Question: For things like this to be truly worthwhile, do you have to ensure the training, post-training of LLM's is filled with brilliant memelang code? The models are probably already good at 'thinking' in SQL at the moment, but if they aren't trained on memelang, surely they will have to use more effort to translate in context to produce equivalent quality queries?
Wouldn't surprise me if in the next few generations we start seeing more LLM generated languages that LLM's prefer to use for expressability, conciseness, etc.
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.
Question: For things like this to be truly worthwhile, do you have to ensure the training, post-training of LLM's is filled with brilliant memelang code? The models are probably already good at 'thinking' in SQL at the moment, but if they aren't trained on memelang, surely they will have to use more effort to translate in context to produce equivalent quality queries?
Wouldn't surprise me if in the next few generations we start seeing more LLM generated languages that LLM's prefer to use for expressability, conciseness, etc.
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.