I use Uptime Kuma to check the availability of a few services that I run, with the most important one being my blog. It’s really nice. Today I wanted to set it up on a different machine to help troubleshoot and confirm some latency issues that I’ve observed, and for that purpose I picked the cheapest ARM-based Hetzner Cloud VM hosted in Helsinki, Finland. Hetzner provides a public IPv6 address for free, but you have to pay extra for an IPv4 address. I didn’t want to do that out of principle, so I went ahead and copied my Docker Compose definition over to the new server. For some reason, Uptime Kuma would start up on the new IPv6-only VM, but it was unsuccessful in making requests to my services, which support both IPv4 and IPv6. The requests would time out and show up as “Pending” in the UI, and the service logs complained about not being able to deliver e-mails about the failures. I confirmed IPv6 connectivity within the container by running docker exec -it uptime-kuma bash and running a few curl and ping commands with IPv6 flags, had no issues with those. When I added a public IPv4 address to the container, everything started working again. I fixed the issue by explicitly disabling the IPv4 network in the Docker Compose service definition, and that did the trick, Uptime Kuma made successful requests towards my services. It seems that the service defaults to IPv4 due to the internal Docker network giving it an IPv4 network to work with, and that causes issues when your machine doesn’t have any IPv4 network or public IPv4 address associated with it. Here’s an example Docker Compose file: name: uptime-kuma services: uptime-kuma: container_name: uptime-kuma networks: - uptime-kuma ports: - 3001:3001" volumes: - /path/to/your/storage:/app/data image: docker.io/louislam/uptime-kuma restart: always networks: uptime-kuma: enable_ipv6: true enable_ipv4: false That’s it! If you’re interested in different ways to set up IPv6 networking in Docker, check out this overview that I wrote a while ago.

Today I learned that Kagi uses Yandex as part of its search infrastructure, making up about 2% of their costs, and their CEO has confirmed that they do not plan to change that. To quote: Yandex represents about 2% of our total costs and is only one of dozens of sources we use. To put this in perspective: removing any single source would degrade search quality for all users while having minimal economic impact on any particular region. The world doesn’t need another politicized search engine. It needs one that works exceptionally well, regardless of the political climate. That’s what we’re building. That is unfortunate, as I found Kagi to be a good product with an interesting take on utilizing LLM models with search that is kind of useful, but I cannot in good heart continue to support it while they unapologetically finance a major company that has ties to the Russian government, the same country that is actively waging a war against Ukraine, an European country, for over 11 years, during which they’ve committed countless war crimes against civilians and military personnel. Kagi has the freedom to decide how they build the best search engine, and I have the freedom to use something else. Please send all your whataboutisms to /dev/null.

It was time to upgrade Hibernate on that one Java monolithic1 backend service that my team was responsible for. We took great precautions with these types of changes due to the scale of the system, splitting changes into as many small parts as possible and releasing them as often as possible. With bigger changes we opted for running a few instances of the new version in parallel to the existing one. Then came Hibernate 5.2. Hibernate 5.2 introduced a new warning log to indicate that the existing API for writing queries is deprecated. Hibernate's legacy org.hibernate.Criteria API is deprecated; use the JPA javax.persistence.criteria.CriteriaQuery instead Every time you used the Criteria API it would print the line. Just one little issue there. Can you see it? Every time you used the Criteria API it would print the line. In a poorly written Java backend service, one HTTP request can make multiple queries to the database. With hundreds of millions of HTTP requests, this can easily balloon to billions of additional logs a day. Well, that’s exactly what happened to our service, resulting in the CPU usage jumping up considerably and the latency of the service being negatively impacted. We didn’t have the foresight to compare every metric against every instance of the service, and when the metrics were summarized across all instances, this increase was not that noticeable while both new and existing instances of the service were running. Aside from the service itself, this had negative effects downstream as well. If you have a solution for collecting your service logs for analysis and retention, and it’s priced on the amount of logs that you print out, then this can end up being a very costly issue for you. We resolved the issue by making a configuration change to our logger that disabled these specific logs. This does make me wonder who else may have been impacted by this change over the years and what that impact might’ve looked like regarding the resource usage on a world-wide scale. I’m not blaming the Hibernate developers, they had good intentions, but the impact of an innocent change like that was likely not taken into account for large-scale services. Last I heard, the people behind Hibernate are a very small team, and yet their software powers much of the world, including critical infrastructure like the banking system. I’m well aware that we’re talking about Hibernate releases that were released around the time I was still a junior developer (2016-2018). Some call it technical debt, others call it over half a decade of neglect. unmaintaned monoliths suck, but so do unmaintained microservices. ↩︎

I have worked with a few software developers who made the switch to this industry in the middle of their careers. A major change like that can be scary and raise a lot of fears and doubts, but I can attest that this can work out well with the right personality traits and a supporting environment. Here’s what I’ve observed. To keep the writing concise, I’ll be using the phrase “senior junior”1 to describe those that have made such a career switch. Overcoming the fear Fear is a natural reaction to any major change in life, especially when there’s risk of taking a financial hit while you have a family to support and a home loan to pay. The best mitigation that I’ve heard is believing that you can make the change, successfully. It sounds like an oversimplification, sure, as all it does is that it removes a mental blocker and throws out the self-doubt. And yet it works unreasonably well. It also helps if you have at least some savings to help mitigate the financial risk. A years’ worth of expenses saved up can go a long way in providing a solid safety net. What makes them succeed A great software developer is not someone that simply slings some code over the wall and spends all of their day working only on the technical stuff, there are quite a few critical skills that one needs to succeed. This is not an exhaustive list, but I’ve personally observed that the following ones are the most critical: ability to work in a team great communication skills conflict resolution ability to make decisions in the context of product development and business goals maintaining an environment of psychological safety Those with more than a decade of experience in another role or industry will most likely have a lot of these skills covered already, and they can bring that skill set into a software development team while working with the team to build their technical skill set. Software development is not special, at the end of they day, you’re still interacting with humans and everything that comes with that, good or bad. After working with juniors that are fresh out of school and “senior juniors” who have more career experience than I do, I have concluded that the ones that end up being great software developers have one thing in common: the passion and drive to learn everything about the role and the work we do. One highlight that I often like to share in discussions is one software developer who used to work in manufacturing. At some point they got interested in learning how they can use software to make work more efficient. They started with an MVP solution involving a big TV and Google Sheets, then they started learning about web development for a solution in a different area of the business, and ended up building a basic inventory system for the warehouse. After 2-3 years of self-learning outside of work hours and deploying to production in the most literal sense, they ended up joining my team. They got up to speed very quickly and ended up being a very valuable contributor in the team. In another example, I have worked with someone who previously held a position as a technical draftsman and 3D designer in a ship building factory (professionals call it a shipyard), but after some twists and turns ended up at a course for those interested in making a career switch, which led to them eventually working in the same company I do. Now they ship builds with confidence while making sure that the critical system we are working on stays stable. That developer also kicks my ass in foosball about 99% of the time. The domain knowledge advantage The combination of industry experience and software development skills is an incredibly powerful one. When a software developer starts work in a project, they learn the business domain piece by piece, eventually reaching a state where they have a slight idea about how the business operates, but never the full picture. Speaking with their end users will help come a long way, but there are always some details that get lost in that process. Someone coming from the industry will have in-depth knowledge about the business, how it operates, where the money comes from, what are the main pain points and where are the opportunities for automation. They will know what problems need solving, and the basic technical know-how on how to try solving them. Like a product owner, but on steroids. Software developers often fall into the trap of creating a startup to scratch that itch they have for building new things, or trying out technologies that have for a very long time been on their to-do list. The technical problems are fun to solve, sure, but the focus should be on the actual problem that needs fixing. If I wanted to start a new startup with someone, I’d look for someone working in an industry that I’m interested in and who understands the software development basics. Or maybe I’m just looking for an excellent product owner. How to help them succeed If you have a “senior junior” software developer on your team, then there really isn’t anything special you’d need to do compared to any other new joiner. Do your best to foster a culture of psychological safety, have regular 1-1s with them, and make sure to pair them up with more experienced team members as often as possible. A little bit of encouragement in challenging environments or periods of self-doubt can also go a long way. Temporary setbacks are temporary, after all. What about “AI”? Don’t worry about all that “AI”2 hype, if it was as successful in replacing all software development jobs as a lof of people like to shout from the rooftops, then it would have already done so. At best, it’s a slight productivity boost3 at the cost of a huge negative impact on the environment. Closing thoughts If you’re someone that has thought about working as a software developer or who is simply excited about all the ways that software can be used to solve actual business problems and build something from nothing, then I definitely recommend giving it a go, assuming that you have the safety net and risk appetite to do so. For reference, my journey towards software development looked like this, plus a few stints of working as a newspaper seller or a grocery store worker. who do you call a “senior senior” developer, a senile developer? ↩︎ spicy autocomplete engines (also known as LLM-s) do not count as actual artificial intelligence. ↩︎ what fascinates me about all the arguments around “AI” (LLM-s) is the feeling of being more productive. But how do you actually measure developer productivity, and do you account for possible reduced velocity later on when you’ve mistaken code generation speed as velocity and introduced hard to catch bugs into the code base that need to be resolved when they inevitably become an issue? ↩︎