The lexer/parser is never the bottleneck. In fact, you can write those two by hand over a single weekend for a largish language. With LLMs, it takes 15 minutes if you have an unambiguous spec.
The biggest time sink, and the reason you will fail for sure, is the inability to restrict the scope of the project. You start with a limited feature set and produce the entire compiler/vm toolchain. Then you get greedy and fiddle with the type system, adding features that you have never used and probably never will. And now you have to change every single phase from start to end.
I mostly give up at this stage.
Coming up with new ideas is hard. Especially since you have to test them in the real world.
The team implementing the survey system wound up using the same language to implement the runtime portion, something I never expected or designed in.
I don't recall anything about what it looked like now. I do remember it was a lot of fun to write.
I wrote a compiler toolchain and debugger that takes a Turing machine description plus input string and emits an encoded tape runnable by a Universal Turing Machine [0]. I had some prior PL experience, but never did an end-to-end compiler pipeline, at least not this low level.
It started as a joke/experiment, but I couldn't believe how fast it pulled me into designing:
- a small low-level ASM for building the UTM
- an ABI for symbol widths and encoding grammar
- an interpreter used as the behavioral oracle
- raw TM transitions for each ASM instruction, generated by having an LLM iterate on candidate emissions and checked against the interpreter oracle
- a CFG-style IR to fix the LLM mess once direct ASM -> TM emission became too hard to keep sane (LLM did a decent job actually, I don't think I would have done a much better job without the IR either)
- a gdb-style debugger for raw transitions, ASM routines, and blocks
- a trace visualizer
- a bootstrapping experiment where an L1 UTM/input pair was itself run through an L2 UTM
- optimisation experiments
And every step came quite naturally and was easy to tie in with everything else. Each one was just the next local repair needed to make the previous layer tractable.
[0] Repo: https://github.com/ouatu-ro/mtm
The tail ends of a language implementation (parsing and code generation) are a fixed cost; the "middle end" can grow unbounded as more production-quality items are added.
My language: https://www.empirical-soft.com
The language, Sapphire, is Ruby inspired, so the most interesting part is digging into the internals of the latter when I'm trying to figure out how something should work.
Only thing that goes for C++ is that it has acceptable (not straightforward) C interop.
I don't like C# and X++ because the language surface is huge but if you use a limited subset than needles to say, very useful and handy languages too.
(also, they might want to look into lua userdata, since that would address their concern about the overhead of converting between native and lua data structures. the language is designed to be embedded in C programs after all)
I'm kind of curious and want to try it for fun as long as i get some free time ^^
Maybe AI is good enough now to help me with that..
The last time I tried, Claude couldn't even help me build a syntax highlighter for a hypothetical language.
Roughly 100%.
But my main point is that libriscv is one of the fastest libriscv emulators and then something like C/C++/lua could've been used with sandboxing purposes for the purposes of the game then.
Am I missing something? Although, making a programming language is one kind of its own projects and that's really cool as well :-D
but I would also love to hear the author's opinion on libriscv as it feels like it ticks of all the boxes from my understanding
