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I recently added a new ‘about’ section to the top of my website. Like most about pages, it has a picture. Instead of a normal photograph, however, you’ll see an AI-generated avatar. This is admittedly fairly trendy at the minute – apps like Lensa offer to make you profile pictures if you give them a set of photos and some cash – but I’ve done something a bit different.

You see, there is not just one image that has been carefully curated, edited, and uploaded. No, the image you see quite possibly has never been seen before and will never be seen again. It’s unique. Just for you.

Background: Stable Diffusion, DreamBooth, et al

You’ve probably heard of Stable Diffusion, the open text-to-image model developed by LMU Munich. Given a text prompt it starts with a random array of static and repeatedly transforms it, each step moving away from pure entropy and towards a real image that befits the prompt. It stands in contrast to competitors like DALL-E and Midjourney in both the code and the model being freely and publicly available.

Six years ago, I described my system for configuring a reverse proxy for docker containers. It involved six containers including a key-value store and a webserver. Nothing in that system has persisted to this day. Don’t get me wrong – it worked – but there were a lot of rough edges and areas for improvement.

Microservices and their limitations

My goal was to follow the UNIX philosophy of “do one thing and do it well”. Unfortunately, that doesn’t really work when applied to network services that have to interact with one and other. UNIX tools are built upon a common file system and simple data passed over STDIN. Microservices don’t have that shared foundation. You could make one: companies that use microservices in anger often have a team that deals with the “developer experience” of creating and using microservices. But as a solo developer that’s not something I wanted to spend my time doing.

One of the biggest flaws in Docker’s design is that it wasn’t created with IPv6 in mind. Out of the box Docker assigns each container a private IPv4 address, and they won’t be able to reach IPv6-only services. While incoming connections might work, the containers won’t know the correct remote IP address which can cause problems for some applications. This situation is obviously suboptimal in the current day and age. It’s a bit like not supporting HTTPS on a website – you might not have any issues because of it immediately, but you’re fighting against the currents of progress and are making life worse for your users.

Thankfully, it’s now relatively easy to make Docker behave a lot nicer. The docker-ipv6nat project has been around since 2016, and uses an IPv6 overlay network and some iptables magic to route traffic to and from containers in a sensible fashion. It uses NAT to emulate the behaviour Docker employs for IPv4 traffic; while using NAT with IPv6 is an anathema, I think it makes sense for containers. You could give each container a publicly routable IPv6 address, but that brings with it a lot of headaches: you’re basically going to be forced to implement service discovery and some kind of DNS management to deal with the fact that your containers will be popping up on randomly assigned IP addresses. That is completely overkill for people running a small number of services on one or two physical boxes; and if it’s not overkill for you then you’re probably already looking at more complicated orchestration solutions like Kubernetes.

More recently, similar functionality has been built into the Docker daemon itself. You can now edit the config file to enable ipv6 and each container will be assigned an address in the range specified when it uses the default bridge network. This gives more-or-less the same functionality of docker-ipv6nat – you lose a little flexibility as you can’t disable IPv6 on the default bridge, but that’s a very worthy trade for having the functionality built-in.

So far this all seems very simple. Hardly worthy of being called an “adventure”. Enter stage left: the wicked witch of destination address selection…

Reproducible builds are builds which you are able to reproduce byte-for-byte, given the same source input. Your initial reaction to that statement might be “Aren’t nearly all builds ‘reproducible builds’, then? If I give my compiler a source file it will always give me the same binary, won’t it?” It sounds simple, like it’s something that should just be fundamentally true unless we go out of our way to break it, but in reality it’s actually quite a challenge. A group of Debian developers have been working on reproducible packages for the best part of a decade and while they’ve made fantastic progress, Debian still isn’t reproducible. Before we talk about why it’s a hard problem, let’s take a minute to ponder why it’s worth that much effort.

On supply chain attacks

Suppose you want to run some open-source software. One of the many benefits of open-source software is that anyone can look at the source and, in theory, spot bugs or malicious code. Some projects even have sponsored audits or penetration tests to affirm that the software is safe. But how do you actually deploy that software? You’re probably not building from source - more likely you’re using a package manager to install a pre-built version, or downloading a binary archive, or running a docker image. How do you know whoever prepared those binary artifacts did so from an un-doctored copy of the source? How do you know a middle-man hasn’t decided to add malware to the binaries to make money?

I run a fair number of services as docker containers. Recently, I’ve been moving away from pre-built images pulled from Docker Hub in favour of those I’ve hand-crafted myself. If you’re thinking “that sounds like a lot of effort”, you’re right. It also comes with a number of advantages, though, and has been a fairly fun journey.

The problems with Docker Hub and its images

Rate limits

For the last few years, I’ve been getting increasingly unhappy with Docker Hub itself. Docker-the-technology is wonderful, but Docker-the-company has been making some rather large missteps. The biggest and most impactful of these has been introducing “pull rate” limits. At the time of writing, if you want to just pull a public image without logging in then you are limited to 100 pulls every 6 hours. If you log in then you’re limited to 200 pulls per 6 hours, but it’s account wide. This might seem like a big enough number, but I repeatedly hit it and there is no way to actually audit what is causing it. I have various containers that may all pull images at arbitrary times (e.g. continuous integration build agents), and the only information you get back from Docker Hub is the number of pulls remaining.