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Comment by furyofantares | Hacker News Reader

I notice I am confused.

> Suddenly the opportunity cost for a DSL has just doubled: in the land of LLMs, a DSL requires not only the investment of build and design the language and tooling itself, but the end users will have to sacrifice the use of LLMs to generate any code for your DSL.

I don't think they will. Provide a concise description + examples for your DSL and the LLM will excel at writing within your DSL. Agents even moreso if you can provide errors. I mean, I guess the article kinda goes in that direction.

But also authoring DSLs is something LLMs can assist with better than most programming tasks. LLMs are pretty great at producing code that's largely just a data pipeline.

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Arguably it really depends on your DSL right? If it has a semantics that already lies close to existing programming languages, then I'd agree that a few examples might be sufficient, but what if your particular domain doesn't match as closely?

Examples of domains that might be more challenging to design DSLs for: languages for knitting, non-deterministic languages to represent streaming etc. (i.e https://pldi25.sigplan.org/details/pldi-2025-papers/50/Funct... )

My main concern is that LLMs might excel at the mundane tasks, but struggle at the more exciting advances, and so now the activation energy for coming up with advances DSLs is going to increase and as a result, the field might stagnate.

Remember that LLMs aren't trained on all existing programming languages, they're trained on all text on the internet. They encode information about knitting or streaming or whatever other topic you want a DSL for.

So it's not just a question of the semantics matching existing programming languages, the question is if your semantics are intelligible given the vast array of semantic constructs that are encoded in any part of the model's weights.

This is somewhat my take too. The way most vibe coding happens right now does create a lot of duplication because it's cheap and easy for LLMs to do. But eventually as the things we do with coding assistants become more complex, they're not necessarily going to be able to deal with huge swaths of duplicate code any better than humans are. Given their limited context size, having a DSL that allows them to fit more logic into their context with fewer tokens, we could conceivably see the importance of DSLs start to increase rather than decrease.

Every language other than machine level assembly instructions as human readable code is a DSL.

I was about to paste the same sentence, and say much the same thing in response.

To add to that... One limitation of LLM for a new DSL is that the LLM may be less likely to directly plagiarize from open source code. That could be a feature.

Another feature could be users doing their own work, and doing a better job of it, instead of "cheating on their homework" with AI slop and plagiarism, whether for school or in the workplace.

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