Welcome to my blog

I’ve been wanting to write this post for a while, about what I think is an elegant way to solve some constraint satisfaction problems. Constraints tend to come up fairly often in real world programs, and some times it can be effective to treat them as constraint satisfaction problems. This post has a bit of background on constraint satisfaction problems I’ve encountered recently, then it goes over to develop Rust code for an algorithm that we can easily use to solve some Advent of Code problems, and we use it to make a solver for sudoku puzzles. Along the way, we explain the syntax we use, it shouldn’t be too hard to understand for someone who is unfamiliar with the language. ...

The past few weeks I’ve been experimenting with DuckDB, and as a consequence I’ve ended up talking about it a lot as well. I’m not going to lie, I really like it! However, experienced programmers will rightly be skeptical to add new technology that overlaps with something that already works great. So why not just use postgres? Well, I really like postgres too, and I think you should consider just using it! But despite both of these technologies being all about tabular data, they’re not really for the same kinds of problems. I think DuckDB is primarily an analysis or ELT tool, and it really excels in this space. postgres can do a lot of the things that DuckDB can do, but not nearly as fast or easily. I wouldn’t want to use DuckDB for a transactional workload, so it’s not going to replace postgres for anything that I use it for. ...

In the winter of 2023, Jordan Tigani declared that big data is dead. In this blog post, he makes several claims about how much data that businesses typically query. It is easy to interpret this to mean that big data was just a hype and most of us shouldn’t need to concern ourselves with it. I don’t think that’s the intended interpretation and I think that would be a mistake. The big data hype has lead to a lot of exciting innovations that increases the capability of commodity machines. I think it’s unwise to ignore that, although I completely agree that most organizations do not need to set up multi-petabyte data platforms. ...

It’s a Tuesday afternoon, and management have been in a state of poorly contained hysteria all day, running in and out of meetings continuously. The team is trying to focus on work, but it’s hard to avoid gossiping. “Do you think it’s ransomware?” says your colleague, and you shake your head. You’ve had the same thought of course, but #security in slack is in the normal state of blissful silence and there’s nothing going on in #ops either. It’s about time for the yearly reorganization, but that usually doesn’t cause so much… distress. Maybe there’s been some scandal at the recent strategy retreat. Did the CEO lose their executive decision dice again? ...

I’ve been using LLMs at some capacity in my work for more than 2 years now. When ChatGPT launched, it was immediately obvious to me that it was valuable as a learning tool. When GPT-4 launched, I started using it very actively as a sparring partner. I signed up for GitHub Copilot as soon as it was possible to use with Jetbrains IDEs. I pay for Jetbrains AI Assistant. I’ve tried running models locally and tried a variety of other cloud services as well. ...

One of my most vivid memories is from the day in my late teens when I first got a contact lens for my left eye. It took a long time to discover that I had poor vision on this eye, you see, like many people, I chose to keep both of my eyes open when I wasn’t sleeping. It was the headaches that sent me to a doctor. I was adamant that I could see well, but when he blocked my right eye, I had the humbling experience of no longer being able to read the biggest letters on the poster. It turned out that my headaches were probably due to my brain working overtime to interpret the world using mostly only one eye. My appointment with an optician was only a few days later, and I got to try a contact lens that same day. ...

A common way that code grows difficult to reason about is increasing the number of things you must keep in your head simultaneously to understand it. Often, this simply happens by adding one attribute, one variable, one column at a time. Some people are gifted with a great capacity for working memory, but most of us aren’t – having to hold the state of 5 variables in your head simultaneously to understand a piece of code may be pushing it far, according to this article from wikipedia: ...

It’s always an exciting day for me when I get access to the source code for an entirely new application I need to work on. How does it look inside, how does it work? Sometimes, there’s some design documentation along with it, or operational procedures, or maybe some developer handbook in a wiki. I do check all of those, but I don’t expect any of those things to accurately describe how the code works, because they tend to change less frequently. It’s also fairly low-bandwidth, it takes a ton of time to ingest technical text. ...

In my post about batch operations, I used the where id = any(:ids) pattern, with ids bound to a JDBC array. I’ve gotten questions about that afterwards, asking why I do it like that, instead of using in (:id1, :id2, ...). Many libraries can take care of the dynamic SQL generation for you, so often you can just write in (:ids), just like the array example. I would still prefer to use the = any(:ids) pattern, and I decided to write down my reasoning here. ...

Throughout a career as a software developer, you encounter many patterns. Some appear just often enough to remember that they exist, but you still need to look them up every time. I’ve discovered that writing things down helps me remember them more easily. This particular pattern is very useful for my current project. So, it’s time to write it down and hopefully commit it to memory properly this time. Although this post is specific to PostgreSQL, I’m sure other databases have the necessary features to achieve the same results efficiently. ...