Salt is a systems programming language that embeds the Z3 SMT solver in the compiler.
You add `requires` and `ensures` clauses to functions, and the compiler proves them at compile time. When Z3 succeeds, the check is elided (zero instructions emitted).
When it fails, you get a counterexample. When it times out (100ms limit per obligation), the check is skipped and counted.
It compiles through MLIR to LLVM and targets KeuOS, a microkernel with an ECS (Entity Component System) architecture. Both are MIT-licensed.
The key difference from Rust/Zig/C: the compiler calls Z3 during normal compilation. No separate verification tool, no annotation language, no proof assistant. The contract syntax is part of the language.
---
What's real
- Compiler: 1,752 unit tests passing, clippy clean. Compiles through MLIR to LLVM IR. x86-64 and ARM64 backends.
- Kernel: 14/14 QEMU e2e tests pass. TCP stack (connect/send/recv/close), ICMP, deterministic builds. NetD (network daemon) runs as a Ring 3 process on SPSC shared memory rings.
- ECS architecture: 13 entity syscalls (402-413). Entity lifecycle (spawn/exit/wait), memory regions as entities (map/brk/alloc), I/O routing via capabilities, socket entity tracking, performance counters, world persistence diagnostics.
- Shell: Inline `ecs`, `ps_ecs`, `free_ecs` commands query ECS World without spawning child processes.
- Benchmarks: Salt vs C (`clang -O3`) on 21 algorithm benchmarks. Salt at parity or faster on 19/21. Allocation-heavy workloads (hashmap, LRU, buffered writer) see 2-10x wins from arena allocation. Compute-bound (matmul, sieve, fib) at 0.9-1.0x of C.
- LSP: VS Code extension ships with semantic tokens, go-to-def, find-refs, Z3 hover.
---
What's not done (research-quality, not production)
- The standard library is incomplete. Many things you'd expect are missing.
- Z3 handles integer arithmetic, bit-vectors, and reals. String and quantifier support is partial. Contracts outside Z3's reach are compile-time checked where possible, silently skipped otherwise.
- Error messages from the Z3 pass can be opaque.
- The kernel targets QEMU (x86-64). Tested on AWS bare metal instances, not local 'bare metal' yet.
- One nights-and-weekends developer.
---
Why this exists
The goal was to find out whether formal verification could be a compiler feature rather than a separate toolchain. The benchmarks say the compiler is fast enough (Lettuce compiles in under a second with contracts enabled). The kernel contracts catch real bugs. But the language hasn't been used by anyone outside the project, and that's the test that matters.
You could nitpick the subsection title, and from that perspective "What's not done" is a bit of a catch-all for caveats.
With that said, I think it's pretty easy to infer the meaning there.
I'm not really sure how else to be more transparent about things, but I am using an AI-augmented engineering as the core workflow for the project, and that includes drafting docs and this post.
I read and edit the work. For a side-project with no current community support or users, I need to make a judicious decision when to spend time and effort. IMO it is reasonable _not_ to try to trick anyone into thinking that AI is not used. My proof reading and editing will miss things from time-to-time. I'm OK with it at the current scale of the project.
This looks pretty impressive but it’s all AI-generated (or written in a similar style) and therefore the documentation is lacking.
There is a language specification [1][2] but it lacks coherence.
I think the way to improve it would be to try to teach this language to people and get feedback from them. That is, it needs beta testers. It looks like there’s no community of users yet?
> [int overflows, etc.] No runtime cost when Z3 can prove it. Otherwise, the compiler emits a safe runtime check as fallback.
Super interesting approach. I see this eventually be integrated into future mainstream languages, though that may take a while. I suspect that the game programming crowd will try to use it first, due to the possibility to prove certain edge cases at compile time and skip the runtime cost. But perhaps this optimization drive is no longer the case because we've got bazillions of cores nowadays. I may be too old for these predictions. Cool nonetheless.
For people looking for other languages with statically checked contracts, you might want to check out SPARK, which has been around in some form since the late 1980s. It is a subset of the Ada language and had been used for safety critical code in aerospace and defense projects, as well as for some Nvidia firmware.
It also uses Z3 to discharge proof obligations generated by the contract annotations, and it lets you use swap out different theorem provers as backends.
The GNAT Ada compiler (which is part of GCC) allows you to turn off the dynamic safety checks that are usually inserted into Ada programs at build time so you can optionally remove them if they are proven unnecessary.
Moving theorem proving upstream into compilers, sandboxes, and the browser seems like the future when we're dealing with increasingly sophisticated AIs. I'm working on similar formal methods but applied to agent sandboxing; do you see Z3 as a better fit than lean? https://github.com/coproduct-opensource/nucleus
Salt chose Z3 because it felt right for a compiler. The 100ms timeout means it's not sound, but it's useful. Lean could be the right choice when a proof is a hard requirement?
> Moving theorem proving upstream into compilers, sandboxes, and the browser seems like the future
The way I saw this proposed, back in the 2000s: "proof-carrying code."[0]
The idea is: the compiler compiles the program, and simultaneously generates a proof that the program doesn't violate X, Y, or Z safety rules.
Later, the end-user downloads the program and the proof together, and the local execution environment (the browser, the OS itself, etc) verifies that proof.
The idea being: constructing proofs is hard and sometimes involves manual steps. But verifying a proof is easy and automatic. So do the slow, manual thing once, and the fast thing each time the program is downloaded.
Sounds a bit like Microsoft's Singularity project, though I don't know if they even used a proof system to ensure a program couldn't do bad stuff(TM). After verification they just let the program run in Ring 0 (kernel space) of the CPU to skip even the performance hit of the cpu's isolation.
The designed arena allocation is both faster and more predictable than GC. The trade-off is that you have to think about memory regions, but you never pay a GC pause.
Bounds and div-by-zero are the easy wins; the real tax with contract systems is the loop invariant Z3 can't infer on its own, where you end up hand-writing an `ensures` longer than the function body. What's the gnarliest invariant you had to spell out by hand to get one of those kernels to verify?
For loops already worked but might not have been documented correctly. I just shipped the functionality for `while` loops now, auto-infer works for common cases of invariants:
```
let mut i = 0;
while i < 5 {
arr[i] = val;
i += 1;
}
```
So in that case, the compiler detects i < N with monotonic increment and synthesizes `i >= 0 && i < N` automatically.
(Works with <, <=, i += 1, and i = i + 1. Same mechanism as for-loop induction variables — zero annotations, Z3 proof.)
For complex loops (while i + j < n, while p.addr() != 0) I have slopped together an explicit `invariant` keyword that I am not 100% happy with, but will refine later. Z3 checks base case + inductive step via Hoare logic and reports counterexamples on failure.
---
RE: "gnarliest invariant"
`ensures(result != 0)` on the SYN cookie in the kernel contract for the OS project in the monorepo.
The more interesting loop invariants aren't the ones about the loop counter but about the contents of the array. If you require `forall i, array[i] == value` after that loop, will the system come up with the loop invariant about the prefix of the array for any given `i`? What about something like bubblesort or insertion sort?
Invariants aren't always inferred automatically. When they aren't, they can be made explicit for Z3 to check.
At the moment, bubble sort with array-content invariants is fully provable:
fn bubble_sort(arr: Ptr<i32>, n: i64)
requires n > 0
ensures forall i in 0..(n-1) => arr[i] <= arr[i+1]
{
for i in 0..n {
invariant forall k in 0..(i-1) => arr[k] <= arr[k+1];
// bubble pass
}
}
Z3 proves the base case (vacuously true at i=0) and the inductive step for each iteration. Both loops have fixed trip counts regardless of data, so the frame axioms are concrete. At concrete sizes the compiler unrolls the loop: for i in 0..4 produces 8 Z3 checks (4 iterations × 2), all proven at compile time, outputting Z3: 8/8 checks proven (100%), 0 deferred to runtime.
And insertion sort is partially provable:
This starts with the same `forall` invariant structure. After the base case, the outer for-loop inductive step is wired but the inner while-loop's trip count depends on the data which can't be determined statically for which indices were modified, so the frame axioms can't fully constrain the array state. This would require splitting on the case for the while condition, which is future work, and wasn't part of my v1.0.0 plans.
There's a comment saying every function in that file has requires clauses, but... They don't?
Also, the actual implementation of matrix multiply is manually tiled, while the website boasts about automatic loop tiling. It's hard to know what to trust here.
For here (and the website), I would be more interested in trustworthy information about what is actually verified at compile time, and what happens when verification fails.
What if you write a function that takes an array (as an array type if you have one, or as a pointer, or a slice, or whatever). It returns `array[0] + array[1]`, but it has no bounds checks in the code, neither as actual `if` statements nor as `requires` clauses.
Verification of such a function must fail at compile time, right? Does it? What does the error message look like? Someone else found some quote somewhere that seems to suggest that integer overflow checks are implemented as runtime checks if you can't eliminate them at compile time. Does that also happen for bounds checks? If yes, does the user get a warning saying that M out of N checks were moved to run time?
It's just really unclear if you are really doing "Verified safety without the runtime cost." or if you have reinvented Java, which will be just as good (actually better) at eliminating these bounds checks as compile time.
27 comments
[ 3.2 ms ] story [ 47.3 ms ] threadYou add `requires` and `ensures` clauses to functions, and the compiler proves them at compile time. When Z3 succeeds, the check is elided (zero instructions emitted).
When it fails, you get a counterexample. When it times out (100ms limit per obligation), the check is skipped and counted.
It compiles through MLIR to LLVM and targets KeuOS, a microkernel with an ECS (Entity Component System) architecture. Both are MIT-licensed.
---
How it works
Call `safe_div(x, 7)` and Z3 proves `7 != 0`. Check elided.
Call `safe_div(x, 0)` and the compiler stops.
The key difference from Rust/Zig/C: the compiler calls Z3 during normal compilation. No separate verification tool, no annotation language, no proof assistant. The contract syntax is part of the language.
---
What's real
- Compiler: 1,752 unit tests passing, clippy clean. Compiles through MLIR to LLVM IR. x86-64 and ARM64 backends. - Kernel: 14/14 QEMU e2e tests pass. TCP stack (connect/send/recv/close), ICMP, deterministic builds. NetD (network daemon) runs as a Ring 3 process on SPSC shared memory rings.
- ECS architecture: 13 entity syscalls (402-413). Entity lifecycle (spawn/exit/wait), memory regions as entities (map/brk/alloc), I/O routing via capabilities, socket entity tracking, performance counters, world persistence diagnostics.
- Shell: Inline `ecs`, `ps_ecs`, `free_ecs` commands query ECS World without spawning child processes.
- Benchmarks: Salt vs C (`clang -O3`) on 21 algorithm benchmarks. Salt at parity or faster on 19/21. Allocation-heavy workloads (hashmap, LRU, buffered writer) see 2-10x wins from arena allocation. Compute-bound (matmul, sieve, fib) at 0.9-1.0x of C.
- LSP: VS Code extension ships with semantic tokens, go-to-def, find-refs, Z3 hover.
---
What's not done (research-quality, not production)
- The standard library is incomplete. Many things you'd expect are missing.
- Z3 handles integer arithmetic, bit-vectors, and reals. String and quantifier support is partial. Contracts outside Z3's reach are compile-time checked where possible, silently skipped otherwise.
- Error messages from the Z3 pass can be opaque.
- The kernel targets QEMU (x86-64). Tested on AWS bare metal instances, not local 'bare metal' yet.
- One nights-and-weekends developer.
---
Why this exists
The goal was to find out whether formal verification could be a compiler feature rather than a separate toolchain. The benchmarks say the compiler is fast enough (Lettuce compiles in under a second with contracts enabled). The kernel contracts catch real bugs. But the language hasn't been used by anyone outside the project, and that's the test that matters.
---
Links
- Source: https://github.com/bneb/lattice)
- Tutorial: https://github.com/bneb/lattice/blob/main/docs/tutorial/your...
- Architecture: https://github.com/bneb/lattice/blob/main/docs/ARCH.md
- Benchmarks: https://github.com/bneb/lattice/blob/main/benchmarks/BENCHMA...
What?
With that said, I think it's pretty easy to infer the meaning there.
I'm not really sure how else to be more transparent about things, but I am using an AI-augmented engineering as the core workflow for the project, and that includes drafting docs and this post.
I read and edit the work. For a side-project with no current community support or users, I need to make a judicious decision when to spend time and effort. IMO it is reasonable _not_ to try to trick anyone into thinking that AI is not used. My proof reading and editing will miss things from time-to-time. I'm OK with it at the current scale of the project.
There is a language specification [1][2] but it lacks coherence.
I think the way to improve it would be to try to teach this language to people and get feedback from them. That is, it needs beta testers. It looks like there’s no community of users yet?
[1] https://github.com/bneb/lattice/blob/main/docs/SPEC.md
[2] https://github.com/bneb/lattice/blob/main/SYNTAX.md
Super interesting approach. I see this eventually be integrated into future mainstream languages, though that may take a while. I suspect that the game programming crowd will try to use it first, due to the possibility to prove certain edge cases at compile time and skip the runtime cost. But perhaps this optimization drive is no longer the case because we've got bazillions of cores nowadays. I may be too old for these predictions. Cool nonetheless.
It also uses Z3 to discharge proof obligations generated by the contract annotations, and it lets you use swap out different theorem provers as backends.
The GNAT Ada compiler (which is part of GCC) allows you to turn off the dynamic safety checks that are usually inserted into Ada programs at build time so you can optionally remove them if they are proven unnecessary.
Here are some resources for comparison:
- https://www.adacore.com/languages/spark
- https://learn.adacore.com/courses/intro-to-spark/chapters/01...
The way I saw this proposed, back in the 2000s: "proof-carrying code."[0]
The idea is: the compiler compiles the program, and simultaneously generates a proof that the program doesn't violate X, Y, or Z safety rules.
Later, the end-user downloads the program and the proof together, and the local execution environment (the browser, the OS itself, etc) verifies that proof.
The idea being: constructing proofs is hard and sometimes involves manual steps. But verifying a proof is easy and automatic. So do the slow, manual thing once, and the fast thing each time the program is downloaded.
[0]: https://en.wikipedia.org/wiki/Proof-carrying_code
It augments Rust with Z3 and is not a pile of unverified slop.
The designed arena allocation is both faster and more predictable than GC. The trade-off is that you have to think about memory regions, but you never pay a GC pause.
```
let mut i = 0;
while i < 5 {
}```
So in that case, the compiler detects i < N with monotonic increment and synthesizes `i >= 0 && i < N` automatically.
(Works with <, <=, i += 1, and i = i + 1. Same mechanism as for-loop induction variables — zero annotations, Z3 proof.)
For complex loops (while i + j < n, while p.addr() != 0) I have slopped together an explicit `invariant` keyword that I am not 100% happy with, but will refine later. Z3 checks base case + inductive step via Hoare logic and reports counterexamples on failure.
---
RE: "gnarliest invariant"
`ensures(result != 0)` on the SYN cookie in the kernel contract for the OS project in the monorepo.
---
Invariants aren't always inferred automatically. When they aren't, they can be made explicit for Z3 to check.
At the moment, bubble sort with array-content invariants is fully provable:
Z3 proves the base case (vacuously true at i=0) and the inductive step for each iteration. Both loops have fixed trip counts regardless of data, so the frame axioms are concrete. At concrete sizes the compiler unrolls the loop: for i in 0..4 produces 8 Z3 checks (4 iterations × 2), all proven at compile time, outputting Z3: 8/8 checks proven (100%), 0 deferred to runtime.And insertion sort is partially provable:
This starts with the same `forall` invariant structure. After the base case, the outer for-loop inductive step is wired but the inner while-loop's trip count depends on the data which can't be determined statically for which indices were modified, so the frame axioms can't fully constrain the array state. This would require splitting on the case for the while condition, which is future work, and wasn't part of my v1.0.0 plans.
See https://github.com/bneb/lattice/blob/main/docs/deep-dives/z3...
There's a comment saying every function in that file has requires clauses, but... They don't?
Also, the actual implementation of matrix multiply is manually tiled, while the website boasts about automatic loop tiling. It's hard to know what to trust here.
What if you write a function that takes an array (as an array type if you have one, or as a pointer, or a slice, or whatever). It returns `array[0] + array[1]`, but it has no bounds checks in the code, neither as actual `if` statements nor as `requires` clauses.
Verification of such a function must fail at compile time, right? Does it? What does the error message look like? Someone else found some quote somewhere that seems to suggest that integer overflow checks are implemented as runtime checks if you can't eliminate them at compile time. Does that also happen for bounds checks? If yes, does the user get a warning saying that M out of N checks were moved to run time?
It's just really unclear if you are really doing "Verified safety without the runtime cost." or if you have reinvented Java, which will be just as good (actually better) at eliminating these bounds checks as compile time.