I like the looks of this, and the idea behind it, but TypeScriot via Deno is an audited language with a good security model, a good type system, and sandboxing in an extremely well-hardened runtime. It's also a language that LLMs are exceptionally well-trained on. What does Mog offer that's meaningfully superior in an agent context?
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.
It's a legitimate question to ask about any new language post AI - given there is no training dataset, any other language would work better with AI.
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.
> given there is no training dataset, any other language would work better with AI.
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.
I generally agree. TypeScript is a great language, and JS runtimes have certainly had a lot of money and effort poured into them for a long time. I would add WASM to this category, as probably the closest thing to Mog. Write a program in some language, compile it to WASM, and load it into the host process. This is (probably) nice and safe, and relatively performant.
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.
I cannot comment on the new language, but Typescript is a huge spec. Yes, it has guardrails, but there is a lot of complexity to handle.
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.
One thing that comes to mind, more of a first reaction than a considered opinion, is the complexity of V8 getting in the way. JavaScript and Typescript present a challenge to language implementors.
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.
I agree with this take. What does this bring to the table that can't be done with pretty much any preexisting toolset? Hell, even bash and chroot jail...
For me this is Gleam. Fairly small lang, type safe, compiled, NO NULLS (very important IMO), good FFI, code is readable, and... you get the BEAM!
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.
Under the hood you're still calling into BEAM/OTP but most operations are covered in gleam, at least I've very rarely needed to FFI into Erlang (couple times to extend what a library provides with a custom edge case).
gleam/io, mist, gleam/otp, sqlight, gleam_pgo, etc.
> Compiled to native code for low-latency plugin execution – no interpreter overhead, no JIT, no process startup cost.
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.
Hum, IIRC, using your definition of an AOT compiler, then V8 is an AOT compiler. V8 never interprets code. It immediately compiles it to machine code. It improves it later, but it's never slow.
V8 is a JIT compiler that uses the Ignition [1] interpreter and only compiles sections of code down to machine instructions once they've been marked as hot via TurboFan [2].
V8 can also go back and forth from machine instructions back to bytecode if it identifies that certain optimization assumptions no longer hold.
> it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c.
Almost all the code LLMs have been trained on uses operator precedence, so no operator precedence seems like a massive foot-gun.
I would usually. Sometimes if it's like 2 * x + b, I would not, but personally, I hate chasing down bugs like this, so just add it to remove ambiguity. Also, for like b + 2 * a, I will almost always use parentheses.
> When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.
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.
Argument 1 ("Syntax Only an AI Could Love") sounds dubious. I am probably not alone in being paranoid enough, to always put those parentheses, even if I am 90% sure, that there is operator precedence. In lispy languages the ambiguity never even arises, and I put many parentheses, and I like it that way, because it enables great structural editing of code. No implicit type coercion has also been part of normal for-human programming languages (see SMLNJ for example).
> 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.
I am disappointed at the amount of negativity here. HN generally loves an experimental domain-specific language, no matter how janky. To be clear, I don't know if this is janky, but the knee-jerk anti-AI sentiment is not intellectually stimulating.
The host is written in Rust, with `extern "C"`, which makes it able to be loaded as a C library by programs written in other languages. Most languages have support for this.
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!
Ran into the same thing. SQLite works until you need cold start recovery or WAL contention with concurrent agents. Built a dedicated memory layer for agent workloads - happy to share: https://github.com/RYJOX-Technologies/Synrix-Memory-Engine
Or, when you have a Django project and started out on SQLite, but then begrudgingly introduce M-to-N relationships, but then suddenly notice, that many things you might want to do or implement with those M-to-N relationships are not supported by SQLite. Then you suddenly wish you had started with Postgres right away.
There are definitely some caveats / tradeoffs with SQLite, but I can't think of any that are specifically related to many to many relationships. Which features did you find missing? Lateral joins maybe?
I feel like a small language designed specifically for LLM's should/will exist someday. Certainly I've found I really like simpler compiled languages with more complete memory models, I would imagine a language designed for LLM's and agents could improve workflows someday.
I’m still waiting for someone to build a good lisp harness. Stick an agent in a lisp repl and they can change literally anything they want easily.
I like the looks of this, and the idea behind it, but TypeScriot via Deno is an audited language with a good security model, a good type system, and sandboxing in an extremely well-hardened runtime. It's also a language that LLMs are exceptionally well-trained on. What does Mog offer that's meaningfully superior in an agent context?
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.
It's a legitimate question to ask about any new language post AI - given there is no training dataset, any other language would work better with AI.
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.
> given there is no training dataset, any other language would work better with AI.
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.
I'd also add all the other things that users expect around a language:
- 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.
I generally agree. TypeScript is a great language, and JS runtimes have certainly had a lot of money and effort poured into them for a long time. I would add WASM to this category, as probably the closest thing to Mog. Write a program in some language, compile it to WASM, and load it into the host process. This is (probably) nice and safe, and relatively performant.
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 explicitly not designed for interpretation (https://arxiv.org/abs/2205.01183)
Wasm is definitely designed to be compiled, either ahead of time or JITed. Wasm interpreters are few and far between.
Huh you're right. I had worked with interpreted WASM before, which is why I thought that was more common.
WASM is a great system, but quite complex -- the spec for Mog is roughly 100x smaller.
I cannot comment on the new language, but Typescript is a huge spec. Yes, it has guardrails, but there is a lot of complexity to handle.
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.
One thing that comes to mind, more of a first reaction than a considered opinion, is the complexity of V8 getting in the way. JavaScript and Typescript present a challenge to language implementors.
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.
I agree with this take. What does this bring to the table that can't be done with pretty much any preexisting toolset? Hell, even bash and chroot jail...
For me this is Gleam. Fairly small lang, type safe, compiled, NO NULLS (very important IMO), good FFI, code is readable, and... you get the BEAM!
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.
How's Gleam's IO story today? Still need to go via OTP?
Under the hood you're still calling into BEAM/OTP but most operations are covered in gleam, at least I've very rarely needed to FFI into Erlang (couple times to extend what a library provides with a custom edge case).
gleam/io, mist, gleam/otp, sqlight, gleam_pgo, etc.
> Compiled to native code for low-latency plugin execution – no interpreter overhead, no JIT, no process startup cost.
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?
Mog is AOT-compiled, not JIT'd.
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.
Hum, IIRC, using your definition of an AOT compiler, then V8 is an AOT compiler. V8 never interprets code. It immediately compiles it to machine code. It improves it later, but it's never slow.
V8 is a JIT compiler that uses the Ignition [1] interpreter and only compiles sections of code down to machine instructions once they've been marked as hot via TurboFan [2].
V8 can also go back and forth from machine instructions back to bytecode if it identifies that certain optimization assumptions no longer hold.
[1] https://v8.dev/docs/ignition
[2] https://v8.dev/docs/turbofan
They could have just used cranelift for jit, but no..
> it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c.
Almost all the code LLMs have been trained on uses operator precedence, so no operator precedence seems like a massive foot-gun.
I agree. But also, do people rely on operator precedence when coding? I just automatically use parentheses in general.
Also for a * b + c ? I know clang tidy wants me to do this but I think it is overkill.
I would usually. Sometimes if it's like 2 * x + b, I would not, but personally, I hate chasing down bugs like this, so just add it to remove ambiguity. Also, for like b + 2 * a, I will almost always use parentheses.
A thing for the current thing.
Would have been a blockchain language 10 years ago.
> When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.
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.
Argument 1 ("Syntax Only an AI Could Love") sounds dubious. I am probably not alone in being paranoid enough, to always put those parentheses, even if I am 90% sure, that there is operator precedence. In lispy languages the ambiguity never even arises, and I put many parentheses, and I like it that way, because it enables great structural editing of code. No implicit type coercion has also been part of normal for-human programming languages (see SMLNJ for example).
> 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.
I am disappointed at the amount of negativity here. HN generally loves an experimental domain-specific language, no matter how janky. To be clear, I don't know if this is janky, but the knee-jerk anti-AI sentiment is not intellectually stimulating.
Awesome!
A few questions:
- Is there a list of host languages?
- Can it live in the browser? (= is JS one of the host languages?)
The host is written in Rust, with `extern "C"`, which makes it able to be loaded as a C library by programs written in other languages. Most languages have support for this.
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!
Ran into the same thing. SQLite works until you need cold start recovery or WAL contention with concurrent agents. Built a dedicated memory layer for agent workloads - happy to share: https://github.com/RYJOX-Technologies/Synrix-Memory-Engine
Or, when you have a Django project and started out on SQLite, but then begrudgingly introduce M-to-N relationships, but then suddenly notice, that many things you might want to do or implement with those M-to-N relationships are not supported by SQLite. Then you suddenly wish you had started with Postgres right away.
There are definitely some caveats / tradeoffs with SQLite, but I can't think of any that are specifically related to many to many relationships. Which features did you find missing? Lateral joins maybe?
Don't know if others have this issue, but for me I can't scroll on Firefox.
Firefox 148.0 MacOS Tahoe - I'm able to scroll.
Doesn't need to be its own language.
I feel like a small language designed specifically for LLM's should/will exist someday. Certainly I've found I really like simpler compiled languages with more complete memory models, I would imagine a language designed for LLM's and agents could improve workflows someday.
I looked at the brainrotty name[1] and instantly assumed AI slop, but I'm glad the website was upfront about that.
[1] https://www.merriam-webster.com/slang/mog
Yeah, it is a really bad connotation. This infantile and obsessive shallow comparisons of everything. I utterly hate to see the term here on HN.
is this project like vibe coded slop from a zoomer or nah?
Wow, we've brought mogging to the programming world. Nothing is safe from looksmaxxing it seems.
https://finalfantasy.fandom.com/wiki/Mog
How is Mog different than Mojo?
Its disheartening to see these crop up after spending 25 years through trial and error learning how to write programming languages.
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"?