Recent Blog Posts

I keep a little black book with ideas for businesses or projects and sometimes also technology I want to learn more about. One of those things I wanted to learn more about was the inner workings of a key-value store. I wanted to know how to allow for virtually infinite growth of such a store without sacrificing read or write speeds or how to best organize the data on a disk.

A while ago I discussed the Photo Search tool that I’ve created and that I use to index all my photos. One thing that had bothered me from the beginning was the need to use Python to load and use the models. I’m sure that there are some cases where using Python is not the worst choice, but those use cases typically involve rapid prototyping and not so much production-like scenarios where things like efficiency and resource consumption matter more.

I run a few of my workloads on VMs in Azure. Some of them deal with data and content that changes over time, and accordingly I like to have the data backed up periodically. Microsoft provides the AzCopy tool for uploading files to Azure blob storage alright, and it works very well with managed identities assigned to VMs (and other services in Azure).

But some of the same properties that apply to data also apply to the backups of that data: their value diminishes over time, so keeping backups for an extended amount of time is pointless. Accordingly, I always want to delete old backups after some time.

With the advent of publicly available LLMs and embedding models, I figured I’d kill two birds with one stone: I’d learn a bit about using such models, and I’d build a tool that lets me use a semantic search on my photos.

I keep those photos on a NAS in my home network, and frequently back them up using bart - my back-up and restore tool. So all I really need is a web site for showing the photos and letting me search them. That’s why I built photo search, a tool that uses publicly available multi-lingual models that work both on text and images to index and query photos based on the contents.

I’ve been running a honeypot for a while just to see what creepy things crawl around on the internet. One of the things I wanted to see is what credentials are probed when trying to sign in, so I equipped the honeypot with a fake WordPress login page.

Today, I’m writing about these credentials. For my evaluation I’m looking at data only from the past 6 months, more or less. On average, almost 80 attempts to login are made every day. Needless to say that for the honeypot, all attempts to login fail - there’s really nothing to login to.

One of the things I like about golang (and Rust too, by the way) is that it’s quite simple to build really small container images by statically linking the executables, and using scratch as the base image. I’ve done this a few times in the past, and was doing it again just recently. Except that this time around, I ran into issues: the container would crash soon after it started.

I recently had to switch phones, because my old phone conked out. I had an app on that phone that I used for short-lived MFA codes for various logins I use. That app was a poor choice, because it didn’t allow for a backup of the secrets used for the code generation, so I had to go to the relevant logins and one by one remove MFA, then add it again. While doing so, I was wondering how this stuff works underneath, so I started looking into this.

Over the years I’ve accumulated a few Raspberry Pis of different versions, each of which I’ve used for certain things, some of them I’ve retired or upgraded to newer versions. Consequently, I have a few Raspberry Pi 1 and 1B still around, and they’re not doing much aside from collecting dust. I also have an old Raspberry Pi NoIR camera that I haven’t been using much.

Over the years I’ve tried different things, including PIR-triggered recording of the camera feed, sort of to build a device that records what’s happening when the infrared sensor is triggered. While that actually worked just fine, I also wanted to be able to get a steady video stream from the camera that can be observed over the internet, if necessary.

First, let me state this more precisely: this is a post about generating c# code for ASP.Net Core from an Open API definition at build time using NSwag. If you’re looking for steps to generate code by using the NSwag toolchain manually, you won’t find that here. If you’re looking for a way to generate an Open API definition from an existing ASP.Net Core app using the NSwag toolchain, you won’t find that here either. In that latter case though you’ll get a statement from me telling you that for a professional service you probably shouldn’t do that: you wouldn’t define your interfaces after making the implementation either, right?

Earlier this year I’ve started using K3s regularly for local testing of Kubernetes workloads, APIs, tools, and more. I’ve grown quite fond of k3s for multiple reasons: it’s very easy and fast to install, very easy to use, and so far I have not found a single service designed for K8s that wouldn’t work on K3s. I’ve run it both on Linux machines as well as on Windows machines through WSL2. It just works. I use it in particular also to test my own K8s controllers and operators. Installation typically finishes in 15-20 seconds, and system pods are usually running fine after another at most 30 seconds.