Does anyone here have experience with Ruby programming? More specifically mRuby or DragonRuby for game programming?

I came across DragonRuby as free for its 7th anniversary and I’m considering checking it out, but I have no experience with the Ruby programming language itself.

Most of my programming experience recently is in C# and Python, but I have some experience with JavaScript/TypeScript.

Does anyone know of good resources for learning Ruby coming from that background, or thoughts on if it is worth it to try to pick up?

I’m not really interested in LLM-based Ruby workflows, at least until I have a decent grasp of the language and syntax myself.

Ive been playing around with learning a lisp language for a while, and I recently decided to go for it and learn fennel (as I already am comfortable with lua) just to see if I like lisps as a class. Normally, I try to do the first 5-10 days of a previous advent of code to pick up a lang.

Setting up my advent of code environment got me wondering: what projects do other people use to learn new languages/tooling? Id love to hear how other people approach learning a new skill.

I probably triggered some PTSD right there.

Was just in a meeting at work, where we listed off everything that makes software development hard and slow. An excersize for the thread would be to replicate that list. It turned out that Claude helps with like 1/5th or less of it....especially in a collaborative environment.

So, the situation we're now encountering is that random business areas can vibe code out something, tell nobody, throw it in AWS, have it become a critical part of a business process that fails when they quit, and nobody even has access to look at what was made.

It gives me comfort that in about 5 years there will be a new surge in demand for programmers to reign in all the rogue applications that need shutdown because of the immense risk to continual operation of a company, from data leaks to broken payroll.

It'll be Y2K all over again.

For a long time programmers have had two types of program verification tools, static analysis (like a compiler's checks) and dynamic analysis (running a test suite). I find myself using LLMs to analyze newly written code more and more. Even when they spit out a lot of false positives, I still find them to be a massive help. My workflow is something like this:

1. Commit my changes
2. Ask Claude Opus "Find problems with my latest commit"
3. Look though its list and skip over false positives.
4. Fix the true positives.
5. git add -A && git commit --amend --no-edit
6. Clear Claude's context
7. Back to step 2.

I repeat this loop until all of the issues Claude raises are dismissable. I know there are a lot of startups building a SaaS for things like this (CodeRabbit is one I've seen before, I didn't like it too much) but I feel just doing the above procedure is plenty good enough and catches a lot of issues that could take more time to uncover if raised by manual testing.

It's also been productive to ask for any problems in an entire repo. It will of course never be able to perform a completely thorough review of even a modestly sized application, but highlighting any problem at all is still useful.

Someone recently mentioned to me that they use vision-capable LLMs to perform "aesthetic tests" in their CI. The model takes screenshots of each page before and after a code change and throws an error if it thinks something is wrong.