A developer going by fflluuxxuuss posted Kibitz on r/vibecoding — a local tool that treats multiple LLMs as coworkers who review each other’s output. The pitch: instead of trusting one model’s answer, have ChatGPT and Gemini fact-check what Claude generates (or any combination), all running on your machine.
The initial use case targets ComfyUI code and workflows, but the concept is model- and task-agnostic:
- Multi-model cross-checking — one LLM generates, another critiques, reducing single-model hallucination risk
- Local execution — runs on your machine, no cloud relay between models
- Claude, GPT, Gemini support — uses the three major model families as interchangeable “coworkers”
The idea of adversarial or cooperative multi-agent setups isn’t new — Microsoft’s AutoGen and CrewAI both explore multi-agent orchestration. Kibitz narrows the scope to something specific and practical: code review across models. If Claude writes a ComfyUI node connection wrong, GPT or Gemini can flag it before you run the workflow and get a cryptic error.
This matters most for vibe coders working in domains where they can’t easily verify output themselves. ComfyUI workflows are a good example — node graphs get complex fast, and a single wrong connection silently produces garbage output. Having a second model sanity-check the first is a cheap substitute for domain expertise.
NOTE
The Reddit post is light on implementation details — no GitHub link, no architecture breakdown, no benchmarks on how often the second model actually catches errors the first model misses. Treat this as an early-stage community project, not a production tool.
Kibitz is worth watching if you’re already juggling multiple API keys and want them earning their keep. The multi-model review pattern — generate with one, verify with another — is a workflow more vibe coders should experiment with, even outside this specific tool. The original post has the details available so far.