fidzu : thunderbird

After nearly 4 years of active development, the next-generation trait solver is close to stabilization. We are enabling it by default on nightly to surface any remaining issues and plan to stabilize it in the next months. This is the largest single change to the Rust compiler since its initial release. It completely replaces how we prove where-clauses, normalize associated types, and much more. Please try out the latest nightly and open an issue if you encounter any bugs or regressions.

This is an internal component of the compiler. The main benefits of this rework will come in the future. The removal of the old implementation will unblock features such as Type Alias Impl Trait and Return Type Notation, allow us to add new implicit default trait bounds (e.g., Move and Forget), and enable us to fix the remaining type system unsoundnesses.

Even so, this already fixes a huge number of issues. As an underapproximation, we currently know of more than 200 issues on GitHub fixed by this change. This also has a significant impact on compile times; more on that later. When developing on nightly, you may accidentally rely on behavior only supported by the new trait solver.

This is an incredibly big change which results in a non-trivial amount of breakage. Most of these changes are intended improvements to type inference or the removal of undesirable behavior. We are tracking the known issues and breakage in a pinned GitHub issue.

What can I do?

Please update to the latest nightly version by using rustup update nightly and use it to test your existing projects and libraries.

⚠️ While the next-generation trait solver has been enabled on our main branch, this change will only be accessible on the nightly channel starting from Saturday 22nd August. You can already test it before then by providing -Znext-solver=globally as a command-line argument ⚠️

Please tell us if you encounter any breakage, compile-time performance regression, or bad diagnostics. We have not yet spent too much time on error messages for the next-generation trait solver, so we would also appreciate you using this nightly for development to find poor diagnostics and other bugs in our error handling.

If you encounter any issue, take a quick look at the pinned GitHub issue to see if the affected crate is already listed, and if not, please open a new issue! To disable the next-generation trait solver on nightly, you can pass -Znext-solver=coherence to rustc, use RUSTFLAGS=-Znext-solver=coherence, or change your project's .cargo/config.toml configuration file:

[build]
rustflags = ["-Znext-solver=coherence"]

What exactly does this mean?

We will go into more detail about the next-generation trait solver, how we got here, and what it changes when fully stabilizing it. This is a quick summary of its main impact.

impl Trait handling

The way opaque types - return-position impl Trait (RPIT), but also the unstable Type Alias Impl Trait (TAIT) and Return Type Notation (RTN) - are handled in the type system has nearly completely changed. This fixes a lot of bugs and edge cases with them and should make their behavior a lot more consistent in general. This change is why the next-generation trait solver is necessary to stabilize TAIT and RTN.

The implementation change mostly does not matter for RPIT as we special-cased impl Trait from the method signature when type checking the method body. This means the only way to observe the old behavior is via recursive function calls. The following snippet errors with the existing implementation, but compiles with -Znext-solver enabled: godbolt

fn foo(b: bool) -> impl Sized {
    if b {
        // The old implementation errored here.
        foo(false) + 1
    } else {
        0
    }
}

Associated types in higher-ranked types

The most impactful change is way we handle associated types referencing bound variables, i.e., lifetimes from a for<'a> binder, for example, the type for<'a> fn(<T as Trait>::Assoc<'a>). While most users don't encounter such types directly, there are widely used crates which do. This change impacts existing code by removing incorrect type inference, such as in bevy and minijinja.

It also fixes a bunch of unnecessary errors like in the following example: godbolt

trait OtherTrait {
    type Assoc<'a>;
}
impl OtherTrait for u32 {
    type Assoc<'a> = &'a u32;
}


trait Trait {}
impl<T: OtherTrait> Trait for (T, for<'a> fn(<T as OtherTrait>::Assoc<'a>)) {}


fn impls<T: Trait>() {}

fn main() {
    // The old implementation failed to prove
    // the where-bound of `impls`.
    impls::<(u32, for<'a> fn(&'a u32))>();
}

Compile-time performance

_{co-authored with jana :3}

We've spent a lot of time on the compile-time performance of the next-generation trait solver. There have been many cases where it performed quadratically or even exponentially slower than the old solver.

Especially the last few weeks were mainly spent on improving performance. This work was shared by many people, with major contributions by Nick Nethercote, jana, Rémy Rakic, and mira.

As part of this effort, Rémy Rakic compared the performance of both implementations for the top 20,000 crates on crates.io. Below you is a visualization of the performance changes over the last two months.

On the left and the right, the major outliers can be found. Note that the sample of crates here is biased towards such crates, because those are more interesting to us. Nearly all crates we tested in the top 20k had effectively the same performance with both implementations.

This graph shows that we've mainly focused our efforts on the negative outliers and made significant progress there. While many of the crates that previously took more than twice as long to compile with the new solver are still slightly slower, our work has made a few of them actually compile faster than with the old solver.

We will continue to improve its performance over the coming months, and there are still a lot of optimization opportunities compared to the existing implementation. My expectation is that, in the long term, nearly all crates will benefit from the next-generation trait solver. I am especially excited about the huge performance benefits for some trait-heavy crates.

As an example, a Chess implementation in Rust's type system hangs with the old implementation while taking a minute with the new one. There are also more practical crates with huge performance benefits, e.g., the datafusion crate compiles more than 8x faster now. For more details about the recent performance work, see this blog post by jana.

---

Again, thank you for testing with the latest nightly and opening a GitHub issue if you encounter any issues! We're excited to fully stabilize the next-generation trait solver soon.

21 Aug 2026 12:00am GMT

There's a popular post going around about Alibaba doing WebAudio fingerprinting and in the process, mucking around with the author's Bluetooth headphones. Here's the tl;dr: browser fingerprinting is a far-too-pervasive method of tracking users across the web, but at least for WebAudio specifically, it's not very effective. Firefox has largely eliminated this fingerprinting vector.

I work on Firefox. Chrome, Brave, Safari probably have defenses against this. I'm pretty sure Chrome's WebAudio code was made approximately constant (like ours) many years ago. It probably still leaks CPU architecture though.

I don't fully agree with the adage "given enough eyeballs, all bugs are shallow" - I think a more true one might be "given enough victims, your attack code is going to change something that makes someone notice". And that's exactly what happened with... sorry, wrong link. That's exactly what happened with Alibaba's fingerprinting code.

If you want to test Alibaba's WebAudio fingerprint, I had claude extract it into a standalone page. My value is sha256:9a388c0dd04cfdc54314f9d961c7e2d247b972067e28d1cea76bd6060cf1392e, and the other value we saw in an internal survey was sha256:16d3191880ce01f726015ec6a1f9a072a81ebd04bf489098d4685d1d1c0b2711.

WebAudio Fingerprinting in Firefox

We made the WebAudio constant in Firefox 118 as part of our initial round of Fingerprinting Protection features. This eliminated most of the differences.

99.24% of users belong to one of three values, and .76% of users had that data collection point fail (a value of zero.) So virtually all users have one of these three values but why are there three values??? Why isn't there one?? Well this is why we collected this telemetry - to ensure we really had made things constant the way we thought we had, and if we hadn't, figure out why and fix it.

The improvements we put into 118 eliminated a lot of differences and I wish I had a graph I could show you of what it looked like before (but I don't.) But what about these remaining three buckets? We were able to figure out that the differences between these are CPU-level differences, which is not surprising given the math operations of audio processing. Specifically one value comes from (all) x86 CPUs and x64 CPUs that lack fused multiply-add instructions, one value from x64 with FMA, and one value from CPUs with the NEON instruction set (ARM).

We followed up on that effort to collapse these buckets into one another. Bug 2036977 collapses the x64 (with FMA3) into the x86/x64 (without FMA3) bucket. Bug 2040494 is on file to collapse the remaining bucket into the NEON bucket but I haven't worked on it in a while because it's just not as high a priority as larger improvements we can make like Sanitizing the WebGL Renderer and Vendor (something I talked a bit about a few months ago).

Because if you've read this far you surely care about the rest of the graph also. There is also the long-tail of 23 other values belonging to 48 other users. This is very unfortunate, as it makes these users completely unique, but it is also not terribly unusual - computers are weird and these results could have been caused by bad RAM, a CPU bug, or possibly some crazy architecture (LoongArch??).

But at the end of the day, WebAudio fingerprinting is nearly useless. I don't expect browser fingerprinting to disappear from websites entirely (unless some regulatory action occurs, fingers crossed) - it's still going to be effective against a majority of users on the web, but at least for privacy focused browsers, it should be wildly less effective.

20 Aug 2026 7:56pm GMT

Open or closed AI isn't a fringe question anymore; it's a live policy debate, with real consequences for who gets to build, audit, and compete in the AI ecosystem. But as that debate plays out in Washington, Brussels, and beyond, the harder questions are still very much up for discussion. What does meaningful openness actually require? Where should the guardrails sit? And who gets a say in setting them?

That's exactly the gap a group of us set out to close two years ago.

Back in 2024, Mozilla and the Columbia Institute of Global Politics brought together more than 40 researchers, builders, and policy experts for the Columbia Convening on Openness and AI. The goal was simple to state, but hard to do: figure out what "open" actually means once you're talking about foundational models rather than traditional software, and build a shared vocabulary that researchers, developers, policymakers, and civil society could all work from.

The convening was the start of a years-long effort to turn a room full of perspectives on openness into something durable and useful for developers, builders, and policymakers alike. Recently, that effort reached a new milestone: Communications of the ACM published "Unpacking Open Source AI: Toward a Framework for Openness in Foundation Models," which I co-authored alongside a host of other allies spanning academia, industry, and civil society.

The paper outlines a framework that does something the open source AI debate badly needs. It stops treating "open" as a single switch you flip on or off. Instead, it breaks the AI stack down into its component parts - data, code, model weights, documentation, and more - and looks at how openness at each layer shapes who actually gets to inspect, use, and build on a system. That kind of specificity matters. A lot of the current policy debate treats open source AI as a monolith, when in reality the details of what gets shared (and how) are what determine whether openness delivers on its promise of safety, competition, and accountability.

A few of the framework's ideas are worth pulling out. First, it treats openness as a gradient rather than a binary. A system can be open on weights while remaining closed on training data, or open on code while offering little documentation on how it was evaluated. The framework offers language to describe that mix precisely, instead of lumping it under one vague label. Second, it argues that safety can't be assessed at the model level alone: what matters just as much is the surrounding system - the deployment environment, the safeguards, the moderation layers, and governance structures. That's because a model's real-world risk profile is shaped by all of it together, not by the weights in isolation. Critically, the framework deliberately stops short of prescribing one "correct" level of openness for every system. Instead, it gives developers, researchers, and regulators a common vocabulary to make and evaluate those tradeoffs on a case-by-case basis.

It's worth pausing on who's behind this work. Scroll through the author list, and you'll find a genuine cross-section of the individuals and organizations contributing to moving the development of open systems forward. It's a collection of people who've spent years arguing, in good faith and often across real disagreements, that open approaches can be a foundation for safer, more democratic, more accountable AI. That's the same spirit that drove the original convening, and it's what makes this paper a genuinely important milestone for our mission to make AI systems accessible to the many, rather than the few.

You can read the full paper here.

The post A New Framework for Open Source AI appeared first on The Mozilla Blog.

20 Aug 2026 3:57pm GMT