Gitbucket VS lila

Gitbucket (https://gitbucket.github.io/)

GitBucket

Link: https://github.com/gitbucket/gitbucket

Another self-hosted project in the space that i've seen was GitBucket, although it runs on the JVM (not necessarily a bad thing, just different from Go): https://gitbucket.github.io/

Version control Systems eg. Github, Bitbucket, Gitbucket help in version control of your code and generally storage of your code. It can also serve as a visual reminder of the progress you make eg. on Github there is a monitoring system that shows how many days you are active on the platform.

> Git itself decentralizes source control, and yet we all want to use single-point-of-failure Github.

This is pretty much why both the organization that i work for, as well as i personally for my homelab use self-hosted GitLab instances: https://about.gitlab.com/

Though in practice there are a lot of other options out there, like Gitea (https://gitea.com/) and GitBucket (https://gitbucket.github.io/), though maybe less so for alternative source control systems (e.g. SVN has been all forgotten, however that's a personal pet peeve).

Not only that, but i also utilize my own Sonatype Nexus (https://www.sonatype.com/products/repository-oss?topnav=true) instances to great success: for doing everything from mirroring container images that i need from DockerHub (e.g. due to their proposed removal policies for old images and already adopted rate limits), to mirroring Maven/npm/NuGet/pip/Ruby and other dependencies, so i don't have to connect to things on the Internet whenever i want to do a new build.

That not only improves resiliency against things on the Internet going down (apart from situations where i need something new and it's not yet cached), but also improves performance a lot in practice, when only the company servers need to be hit, or my own personal servers in the data center for my cloud hosted stuff, or my own personal servers in my homelab for my own stuff.

Admittedly, all of that takes a bit of setup, especially if you happen to expose anything to the web in a zero trust fashion (permissible for my own stuff, as long as i'm okay with manually managing CVEs just to probably get hacked in the end anyways, but definitely not that any corporation with an internal network would want to do), but in my eyes that's still worth the effort, if you value being in control of your own software stack and the ecosystem around it.

It's probably much less worth it, if you don't see that as a benefit and don't want to be the one responsible for whatever project you're working on getting hacked, e.g. if you'd fail to patch out the recent GitLab CVE where exiftools could execute arbitrary code, which is probably the case if you don't have the resources to constantly throw at maintenance, in comparison to companies with 100x - 1000x more resources than you have for that sort of stuff.

> Rails doesn't scale? Github's the largest code repository site in the world.

You know, i think i understand both of the viewpoints here. Personally, i'd say that Rails doesn't scale as well as i'd expect it to. You can definitely build scalable systems in it, though you'll end up throwing a whole bunch of hardware resources, when compared to certain other languages and technology stacks, to serve similar load.

For example, right now i self-host a GitLab (https://about.gitlab.com/) instance for managing my code repositories, CI builds and so on. Even with just me using it (alongside some automated processes), it routinely eats up close to 4 GB of RAM, which in my case is an entire VPSes worth and costs me about 60 Euros a year with Time4VPS (affiliate link, if you'd like to check it out: https://www.time4vps.com/?affid=5294) but would cost me way more in AWS, GCP etc. One could argue that that's not too expensive, but not everyone earns a lot of money and running 10-20 VPSes does eventually build up, since i can't afford colocation and my residential homelab setup with a WireGuard tunnel to bypass ISP NAT with a proxy VPS is pretty slow, even if i can afford more storage, RAM and CPU power that way.

Compare that situation to projects like Gogs (https://gogs.io/), Gitea (https://gitea.com/), GitBucket (https://gitbucket.github.io/) and sourcehut (https://sourcehut.org/) - i'd argue that all of them on average use less CPU resources and memory for accomplishing similar tasks. For example, have a look here: https://forgeperf.org/

However, we cannot ignore the fact that using Ruby might have been exactly what allowed for quickly creating the functionality of GitLab and many other platforms and tools out there, GitHub included, so the choice between usable software and innovation in the near future and performant software possibly years from now is a tricky one.

There are probably good arguments for both, but noone can declare either to be better. Personally, i don't mind using Ruby, Python or even PHP when it makes sense and i don't need to worry about scalability from day 0.

I saw this on HN and have been using it for the past two weeks for some small hobby projects. The docs are so-so but I got it set up in Docker without much hassle. I've since migrated completely from gitbucket. Great software - I encourage everyone to try it out.

A Git platform (like github or gitlab) written in Scala. Definitely not a pet project so might be fun to read the code. https://github.com/gitbucket/gitbucket

Lichess - Online Chess Server. Scala, TypeScript

> the player who committed more blunders lost 86% of the time

In some sense this is almost tautological. While finding an exact definition for a chess blunder isn't straightforward, here is one example from the Lichess UI:

https://github.com/lichess-org/lila/blob/b527746b179cdde6438...

Basically, if you make a move which decreases your winning probability more than 14% over the best move, that's a blunder. But winning probability is a nonlinear function of stockfish centipawns. A drop in 100 centipawns when you're up 15 points isn't a blunder. When the game was equal, it is.

Point is, by the time you know it's a blunder you already know something about the outcome of that move, that it swung the winning probability by more than 14%. So the analysis is kind of just measuring some function of winning probability and saying that it is highly correlated with winning probability.

If you want to improve make it your goal to play the best chess you can, not increase an arbitrary number. Watch YouTube series like John Bartholomew's "Climb the Rating Ladder" for some general insight into what you might be doing wrong. Read Irving Chernev's "Logical Chess: Move By Move" to see the thinking process of high level players. Do lots of puzzles (I like lichess.org for puzzles). And always analyze your games. When you analyze make it your goal to find at least two things you could have improved.

The easiest way to play against Stockfish is perhaps on https://lichess.org/, but it's not the only chess engine that evaluates positions with a neural network.

Lichess.com

if you actually like chess, try lichess.org, the free and open-source, no ads ever, premium alternative

The Chessnut Evo works almost flawlessly (I did not experience this issue but people have reported having ChessnutVision stop working on occasion which requires turning on/off to fix) with popular chess sites (officially supported are chess.com, lichess.org, Chess Kid and Chessable). I experienced no major lag when playing games on Lichess through the board There is the unavoidable delay of physically moving pieces, so it may not be ideal for blitz But for rapid or longer time controls. the ability to have your OTB games instantly logged and the ability to effortlessly analyze games after is game-changing for me. The one occasional hiccup I encountered was when quickly sliding pieces, it would register an incorrect move. But that’s an easy fix of adjusting the Limbo move delay (I don't like this option as it makes the board feel less responsive I prefer to just be aware and lift pieces instead of sliding).