

🎯
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
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich We built a searchable database of 25K ve.... 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.
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