mech VS cheatsheets

I’m working on a programming language for robots called Mech!

https://github.com/mech-lang/mech

Mostly a research project until I find some more people who might be interest in pushing it more.

A recent blog for anyone who wants to check it out: https://mech-lang.org/post/2025-01-09-programming-chatgpt/

And a 10 minute video: https://www.hytradboi.com/2022/i-tried-rubbing-a-database-on...

There's also https://github.com/mech-lang/mech which is a sort of descendant of Eve https://witheve.com/ . That too seems to be getting close to hiatus. It's a bit of a shame since it seems like quite a nice paradigm for some stuff like GUIs, interactive stuff, and discrete event simulation, but I suppose the paradigm is both a bit obscure and different enough from everything else that it becomes a "boil the ocean" situation where one or a few people try and hack away but aren't really able to get much traction and eventually tired themselves out.

You should take a look at the language I’m developing, Mech: https://github.com/mech-lang/mech

I'm working on a language called Mech (github.com/mech-lang/mech) that is semantically parallel and asynchronous first. You can write something like this:

Hi everyone. I've posted here a couple times about my language Mech, which you can find here. I've just put together an update which I hope this community will find interesting!

> HN readers - do you have an "up and coming" language that you think has better structured the fundamentals from R, that you hope will someday have enough capabilities you can use it instead of R?

Hope is the operative word here!

I'm writing a language to compete in this area. It's called Mech and I'll be releasing the first beta in October. You can think of it like Matlab + Excel. It's very fast, has default-parallel semantics for operators and functions, and supports full interactive coding with no startup/compilation latency issues. It's meant for robots, but I've also designed it to be a better Matlab, and I think it should take on R handily. Fair warning, it's public alpha now so error messages are sparse and the happy path is narrow.

https://github.com/mech-lang/mech

I 100% agree, I think the functional features that have been added across all the popular languages (map, reduce, fold etc.) has been a positive. Nothing demonstrates it better (imo) than purrr in R: https://github.com/rstudio/cheatsheets/blob/main/purrr.pdf

I also think there is some merit to “high syntactical density” clearly if you can see the entire code in one place instead of having to navigate through many files or sections that’s beneficial. (Heavily discussed in the last big HN thread: https://news.ycombinator.com/item?id=38981639)

I also think JQ has proven the merit of tacit functional languages in that you can concisely write arbitrary transforms/queries on json that can be more expressive than SQL (many SQL engines have added JSONPath anyway). And I also think postfix is great for processing pipelines.

But I am not totally convinced in the approach of APL/J/Q/KDB for the combination of terse style + prefix + tacit because it makes the code so difficult to read. I think if you took an approach similar to JQ where instead of relying on symbols operators were just human readable words it would be easier to get us half way there to trying out the verb, adverbs etc. approach of the APL family. The problem with making it human readable text is that you lose the conciseness which is part of the draw of the APL family as they want to have a high syntax density and analogous code to mathematical expressions.

If you're an R user, stringr + its cheatsheet gets you very close to remembering what to do without needing to look further!

For more information on how to use ggplot2 and create charts consult the ggplot2 official page or the ggplot2 cheat graphic.

I'd suggest you have a look at cheatsheets (or download them from GitHub) if you want to get to know your way around a package or set if functions, it saves you a lot of time.

You could use geom_hline and geom_vline, geom_abline, or geom_segment for this. (The ggplot cheat sheet is very useful for answering these kinds of questions, BTW.)

I can't say for sure because I don't know what your ultimate aim is for your visualization. Check out the cheat sheet for ggplot2 here.

Finally just do your analysis. You should also should give a try and see the cheat sheet for data importing on the tidyverse package.

That said, when I’m trying to come up with an interesting way to visualize data, I find the ggplot cheat sheet very helpful: https://github.com/rstudio/cheatsheets/raw/main/data-visualization-2.1.pdf

Here's a cheat sheet: https://github.com/rstudio/cheatsheets/blob/main/strings.pdf

The cheat sheet of the stringr package should give you good overview of string manipulation/ regex in R.