cantagi

They have been removing features from the open source version for a while.

The closest alternative seems to be RustFS. Has anyone tried it? I was waiting until they support site replication before switching.

act is brilliant - it really helps iterate on github or gitea actions locally.

I have a problem with the Github Actions documentation. There is a lot of it, but it feels as though it was written from a "product" perspective, to explain how to use the product.

None of it usefully explains how GHA works from the ground up, in a way that would help me solve problems I encounter.

act is great. I use it to iterate on actions locally (I self-host gitea actions, which uses act, so it's identical to github actions).

schmoyoho are incredible at making these songs, and so quickly after each event. I'm amazed they usually only get 10s of thousands of views, with the odd exception over 1m.

UK resident here. The original version gave me the push I needed to get a rPi 2B+, subscribe to a VPN, and use it as a wifi AP that routes all traffic from my house through it.

Can you trust a VPN who say they don't log? No, but more so than an ISP who might be legally required to at any moment without you ever finding out.

Also, I will now never start a tech company in the UK, and this is because I will never put myself into a position where I am forced to add backdoors to a product.

TBH I haven't ever had a problem with SD card corruption so far. If I did, it wouldn't really matter, since all the important data is on the RAID array, and the OS can be reprovisioned if needed.

Performance proved to be an issue for SD cards though, when attempting to host nextcloud and postgres. I do what teh_klev is talking about and selected the fastest USB stick I could find, which was a Samsung FIT Plus 128 GB Type-A 300 MB/s USB 3.1 Flash Drive (MUF-128AB), and this gave me a huge speedup.

Unfortunately Jellyfin is not really fast enough on an rpi and I have no solution.

The range is indeed poor, and it depends a lot on your house/flat. Would definitely recommend using another machine with something like this.

In terms of throughput, right next to an AP, I just got 65Mb/s.

I have 3 rpi4s running my entire home network.

One is a vpn router and a wifi AP, it also has Uptime Kuma. I need this to be reliable and rarely touch it except to improve its reliability. - Openvpn - HostAPD - Uptime Kuma (in docker) - A microservice invoked from Uptime Kuma that monitors connectivity to my ISPs router (in docker) - nginx, not in docker, reverse proxies to Uptime Kuma

The second acts as a NAS and has a RAID array, consisting of disks plugged into a powered USB hub. It runs OpenMediaVault and as many network sharing services as I can set up. I also want maximum reliability/availability from this pi, so rarely touch it. All the storage for all my services is hosted here, or backed up to here in the case of databases that need to be faster.

The third rpi runs all the rest of my services. All the web apps are dockerized. Those that need a DB also have their DB hosted. Those that need file storage are using kerberized NFS from my NAS. This rpi is also another wifi AP. This rpi keeps running out of RAM and crashing and I plan to scale it when rpis become cheaper or I can repair some old laptops.: - Postgres - HostAPD - nginx - Nextcloud - Keycloak - Heimdall - Jellyfin - N8N - Firefly-iii - Grist - A persistent reverse SSH tunnel to a small VM in the cloud to make some services public - A microservice needed for one of my hobbies - A monitoring service for my backups

All of these pis are provisioned via Ansible.

To me it's been obvious ever since the term SaaS was coined, that it would be worse for users. Not only is it more expensive, you don't get control over your data or how you use the product. The idea of cloud computing is similar - you have to pay more for someone else's computer. Granted, SaaS and cloud computing make sense if you're an organization, and can have advantages in terms of scale, reliability, etc.

But also, when business interests get involved in producing software in general, it often causes problems, i.e. ads, worse interop, performance considered unimportant, marketing emails, DRM, the software not working after the company is acquihired or fails. However, producing software takes time which costs money. So, commercially produced software can only exist at this intersection between there being a business model, and the software being useful. The condition is, the usefulness must be enough to be worth paying for, and the result is what we have now.

Imagine rewinding to 1990 with unlimited borrowed VC funds, hiring every person employed in tech full time until 2023, and building a massive suite of useful software for individuals, companies, govt, with a few different alternatives for each use case (like we have now), except they communicate via a series of well defined and public APIs. The entire software stack would be developed in this way, for maximum usability, performance, interoperability, features, etc. . After getting to the set of features we now have in late 2022, we pause the thought experiment, note the date, and split the cost between the users. Ignoring the various practical issues with this experiment, I bet it would be possible to get to where we are sooner and far cheaper per user.

Long story short, I don't think the goal of making money as a business is very well aligned with the goal of producing really good quality and long lasting software, even if the users are willing to pay, and this is a real problem. For personal use, I won't tolerate ads, DRM, etc., so I now self host.

Yes, people writing unmaintainable code in Jupyter notebooks is a problem.

Personally, I start every notebook with

    %load_ext autoreload
    %autoreload 2

then develop production quality code in .py files.

> Why bother with docker for a home server other than for the fun of it?

I do this. Over time you forget how each service was configured, or simply don't care. Adding more and more stuff to a home server increases the complexity and the attack surface more than linearly in the number of services.

I run nearly all my home services in docker, and I have a cookie-cutter approach for generating SSL certs and nginx config for SSL termination (not dockerized). Provisioning is automated through ansible, so my machines can be cattle not pets, as far as is possible on 3 raspberry pis.

I also found it very weird, but here's my intuition.

There are 2^k > 512 spheres stuck to eachother across k-d (pretend k=9). The line from the center to the point where the inner sphere touches one of outer spheres has to shortcut through all k dimensions to get from the center to the sphere.

This distance has been massively inflated due to the number of dimensions. But the distance to the edge of the box hasn't been inflated - it's just constant, so the inner sphere breaks out.

The fourier transform is the same for all time, and the time-series contains all frequencies, so it makes sense to plot them on orthogonal axes. However it makes sense for them to share the 3rd axis for magnitude.

However, the Im parts are just bolted on orthogonally to the Re parts and orthogonally to the magnitude. The reason the Im part of the time series shares an axis with frequency is just because the author ran out of dimensions.