Posts

I’ve been moving slowly away from Cloudflare tunnels and into a self hosted solution. For this, I use a self hosted instance of Pangolin. Pangolin allows to expose services from my homelab for the whole world to see, should I wish to do so. However, not all services are created equal and there are some that I’d like to only be accessible via VPN. Thankfully, Pangolin allows for this via its private resources. You can define a resource that is only accessible when you’re connected to the network via the pangolin client. It also allows setting a DNS alias for the resource in question, so you can point a subdomain like frigate.example.com to the internal resource. When connected via the client, the DNS will be resolved by a private DNS server running in the client and pointed in the right direction. This poses a small issue though - while Pangolin will happily pass ACME domain challenges for public resources, there’s no such mechanism at the moment for private resources. What this means is that you can’t easily obtain TLS certificates for such domains. ...

There are books & many articles online, like this one arguing for using Postgres for everything. I thought I’d take a look at one use case - using Postgres instead of Redis for caching. I work with APIs quite a bit, so I’d build a super simple HTTP server that responds with data from that cache. I’d start from Redis as this is something I frequently encounter at work, switch it out to Postgres using unlogged tables and see if there’s a difference. ...

I’ve self hosted an n8n to automate video uploads to youtube. The idea was pretty simple - when I put an mp4 file in a specific dir on my nas, n8n would pick it up, as well as a metadata.json file. The metadata file would contain things like the title and description, and it would upload this to youtube. I started creating this automation and pretty soon had something that resembled the finished workflow. I started testing it out. However, I ran into a problem. I had a Read/Write files from disk node in my workflow, and whenever I manually executed it, it would take way too long to finish - a couple of minutes. My first thought was OK - the video files are pretty large, and we’re likely loading them into memory. Then again, it should not take that long, as the video I was testing with was about 200MB in size. I tried playing around with it a bit more but could not get it to work properly. The spare time I had ran out, so I left it as is to continue with it when I next have time for it. ...

Sometimes I have to install Node on my machine for work, or a personal project. This occurs rarely, so I keep forgetting how to do it. So I did what I usually do, and Googled how to install nvm. To my surprise there’s a sponsored result, which immediately triggers a red flag: This link leads to a repo in Github. It has the following readme - I’ve omitted the domain and path: ...

I find Go’s error handling refreshingly simple & clear. Nowadays, all I do is wrap my errors using fmt.Errorf with some additional context. The pattern I tend to use most of the time just includes a function/method name, like so: func someFunction() error { err := someStruct{}.someMethod() return fmt.Errorf("someFunction: %w", err) } type someStruct struct { } func (ss someStruct) someMethod() error { return fmt.Errorf("someMethod: %w", errors.New("boom")) } If I now call someFunction, the error - when logged or printed - will look like this: someFunction: someMethod: boom. This gives just enough context to figure out what happened, and where. ...

For work, I was going to store some hashed tokens in a database. I was going to keep it simple and go with HMAC-SHA256 for it but having recently read Jean-Philippe Aumasson’s book “Serious Cryptography” I remembered that BLAKE2 should be quicker: BLAKE2 was designed with the following ideas in mind: … It should be faster than all previous hash standards Cool, I thought, let’s consider BLAKE2 then. First, let’s write a simple benchmark to see just how much faster BLAKE2 would be than HMAC-SHA256. Performance is not important for my use case as the hashing will almost certainly not be a bottleneck but I was curious. So I write a benchmark: ...

For many years now I’ve had at least one machine at home which would work as a server to host some apps. In the last couple of years I’ve been getting more into it which has led me to purchase additional hardware. I’ve decided it would be nice to document my journey so I’ll try to make a post like this - detailing the setup that I currently have with both the hardware and the software once a year. ...

My homelab consists of 4 machines currently. When choosing them I tried to be energy conscious - using hardware which would not consume too much electrical power, while still trying to maintain the up-front cost low. These are mostly older systems and I was unable to find decent power consumption numbers for them. The specs 1x MSI Cubi 3 Silent NUC CPU Dual-core i5-7200U RAM 2×16GB 2x Lenovo ThinkCentre M910 Tiny ...

I’ve been using Umami analytics on this blog for quite some time now. I self host an instance on my homelab. I spent a bit of time researching self-hosted analytics and generally they had a few issues. First, I’d like the analytics platform to be privacy focused. No cookies, GDPR compliant, no PII. Umami checks this mark. Second, many of the alternatives had quite a bit of hardware resource overhead once hosted. They would either consume a ton of memory, the cpu usage would be high or require me to host something like ClickHouse to run them. Since this blog is not the new york times I feel like hosting a specific database for it would be overkill. My homelab is rather small, so keeping things minimal is how I manage to stretch it. Umami consumes around 1% of a CPU core and ~240MiB of RAM on my homelab. ...

After reading the technicalwriting.dev post on embeddings I thought to myself - this seems like something that I can implement quite quickly and would serve as a good starting point for some hands on experience with machine learning. I want to start small, so something as simple as filling the related posts section of this blog seems like an ideal candidate to get my hands dirty. This blog is made with Hugo and uses the related content feature which provides a list of related posts based on the tags & keywords you use. While I have no quarrels with the mechanism, I thought this would be a good place to try and experiment with embeddings. ...