AI Signal 531
Clean up Claude 5's token vomit with a separate LLM
Vomit translates Claude's raw token output into readable English by routing it through a locally run LLM.
It gives engineers a way to inspect Claude's output without sending data to external services, preserving privacy. However, the translation relies on another model that can hallucinate, is slow, and may lose the original message, so users must weigh these trade-offs.
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Vomit runs entirely locally, requiring no external telemetry or dependencies beyond the chosen LLM.
Setup involves installing the Go binary and initializing a connection to a local LLM such as Llama.app or Ollama.
The translation process can introduce hallucinations, operates slowly, and has only been validated on macOS, with a risk of missing Claude's intended message.
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Vomit provides a pipeline that takes the token stream Claude emits and feeds it into a separate language model running on the same machine. By doing so, it converts the opaque token output into human-readable English while keeping all processing local. This approach avoids any network telemetry or external API calls, addressing privacy concerns for engineers who need to inspect model behavior.
To use Vomit, an engineer first installs the Go-based binary and then runs the init command to point it at a local LLM endpoint, which can be supplied by Llama.app, Ollama, or any service that mimics the OpenAI API. Once configured, commands such as list, tail, and help allow users to enumerate Claude sessions and translate their token output on demand. The tool also offers a non-invasive mode that runs alongside Claude without interfering with its execution.
The translation is not perfect; the local LLM may hallucinate content, the process is noticeably slow, and the software has only been tested on macOS, so behavior on other operating systems is uncertain. Because Vomit does not alter Claude's runtime state, there remains a possibility that the translated output omits or misrepresents the original message, prompting users to keep a copy of the raw tokens via other utilities if fidelity is critical.
Released under an open-source license, Vomit’s source can be inspected and modified, enabling teams to adapt the pipeline to their specific LLM or to add safeguards against hallucination. This openness also means that any improvements or fixes depend on community contributions rather than a centralized vendor. Engineers considering adoption should weigh the privacy benefits against the performance and accuracy limitations described.
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