kartoshka

Can confirm that recent deepseek models refuse on some political questions (not just API level refusals). Actually deepseek models are basically the only chinese models to outright refuse instead of responding with propaganda. But recent deepseek models are not nearly as biased as R1.

Had a blog post on this a couple weeks ago: vals.ai/blogs/china-us-analysis

It would be nice if there were a way to give some global instructions for the auto-approver like "always reject ___" or "be extra extra careful with ___" for things like file removal and git.

Has anyone had Claude Code or Codex approve a harmful/damaging command in auto mode?

I have been using Codex with auto-approve mode for a couple months and haven't had a single incident (or at least haven't noticed). Maybe as capabilities get better and better and they are less likely to do something dumb like wiping ~/, we can just trust them?

I guess this argument works unless we worry about agents doing something out of malice instead of stupidity.

I think for a realistic comparison we would need to see LLMs benchmarked with and without a "use Memelang for queries" prompt (and a short file somewhere explaining the syntax). I think it's unlikely that the token-efficiency of Memelang would provide much benefit for current LLMs; my intuition is that much more tokens would be spent on reasoning and mistakes in Memelang than are saved from just using SQL (SQL queries aren't usually that long anyway??).

In the few public reasoning traces from recent OpenAI models, we see a lot of "grug speak" as it is token-efficient, and they presumably trained the models for token efficiency in reasoning. It is indeed not too much of a leap to see future token-efficient programming languages.