ilikebits's comments

sccache caches at the per-rustc invocation level, whereas we cache at the package level. In practice, this means we get more cache hits and our cache hits take less overhead to load. You can see the benchmarks in our CI!

We haven't worked with Dioxus in particular, but the CLI is a project that we benchmark against: https://github.com/attunehq/benchmarks

Monads are a generalization of Promises. Each type in Monad defines their own `.then` in a different way. For promises, `.then` is defined as "run this function once you have this deferred value from the last promise". For optionals (`Maybe`), `.then` is defined as "run this function if the last optional had an actual value". For Either, `.then` is defined as "run this function if the last Either returned Right, otherwise early-return with the value from Left" (this is functional early-return, basically).

How do you compare against Morph (https://morphllm.com/)?

Sorry I missed this - let us know if there's anything we can do to help! Feel free to email me at [email protected].

Yup, this was similar to our experience as well. If publishing is still a source of toil for you, feel free to reach out at [email protected] - we'd love to see if we can do any dev work or hosting pro bono for your open source projects.

I'm not familiar with mise and ubi, but these look like they're tools that the end user runs to install binaries. In contrast, we're building something that sets up a server so that end users can use their existing `apt-get install`.

Thanks, appreciate it!

Yeah, solving this locally for one repository definitely isn't that hard at all. Most of the features we're building become useful when you're trying to build CI integrations for a larger team while also complying with enterprise security requirements (e.g. audit logging, HSM key protections, etc.).

That's true! The Debian and Ubuntu folks are also experts at this. In our experience, the sharp edges generally affect teams that don't have a lot of in-house expertise in this, and where release engineering is not a core engineering competency that they want to invest in.

We don't plan to do that right now. The CLI isn't that complicated, and the choice for that was driven more by Go's excellent cross-compilation story than anything else.

It would be a fun thing to do if we had the resources to get equally good cross-compilation in Rust, but we're focused on building functionality right now.

Yeah, multi-distro and multi-arch is something on our roadmap! We're especially interested in automating the more annoying OS-specific parts of releasing, like MacOS notarization.

Our use case is customers who (1) want to use a managed cloud hosting service that we provide, but (2) are not willing to give us their signing keys. Our design allows them to keep all of their signing keys local to their environment.

To our knowledge, we have not found another provider who supports both of these requirements. It's not some amazing technical innovation, but it is one of those annoying paper cuts that builds up with all the others.

Thanks for trying, and sorry in advance for the rough edges :)

We're also working on a hosted service! If you'd like a sneak peek, send us a message at [email protected] (or email me directly at [email protected]). I'm happy to talk about your specific needs and see if we can build something for them.

(And yes, it is Rust. I keep trying to find projects where I get to stretch my Haskell wings again, but unfortunately I keep working on things with silly requirements such as "easily understandable performance" and "a cross-compilation story" and "not making my collaborators sit through another monad tutorial".)

You are correct. The reason we built this tool is because all of the scripts we found had sharp edges when we tried to use them in production at scale (I talk about some of them here: https://news.ycombinator.com/item?id=43730136).

Most of these scripts were designed for a world where there was A Blessed Deployment Machine that acted as its own de facto centralized control plane. We're designed for a newer world where publishing is just another piece of your CI, so you need more features to handle concurrency control, distributed signing, incremental index rebuilds, etc.

One of the tools we tried was Aptly! A couple issues we had with Aptly:

1. Aptly needs to rebuild the entire repository before it can do any changes. One of our customers builds packages for every Linux OS, and every architecture, and each release contains 10-something separately packaged tools, and they do regular releases. This means their repository of packages is _huge_. When they try to do small maintenance actions like removing a package that has a bug, they need to rebuild their entire repository to do this, which takes upwards of 30 minutes. On Attune, since we have a centralized control plane, it takes about 10 seconds.

2. Aptly doesn't have concurrency controls. This makes it annoying to run in CI. Imagine you're setting up a CI pipeline that runs on every commit and publishes a package. You need to make sure you don't have two different instances of Aptly running at the same time, because their updates will clobber each other if they publish at the same time. Can you ensure that your CI only ever runs one publish at a time? This is easy in some setups, but surprisingly hard in GitHub Actions - concurrency keys don't quite work the way you'd expect them to.

3. Aptly uses its storage as its source of truth for the current state of packages. This is a great design choice for simplicity! However, if your CI job dies halfway through during an Aptly publish, this can leave your repository in a corrupted (in this case, half-updated) state. To recover from this, you need to rebuild the repository state... which you can't, because the ground truth is contained in the storage which was just corrupted! We mitigate this issue by tracking metadata in a separate transactional database so CI crashes don't cause corruption.

These are some examples of little cuts that build up over time with all of the tools we've found so far. Lots of the open source tooling is great at low scale, but the cuts start to build up once you are pushing a meaningful amount of packages to production.

Totally agree here. The current target is people who are familiar with the process, and we plan to write more documentation as we start thinking more about accessibility to a broader audience. We actually ran into this issue ourselves while building the tool - the Debian documentation is all there, but not the easiest to approach. These docs are one of the rough edges (sorry!) since we wanted to get this out earlier for feedback.

To answer your direct question: on self-hosted repositories, the URI right now is just a string identifier. We are working right now on a cloud hosted service, and there the URI will help us figure out which repository to actually serve.

Congratulations on the launch! I think you might have a typo in your screenshots, where "liabilites" should be spelled "liabilities".

This is great! I loved playing Sub Command as a kid, and seeing the very familiar waterfall display was a real blast from the past.

I've been following Unison since almost the beginning (back in the structure editor days!). It's a very cool project, https://www.unison.cloud/our-approach/ is a great read, and the Unison language (especially their formulation of effect handlers as "abilities") is very cool.

There are two specific things here that make me reluctant to use Unison Cloud in my own work:

1. It doesn't look like there's any FFI or way to shell out to other tools within Unison Cloud. I understand that this is necessary to provide the desired static guarantees, but the lack of an escape hatch for using pre-existing code makes this a really hard sell.

2. Typed storage is excellent! What are its performance characteristics? In my experience, gaining expressiveness in storage systems often requires trading away performance because being able to store more kinds of values means we have fewer invariants to enable performance optimizations. How do migrations work? I've always found online migrations to be a major pain point, especially because data rapidly becomes very heavy. (At a glance, it looks like storage is key-value with some DIY indexing primitives, and I couldn't find anything about migration.)

The approach article asks "Why is it so complicated, anyway?". My guess would be that:

1. For small projects where you can toss together NextJS and SQLite and throw it onto Hetzner, it really _isn't_ that complicated.

2. For large projects where with large amounts of data, high availability requirements, and very large scale, performance and operability matters a lot, and none of these all-in-one systems have yet to demonstrate good performance and operability at scale.

3. There really is not that much demand for projects between these two sizes.