Zum Hauptinhalt springen
Echtzeit-Radar & Feeds
Alle RSS Feeds ➔
👥 Community & Social
Windows Tipps & SecurityGrafikkarte vor Überhitzung schützen: So geht’s(25.09.2026 um 08:00 Uhr)
••••••••••
Windows Tipps & SecurityGrafikkarte vor Überhitzung schützen: So geht’s(25.09.2026 um 08:00 Uhr)
••••••••••
Intelligence View
⚡ tsecurity.de Intelligence

I built a local AI code review dashboard because no one reviews my code

I'm a solo developer. I work on multiple projects, ship code daily, and nobody reviews any of it. I looked at existing AI code review tools. They all had the same problems: Need API keys (pay per token) Send your code to external…

0
↗ Quelle (dev.to)
Reagiere als Erste:r — dein Feedback zählt!



I'm a solo developer. I work on multiple projects, ship code daily, and nobody reviews any of it.

I looked at existing AI code review tools. They all had the same problems:




  • Need API keys (pay per token)

  • Send your code to external servers

  • Require org-level GitHub setup

  • Need accounts, onboarding, configuration



Meanwhile, I'm already paying for Claude Pro. I have Ollama running locally. These tools sit there unused while I push unreviewed code.



So I built CodeLens.





What it is



A self-hosted dashboard that watches your local git repos, auto-detects whatever AI CLIs you have installed, and runs automated code reviews. No API keys. Fully local with Ollama, or use your existing cloud AI subscriptions directly without a middleman.





How it works




  • Start the app (npm run dev)

  • Add a repo by browsing to its folder

  • CodeLens auto-detects your installed AI tools (Claude Code, Ollama, Gemini, OpenAI)

  • It immediately reviews the last 30 days of commits

  • Going forward, it polls every 60 seconds for new commits





The review page



This is the core of the product. Three panels:



Left: list of findings. Severity dots, category, file path. Click one to select it.

Centre: the actual code diff with line numbers. Green additions, red removals. Coloured dots mark lines with issues.

Right: full description, suggested fix, and a ready-to-paste prompt for your AI chat.



The idea is: CodeLens finds the bug, tells you what's wrong, and gives you a prompt to paste into Claude/ChatGPT to fix it. For vibe coders, it's copy-paste-done.





Features I didn't plan but ended up building




  • Deep review: scans your entire codebase, not just the latest commit. Good for auditing a project you inherited.

  • Fix tracking: when a new commit modifies a file and the AI no longer flags the same issue, the old finding automatically gets marked as "fixed."

  • Export as Markdown: download any review as a formatted .md file. Useful for PRs or client documentation.

  • 500 fun loading messages: "Judging your variable names," "Wrestling with regex," "Questioning life choices." They rotate with pastel colours while the review runs.

  • Notification bell: tracks all review completions across repos with unread counts.







The self-review



The most satisfying moment was pointing CodeLens at its own repo. It found real bugs:




  • A full table scan loading every finding into memory

  • Timer leaks in the provider adapters (setTimeout never cleared on success)

  • A path traversal vulnerability in AI-generated file paths

  • A race condition where two concurrent review triggers could create duplicate records

  • Silent error swallowing that hid database failures



I fixed every finding it reported. Then it reviewed those fixes and found more issues. We went through 5 rounds before it converged to only info-level style suggestions.





Stack




  • Next.js 16 (App Router) with Turbopack

  • SQLite via Drizzle ORM (zero setup, file-based)

  • Custom component library (no browser defaults)

  • Google Sans typography

  • Lucide icons



Try it




git clone https://github.com/sebiomoa/codelens.git
cd codelens
npm install
npm run db:migrate
npm run dev






Open localhost:3377. Add a repo. That's it.

It's MIT licensed. If you're a solo dev or freelancer who wants a second pair of eyes on your code without the overhead, give it a try.

GitHub: https://github.com/sebiomoa/codelens

1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Vulnerability Remediation & Verification
Syntax validiert (0 Fehler)
title: Detect Exploitation - I built a local AI code review dashboard because no one reviews my code
id: 3a6b4f89-536e-4cdc-9228-73b20574f274
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-26
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 = "2026-09-26"
        description = "YARA Signature for "
    strings:
        $str = "I built a local AI code review" 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 local AI code review dashboard")
| 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 local AI code review dashboard*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "I built a local AI code review dashboard"
| 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

CTI Threat Relationship Graph3 Knoten / 2 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
🎯
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 I built a local AI code review dashboard.... 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
Ähnliche Beiträge
🔍 Verwandte News

Auch interessante Nachrichten I built a local AI code review dashboard because no one reviews my code

Thematisch verwandte Begriffe: built, local, code, review · 6 Treffer

Laden...

Beiträge werden geladen ...

Laden...

Videos werden geladen ...

Laden...

Beiträge werden geladen ...

Laden...

Videos werden geladen ...

Laden...

Beiträge werden geladen ...

Laden...

Videos werden geladen ...

Laden...

Beiträge werden geladen ...

Laden...

Videos werden geladen ...

Zum Aktualisieren ziehen
ZERO-DAY CVE-2026-100532 | @openclaw/whatsapp (npm) before 2026.8.1 exposes the WhatsApp login too…
Advisory →
tsecurity.de Icon
Offline-Lesen, Eilmeldungen & 0ms Ladezeit

Installiere tsecurity.de direkt auf deinen Home-Bildschirm für das ultimative Vollbild-Magazinerlebnis ohne Browser-Leisten.

Nächster Beitrag