Luckily there's a lot of people who write prototype code, toy projects, and other "non-production" code that can experiment with a new project like this
I have tried it.
It feels pretty good to use, but you have to keep in mind that it's very early software.
They currently break the API every other week, and it's still missing a lot of fundamental features.
I have made a few basic web apps with it, and on v0.8.1 it was still missing a lot of reactivity which meant you had to use Javascript to bridge the gap.
We’re using it to build our web app (https://uncook.xyz) and while it’s a slight departure from my team’s NodeJS background it’s been pretty cool to work with
I haven't tried to the web app part but I've tried Toasty. The API seemed to be a little simpler and nicer to use than SeaORM for me. Here are a few things I liked in Toasty:
- In SQLite UUIDs are default stored as compact blobs instead of inefficient varchar/text (SeaORM)
- Timestamps were easier via jiff instead of chrono (SeaORM)
- Model structs use the actual name of the $table instead of being named like $tablemodule.(Model|ActiveModel) (SeaORM) they are just $Table. This makes writing libraries a little simpler that need to use entity struct's from two separate modules/crates otherwise you get a name collision on Model/ActiveModel and need to do alias imports.
- There's an API to fetch related data to a query similar to Django ORM. IIRC this is missing from SeaORM
- The query expression language doesn't cover as much of SQL as SeaORM but seems to be enough for most web apps.
- I didn't like how the migration system works. I would skip that part of the library
Do you have feedback on what you don’t like re: the migration system? It is inspired by drizzle. Is it something we missed or just not a fan of that style entirely?
> write it in C and ask an LLM to weed out the memory bugs?
because a non-determinist chain of reasoning is not the same as a fully deterministic algorithm. Its the best tool when a deterministic system is not feasable.
>because a non-determinist chain of reasoning is not the same as a fully deterministic algorithm.
It is not. But rust's borrow checker has its costs. It forces awkward implementations. Either suffer the horrible life time syntax, or pay the price of clone everywhere.
If you use C and run LLMs over it once in a while, I think it will get you most of the way.
99% of code only needs to know 'static and 'a, maybe 'b when you take two references and return one but that's pretty rare. You only get into complex lifetimes if you write the kind of code that belongs in a library.
Why bother with rust when you can write it in C and ask an LLM to weed out the memory bugs?
Here is a idea, why not combine both? Have the advantage of Rust its build in checks and LLMs independent checks. Now you get both for a even more safe program.
Some absolutely great work, but I don't know why you would come up with a new term for a server component (shard). Why not just use the obvious client/server annotations so you don't have to define a novel term for new users?
Just needed a different name for it. What should it have been called? `#[component]` is not reachable from the client. We went with `#[shard]` to make it clear that it is different (callable from the client).
It's interesting to see that they let you mix markup with logic directly in rust, but I have worries about maintainability. I remember the bad old days of PHP where you'd mix markup with logic directly, even when doing OOP with classes and it really made things more difficult long term.
It’s not a rust thing. Custom markup is usually implemented in rust frameworks using macros. If mixing macros and logic becomes a problem, we can change how the macros are implemented.
> Rust is the best general-purpose language for the new world of AI-driven development.
Funny, the Python guys say Python is the best general-purpose language for AI and the Go guys say Go is the best general-purpose language for AI etc etc
Do you have any substantial sound metrics and study? I can think of many ways for a Rust program to not remove all fears that it will not do what I intended, doubly so for a vibe-coded Rust program.
You‘ll notice a lot of "I found"s and "my hunch"s and similar in the comments.
There aren’t many comparative benchmarks, and obviously the design of such a benchmark is difficult, but, e.g. https://arxiv.org/abs/2508.09101
In this benchmark, models can correctly solve Rust problems 61% on first pass — A far cry from other languages such as C# (88%) or Elixir (97%, no static typing whatsoever).
First pass is not that important today. All coding agents run until they get it right or hit a limit.
Having to tweak code and compile more often makes things slower, but strict compilers also help you find bugs faster and navigate the codebase. I think the two effects balance out and leave you with safer code in the end. And that's not counting the performance benefits of a compiled language.
I've found that the guardrails of a strong, expressive type system are amazing for LLMs, especially if you don't use the top-of-the-line models but stick to the cheaper options. Agents need feedback to do their magic, and the type system is great for that.
But on the other hand, the compile time of Rust is really counterproductive. On large, established projects, most prompts I write now spend more time compiling on my machine than they spend outputting tokens. In a way this is great because the tokens are the expensive part, but it does mean that when faster LLMs will come, they will not meaningfully improve iteration speed for me.
I wonder how much of the compile time of Rust is inherent to the type system. There might be room for a language with slower runtime speed (GC?), but as good of a type system as Rust, so long as compile times are much faster. Switching languages has never been easier, anyone have any suggestions? IMO hard requirements are algebraic types and error handling based on them.
Compile times in Rust are partly the amount of analysis that occurs, partly that it uses LLVM which is optimized for creating fast code at runtime but not necessarily fast-compiling code, and partly the fact that the Rust compiler is inefficient and does the same work twice or recompiles things it doesn’t need to.
But you can speed things up a lot by organizing your project into separate crates (which are compiled in parallel) and tweaking compiler flags. I speed up a large project by 7x this way.
Also, if you’re using fat LTO in release builds, switch to thin. It’s almost as fast at runtime and much much faster to compile.
Even incremental builds can be slow depending on eg. what macros you use. For example, i had bad surprises with askama which is a delight in most cases, but really makes compilation slower as you add more templates. On a project with ~20 templates, incremental build in debug mode now takes ~40s on a very fast machine.
I love python for some stuff, but i definitely would not say it has an expressive type system. Enums are a second-class citizen, and typed dicts are definitely not as convenient as i would like. And to my knowledge, type checkers don't support proper exception catching rules; rust also has no guarantee that a function won't panic, but usage of Result type makes it less of a problem (because panic is more or less exclusively for unrecoverable invariant violations), and there's ecosystem tools to make sure you don't ever panic.
If by enums you mean sum-type ADTs (like in Rust), then Python is certainly quite expressive - it has union types, which can represent polymorphic variants - those are slightly more expressive than pure Rust-style ADTs since you can arbitrarily subset or extend the enum cases.
@dataclass @final class CaseA[T]:
field: T
type MyEnum[T] = CaseA[T] | CaseB
This also gets exhaustiveness checking in `match` statements (depending on the type checker). Overall, enums have a similar style to Scala and Java >=21 (mixing OO + ADT). I guess syntactically it can be slightly verbose depending on the exact situation...
I wouldn't call them second class, but yeah, not a lot of existing libraries use this style.
Typed dicts (with the latest PEPs) are an improvement over any other language besides TypeScript. Yes, in TS, certainly `Partial` / `Pick` / `Omit` and intersection types make modeling the web API swamp easier, and that's one place TS is superior to anything else.
Python does have the Type Manipulation PEP 827 [1] out to give it similar powers to TS, but I feel that's unlikely to be implemented soon / as-is.
Having an effect system for exceptions would be nice indeed. That's actually something I'm looking into (having an "allowed exceptions" annotation for functions and then checking it at test-time via failure injection and possibly in type checkers).
Exactly my point. Everyone says agents are “best” at their language. Which probably translates to agents generate equally well for all programming languages. So, why would you not pick the programming language that gets you the best (fastest, least memory) end product?
None of the other languages are as strict as rust is. With eager clippy and cargo lints and software design as close to the type system as possible, LLMs have tight guard rails to land on point.
It's the Python guys whom I totally don't understand - it's all fun and dandy until you try to maintain and cleanup a legacy codebase of tens of millions lines of code in any dynamic language :)
With or without AI - doesn't matter. Only at that point you gain understanding of the limitations both of LLMs and of dynamic languages.
Unless you're doing something really dynamic, I don't really understand this position and would like to learn more. for most cases gradual typing is a great option that can be done incrementally in Python (two things even cheap agents are really good at).
You can't forbid people doing something "really dynamic". And in a sufficiently big system, over long period of time, this stuff will accumulate - 100%. Then you'll get to a point where answering questions like "is this piece of code still alive" with enough confidence is almost impossible.
The question is where the "enough confidence" border lies - and in the worse case the only way to do some reliable investigatios would be printf debugging or its alikes.
It sounds like your app is using a lot of dynamic techniques like meta-programming? If so, that makes a lot of sense. Most of the apps I've seen use very little use of meta-programming and friends and incremental typing worked rather well.
I don't write Rust & have no horse in this race but my intuition on this is you have 3 scenarios:
1. Humans writing code: here the usability, readability, accessibility of the language matters - strictness can be a hurdle depending on how a language is designed, so ultimately it's a trade-off.
2. Humans reviewing AI-authored code: here the requirements of (1) still apply to the code reviewer
3. Autonomous agents writing code: above requirements no longer apply so having a strictly-defined language with tight guardrails is the primary consideration.
I think most people are operating workflows in category (2), but it seems uncontroversial to say Rust is more suited to category (3) than python or golang. Typescript, Haskell, Elm, Ocaml could be considerations but you'll find Rust hard to beat here. Certainly neither python nor golang are in the running at all.
Never met a person who said python is the best except the python-cult. Legacy language that should be gone, it’s a scripting language that got known by data scientists as a replacement of R because they are not developers, they wanted something hacky and dirty to do things quickly, it was never meant for serious programming, something you can see in the dependency hell, indentation, syntax, performance, etc, and then you see people are trying to use it in UI or even robotics, what are you doing?! Maybe before llm it was an easy hacky way, now it should die for good.
I have prompted a LOT of Go in the past few months. While LLMs do indeed know Go very well, the amount of over-abstraction I'm seeing is awful. It's not at all indicative of code that I'd write myself. The whole point of Go is to have a language that is immediately approachable by juniors. And I've tried to keep my code in that lane. The reason I won't prompt Rust at all, is that I know it'll output working code... that I likely will not understand. (that's not the LLM's fault)
Honestly Rust is an ugly looking language, I mean it's probably okay if you're coming from C or C++ but if you're coming from say Java, it's horrendous.
but then again that probably does not matter at all.
Just like any other language, you can get used to it. There are ways to hide the uglyness and i am not sure if this is a good thing. (i will always hate macros!)
The majority of rust's ugliness is the fact that it's handling a different problem than Java does. Java relies heavily on a GC to simplify. Rust does not.
I think the only thing that I find ugly in rust is that memory leaks are considered memory safe. [1]
That, unfortunately, means that you can't 100% rely on the language to not leak memory. Though, it's possible to leak memory in Java as well (Use `Deflate` incorrectly and you'll quickly find a hard to catch memory leak).
I thought the same thing. I started working full time in Rust back in March and the first few weeks I thought “this is ridiculous, it’s so ugly and hard to read”. My background is in C++ for game development (10y) followed by CTO of a web startup (JS/Python/TypeScript), so I know ugly code!
You absolutely do get used to it and I really like the built in formatters. I would write everything in Rust going forward if I could, it really is a different way of thinking about how to write code. I don’t agree it’s the right tool for every job though.
In times of AI the language doesn't matter, not even if the dev can read it or not, what matters is the result, speed, optimization, interoperability, UI/UX adaptability to user expectations
I've built apps recently in swift, rust, zig, node with HTML UI (not electron) for MacOS and I care less about the code as the AI agent does a perfect job in all of them, even if I don't see much difference in the results (except HTML which is pretty but weird)
Asked the same question to AI and she told me Swift is the best choice for MacOS apps just because it doesn't need C bridges between their kits/libraries and that in itself is an optimization gain
Of course Swift in Linux is not the best option, probably the last, in Microsoft probably .NET I don't know, haven't used MS products in twenty years
The point is, language selection is best decided on platform optimization, not on personal preference. I guess AI killed language wars?
Topcoat is very cool though, and there’s a few others in this space too, like Loco. So maybe we’ll see folks move to something more batteries included.
I used to write Leptos, but abandoned it. I hoped the experience would get better, it took a lot of memory just for the language server to validate especially. Web apps become too cumbersome to maintain in bigger distributed teams where we want fast feedback. I think writing web apps in straight rust will always be niche. There's not many frontend devs who write React or Vue who want to write straight Rust just because of safety or performance.
That's why I created SnapFire FSR (https://www.snapfirers.com), but many examples, to allow frontend devs to develop in their favorite framework (or mixture of). Write Tera templates, Web Components if you don't want to bring in frameworks like I tend to.
I found it much better to create and maintain web apps near the markup and language they originated from. You won't find me writing straight Rust for Web Apps unless I need to anymore.
Topcoat needs an Inertia like React integration + Stylex (type safe CSS) on the frontend imo if you want make it as agent friendly as possible. HTMX, Tailwind etc. are still ok but folks who switch to Rust now do this mostly for easier and safer automation. So if you want to grow the framework better think about React.
I'd personally like my ORMs (if I'm going to use them) to be closer and more explicit about the queries that I'm writing. ORMs tend to add a lot of complexity for very little benefit. SQLx is much closer to my cup of tea than Toasty.
Underlying DBs have different characteristics, so trying to make an abstraction layer for multiple dbs (including no-sql in the docs) means you are going to run into limitations and abstraction leaks that are hard to square. Correctly using the likes of Dynamodb is very different from correctly using postgres, for example.
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[ 0.39 ms ] story [ 16.5 ms ] threadAs honest as it is I can't risk using this anywhere near production if this is the attitude.
- In SQLite UUIDs are default stored as compact blobs instead of inefficient varchar/text (SeaORM)
- Timestamps were easier via jiff instead of chrono (SeaORM)
- Model structs use the actual name of the $table instead of being named like $tablemodule.(Model|ActiveModel) (SeaORM) they are just $Table. This makes writing libraries a little simpler that need to use entity struct's from two separate modules/crates otherwise you get a name collision on Model/ActiveModel and need to do alias imports.
- There's an API to fetch related data to a query similar to Django ORM. IIRC this is missing from SeaORM
- The query expression language doesn't cover as much of SQL as SeaORM but seems to be enough for most web apps.
- I didn't like how the migration system works. I would skip that part of the library
I personally like Rust for web applications a lot because you can catch many bugs at compile time instead of during tests or runtime.
because a non-determinist chain of reasoning is not the same as a fully deterministic algorithm. Its the best tool when a deterministic system is not feasable.
It is not. But rust's borrow checker has its costs. It forces awkward implementations. Either suffer the horrible life time syntax, or pay the price of clone everywhere.
If you use C and run LLMs over it once in a while, I think it will get you most of the way.
Here is a idea, why not combine both? Have the advantage of Rust its build in checks and LLMs independent checks. Now you get both for a even more safe program.
The things that look great are LiveView and getting rid of boilerplate for client-side reactivity
Funny, the Python guys say Python is the best general-purpose language for AI and the Go guys say Go is the best general-purpose language for AI etc etc
There aren’t many comparative benchmarks, and obviously the design of such a benchmark is difficult, but, e.g. https://arxiv.org/abs/2508.09101
In this benchmark, models can correctly solve Rust problems 61% on first pass — A far cry from other languages such as C# (88%) or Elixir (97%, no static typing whatsoever).
Having to tweak code and compile more often makes things slower, but strict compilers also help you find bugs faster and navigate the codebase. I think the two effects balance out and leave you with safer code in the end. And that's not counting the performance benefits of a compiled language.
But on the other hand, the compile time of Rust is really counterproductive. On large, established projects, most prompts I write now spend more time compiling on my machine than they spend outputting tokens. In a way this is great because the tokens are the expensive part, but it does mean that when faster LLMs will come, they will not meaningfully improve iteration speed for me.
I wonder how much of the compile time of Rust is inherent to the type system. There might be room for a language with slower runtime speed (GC?), but as good of a type system as Rust, so long as compile times are much faster. Switching languages has never been easier, anyone have any suggestions? IMO hard requirements are algebraic types and error handling based on them.
I realized all the Rust code produced meant squat if it couldnt be audited well afterwards.
But you can speed things up a lot by organizing your project into separate crates (which are compiled in parallel) and tweaking compiler flags. I speed up a large project by 7x this way.
Also, if you’re using fat LTO in release builds, switch to thin. It’s almost as fast at runtime and much much faster to compile.
Since it's going to be mostly cargo check and incremental builds, I don't think it matters in practice.
There is OCaml which compiles very fast in debug mode.
The big difference to Rust is the availability of interpreters, REPL, which can equally load compiled code, and full blown AOT for release builds.
Rust's problem isn't type system, rather lack of tools.
Typed dicts (with the latest PEPs) are an improvement over any other language besides TypeScript. Yes, in TS, certainly `Partial` / `Pick` / `Omit` and intersection types make modeling the web API swamp easier, and that's one place TS is superior to anything else.
Python does have the Type Manipulation PEP 827 [1] out to give it similar powers to TS, but I feel that's unlikely to be implemented soon / as-is.
Having an effect system for exceptions would be nice indeed. That's actually something I'm looking into (having an "allowed exceptions" annotation for functions and then checking it at test-time via failure injection and possibly in type checkers).
[1]: https://peps.python.org/pep-0827/
With or without AI - doesn't matter. Only at that point you gain understanding of the limitations both of LLMs and of dynamic languages.
Your legacy codebase generates ~300 billion a year?
The question is where the "enough confidence" border lies - and in the worse case the only way to do some reliable investigatios would be printf debugging or its alikes.
1. Humans writing code: here the usability, readability, accessibility of the language matters - strictness can be a hurdle depending on how a language is designed, so ultimately it's a trade-off.
2. Humans reviewing AI-authored code: here the requirements of (1) still apply to the code reviewer
3. Autonomous agents writing code: above requirements no longer apply so having a strictly-defined language with tight guardrails is the primary consideration.
I think most people are operating workflows in category (2), but it seems uncontroversial to say Rust is more suited to category (3) than python or golang. Typescript, Haskell, Elm, Ocaml could be considerations but you'll find Rust hard to beat here. Certainly neither python nor golang are in the running at all.
1. Python has a lot of training data
2. Go runs with the philosophy of one same way to do stuff even further than Python. It’s also typed
3. Rust’s type system and strict compiler is what helps keep AI on guardrails
The majority of rust's ugliness is the fact that it's handling a different problem than Java does. Java relies heavily on a GC to simplify. Rust does not.
I think the only thing that I find ugly in rust is that memory leaks are considered memory safe. [1]
That, unfortunately, means that you can't 100% rely on the language to not leak memory. Though, it's possible to leak memory in Java as well (Use `Deflate` incorrectly and you'll quickly find a hard to catch memory leak).
[1] https://brson.github.io/rust-anthology/1/memory-leaks-are-me...
Start a thread with an infinite loop, you've just leaked some memory.
The language just can't prevent you.
You absolutely do get used to it and I really like the built in formatters. I would write everything in Rust going forward if I could, it really is a different way of thinking about how to write code. I don’t agree it’s the right tool for every job though.
What the heck is this.
Hell yeah, been saying this more than 2 years already
I've built apps recently in swift, rust, zig, node with HTML UI (not electron) for MacOS and I care less about the code as the AI agent does a perfect job in all of them, even if I don't see much difference in the results (except HTML which is pretty but weird)
Asked the same question to AI and she told me Swift is the best choice for MacOS apps just because it doesn't need C bridges between their kits/libraries and that in itself is an optimization gain
Of course Swift in Linux is not the best option, probably the last, in Microsoft probably .NET I don't know, haven't used MS products in twenty years
The point is, language selection is best decided on platform optimization, not on personal preference. I guess AI killed language wars?
Topcoat is very cool though, and there’s a few others in this space too, like Loco. So maybe we’ll see folks move to something more batteries included.
That's why I created SnapFire FSR (https://www.snapfirers.com), but many examples, to allow frontend devs to develop in their favorite framework (or mixture of). Write Tera templates, Web Components if you don't want to bring in frameworks like I tend to.
I found it much better to create and maintain web apps near the markup and language they originated from. You won't find me writing straight Rust for Web Apps unless I need to anymore.
Better leave Rust for when any kind of automatic resource management isn't acceptable, either due technical or social reasons.
Also here is another point, AI agentic programming is basically using automatic resource management infrastructure.
When you are no longer at the steering wheel, if the taxi has manual or automatic is irrelevant to reach the destination.
I'd personally like my ORMs (if I'm going to use them) to be closer and more explicit about the queries that I'm writing. ORMs tend to add a lot of complexity for very little benefit. SQLx is much closer to my cup of tea than Toasty.
Underlying DBs have different characteristics, so trying to make an abstraction layer for multiple dbs (including no-sql in the docs) means you are going to run into limitations and abstraction leaks that are hard to square. Correctly using the likes of Dynamodb is very different from correctly using postgres, for example.