I straight up skip all the memory thing provided by harnesses or plugins. Most of my thread is just plan, execute, close - Each naturally produce a file - either a plan to execute, a execution log, a post-work walkthrough, and is also useful as memory and future reference.

The problem is human, not society, I don't any any -ism can fix human.

The points you brought up all are valid. Lexer, parser and general concepts are not language-specific, yes, and I wasn't talking about how the implementation is different.

When I said "you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design", I was thinking about the many times they just fail to write in my language even when provided will full language specs. LLMs don't "think" and boilerplate is easy for LLMs because highly similar syntax structure even identical code exist in their training data, they are kind of just copying stuff. But that doesn't work that well when they are tasked to write in a original language that is... too creative.

But I feel like the rationale would still stands: Considering LLMs' natures, common boilerplate tasks are easy because they can kind of just "decompress" from training data. But for a new language design, unless the language is almost identical to some other captured by the model, "decompression" would just fail.

In the past 2 months I've been using all the SOTA models to help me design a new DSL for narrative scripting (such as game story telling) and a c# runtime implementation o the script player engine.

The language spec and design is about 95% authored by me up to this point; I have the LLMs work on the 2nd layer: the implementation specs/guidelines and the 3rd layer: concrete c# implementation.

Since it's a new language, I consider it's somewhat new/novel tasks for LLMs (at least, not like boilerplate stuff like HTTP API or CRUD service). I'd say, these LLMs have been very helpful - you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design - but they are mostly smart enough to carry out the objectives, and they get better and better after the project got on track and has plenty of files/resources to read and reference.

And I'd also say "prompt better" is a important factor, just much more nuanced/complicated. I started with 0 experience with LLM agents and have learned a lot about how to tame them, and developed a protocol to collaborate with agents, these all comes from countless trial and errors, but in the end get boiled down to "prompt better".

They are known to be very inefficient using only Powershell to interact with files, unless put in WSL. They tend to make mistakes and have to retry with different commands.

Another example is Serena. I knew about it since the first day I tried out MCP but didn't appreciate it, but tried it out again on IDEs recently showed impressive result; the symbolic tools are very efficient and helps the agents a lot.