> String Slicing > You can extract a substring using bracket syntax with a range: s[start:end]. Both start and end are byte offsets. The slice includes start and excludes end.
Given that all strings are UTF-8, I note that there's not a great way to iterate over strings by _code point_. Using byte offsets is certainly more performant, but I could see this being a common request if you're expecting a lot of string manipulation to happen in these programs.
Other than that, this looks pretty cool. Unlike other commenters, I kinda like the lack of operator precedence. I wouldn't be surprised if it turns out to be not a huge problem, since LLMs generating code with this language would be pattern-matching on existing code, which will always have explicit parentheses.
I see that Deno requires a subprocess which introduces some overhead, and I might be naive to think so, but that doesn't seem like it would matter much when agent round-trip and inference time is way, way longer than any inefficiency a subprocess would introduce. (edit: I realized in some cases the round-trip time may be negligible if the agent is local, but inference is still very slow)
I admittedly do prefer the syntax here, but I'm more so asking these questions from a point of pragmatism over idealism. I already use Deno because it's convenient, practical, and efficient rather than ideal.
The bigger problem is maintainability over the long term, Deno is built by Node.js creator and is maintained for half a decade now, that's hard to compete with. In a way it's much more about social trust rather than particular syntax.
It's expensive of course, but if a new language is genuinely better for LLMs to write and understand, that would not be an issue.
I guess it depends on what "would work better" really means, but I don't think it's always a given. I've made my own languages, there is no available training set on exactly those, but AI with a prompt can figure out how to effectively use them as much as any other language, it seems to me. I guess it helps that most languages are more similar to each other than different, but even experimenting with new syntax seems to work out OK for me.
- GitHub syntax highlighting
- IDE integrations, LSP
- Modules and dependency management
I don't see an agent first language becoming a thing while humans are still ultimately responsible.
Something purpose built to enable embedding allows it to be used in more contexts. Maybe I want a Mog plugin for my latest video game. Embedding JS is possible, but no fun.
I didn't mean to suggest there's no need for Mog either. I love to see developments like this. Deno is a practical solution for me today, but I see why it isn't a perfect forever-solution too.
Since it's new, Mog will likely not yet beat existing systems at basically anything. Its potential lies in having better performance and a much smaller total system footprint and complexity than the alternatives. WASM is generally interpreted -- you can compile it, but it wasn't really designed for that as far as I know.
More generally, I think new execution environments are good opportunities for new languages that directly address the needs of that environment. The example that comes to mind is JavaScript, which turned webpages into dynamically loaded applications. AI agents have such heavy usage and specific problems that a language designed to be both written and executed by them is worth a shot in my opinion.
WASM is a great system, but quite complex -- the spec for Mog is roughly 100x smaller.
There is something to be said about giving AIs a clean foundation on which to build their own language. This allows evolution of such systems to go all the way into the compiler, beyond tooling.
Agents can pretty much iterate on their own.
The most important thing for me, at least for now (and IMO the foreseeable future) is being able to review and read the output code clearly. I am the bottleneck in the agent -> human loop, so optimizing for that by producing clear and readable code is a massive priority. Gleam eliminates a ton of errors automatically so my reviews are focused on mostly business logic (also need to explicitly call out redundant code often enough).
I could see an argument for full on Erlang too, but I like the static typing.
gleam/io, mist, gleam/otp, sqlight, gleam_pgo, etc.
If you're running the compiled code in-process, how is that not JIT? And isn't that higher-latency than interpreting? Tiered-JIT (a la V8) solves exactly this problem.
Edit: Although the example programs show traditional AOT compile/execute steps, so "no process startup cost" is presumably a lie?
JIT means the code is interpreted until some condition kicks in to trigger compilation. This is obviously common and provides a number of advantages, but it has downsides too: 1) Code might run slowly at first. 2) It can be difficult to predict performance -- when will the JIT kick in? How well will it compile the code?
With Mog, you do have to pay the up-front cost of compiling the program. However, what I said about "no process startup cost" is true: there is no other OS process. The compiler runs in process, and then the compiled machine code is loaded into the process. Trying to do this safely is an unusual goal as far as I can tell. One of the consequences of this security posture is that the compiler and host become part of the trusted computing base. JITs are not the simplest things in the world, and not the easiest things to keep secure either. The Mog compiler is written entirely in safe Rust for this reason.
This up-front compilation cost is paid once, then the compiled code can be reused. If you have a pre-tool-use hook, or some extension to the agent itself, that code runs thousands of times, or more. Ahead-of-time compilation is well-suited for this task.
If this is used for writing a script that agent runs once, then JIT compilation might turn out to be faster. But those scripts are often short, and our compiler is quite fast for them as it is in the benchmarking that I've done -- there are benchmarking scripts in the repo, and it would be interesting to extend them to map out this landscape more.
Also, in my experience, in this scenario, the vast majority of the total latency of waiting for the agent to do what you asked it is due to waiting for an LLM to finish responding, not compiling or executing the script it generated. So I've prioritized the end-to-end performance of Mog code that runs many times.
V8 can also go back and forth from machine instructions back to bytecode if it identifies that certain optimization assumptions no longer hold.
Almost all the code LLMs have been trained on uses operator precedence, so no operator precedence seems like a massive foot-gun.
But LLMs very much do care. They are measurably worse when writing code in languages with non-standard or non-existent operator precedence. This is not surprising given how they learn programmming.
> There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.
OK that does immediately make it boring, I give them that much.
Would have been a blockchain language 10 years ago.
A few questions:
- Is there a list of host languages?
- Can it live in the browser? (= is JS one of the host languages?)
It's also designed to be run in an event loop. I've tested this with Bun's event loop that runs TypeScript. I haven't tried it with other async runtimes, but it should be doable.
As for the browser, I haven't tried it, but you might be able to compile it to WASM -- the async stuff would be the hardest part of that, I suspect. Could be cool!
The project is here: https://codeberg.org/ZelphirKaltstahl/web-app-vocabulary-tra... But I left it unfinished and a quick grep does not yield comments or something that explains why at some place I do something to circumvent the SQLite problems. I remember though, that I basically swore to myself, that I would not ever use SQLite in production with Django ORM. And if I am not using it in production, then testing also better should not be using it, because one should test with the same RDBMS as runs in production, or risk unexpected issues suddenly only happening in production. So SQLite is out for anything serious in Django projects for me.
Please think twice before releasing these, if you're going to do it come up with at least one original idea that nobody else has done before.
Why didn't you just call it "bad rust copy"?