pkage

As with all text-only AI detection schemes, I am concerned about the accuracy of the detection. I'm skeptical of the methodology, specifically the final join of the three detector scores---how can you be sure that that final step does not introduce any biases? There's no source available, so it's difficult to tell exactly how this works or reproduce the research.

I've also uploaded text samples from my own (unreleased) research from pre-LLM era, and it's seemingly scoring pretty high on the LLM-detection scores. On other papers, nearly every sentence is highlighted as red "machine-leaning," but that does not impact the score? Additionally, there are dramatic differences between the scores for identical text with and without LaTeX formatting, despite the fact that it should not matter.

The takeaway from this should be "it is difficult to detect generated text and we should be careful about accepting results simply because they confirm a hypothesis."

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Relatedly, the text above scores as highly machine-written, despite the fact that I just wrote it with my human hands, I promise :)

The filter doesn't appear to be perfect, one of the top posts right now is "My Agent Skill for Test-Driven Development"

I switched from iTerm2 because at the time (possibly still), iTerm2 had a performance bug where large amounts of underlined text would cause the terminal to slow down noticeably. Wezterm works perfectly, and I appreciate the .lua configuration over iTerm2's mess of menus.

Does not appear to work under Firefox, getting a bunch of CORS-related errors (header ‘user-agent’ is not allowed according to header ‘Access-Control-Allow-Headers’ from CORS preflight response) on the /graphql endpoint.

In addition to the other responses, it's also worth noting that wasm itself is useful outside of the web itself; e.g. in containerized applications.

Yes, that's an http connection to the Docker Engine api on localhost failing due to the same issue—the docker engine cam't negotiate with the Docker Hub to get the new image and is passing the error back through the local api to your updater process.

The DC Metro system operates similarly.

mispasted, thanks!