config VS evcxr

I actually quite like the config, seems intuitive! Though, I am comparing it to this abomination, so I might have low expectations here :-)

4) For scripts you put for everyday use in ~/bin, I've found busybox style multipurpose binary invaluable. You put all your scripts into a single Rust binary, and than hard-link this binary under different names in PATH, so that the first argv is actually the name of subcommand. Here's how the infra works for my scripts, and here's an example script.

1) Invest into application-level profiling, using tracing::span! for collection and something like tracing_tracy or tracing_span_tree for visualization. This should give you high-level logical profile. 2) If on Linux, learn a bare minimum of perf for collecting data (the extent of my knowledge about perf is basically this script https://github.com/matklad/config/blob/master/tool/src/prf.rs). Use neither flamegraph.pl nor cargo flamegraph for visualization, use https://profiler.firefox.com/ instead.

And, while a spam links, here’s a link with more links: https://github.com/matklad/config/blob/master/links.adoc

Emacs didn't invent REPL, and it's common everywhere. For Rust: https://github.com/evcxr/evcxr/blob/main/evcxr_repl/README.m.... But heck, the compiler is reasonably fast enough that any IDE can REPL by compiling the code.

The value here is more in being able to read a script before you run it, then have it run fast, maybe tweaking something here and there. And a compiled script will run 10,000 times faster than LISP, which can be important.

https://github.com/evcxr/evcxr can run Rust in a Jupyter notebook. It's not Golang but close enough.

The engine uses rust_decimal::Decimal to represent high precision decimal numbers, like the weight property. Serialization of RocksDB keys is done by the storekey crate. To know how Yumi's machine stores diffs, we can now ask- How does storekey serialize rust_decimal? Well, using evcxr to run Rust in Jupyter, the answer is as a null-terminated string:

In theory you should be able to create Rust notebooks (Jupyter notebook) using evcxr so maybe some AI, data analysis, prototyping make sense if you aim for good performance in final application (protype in evcxr and use notebook as reference to implement final application in Rust for speed and safety).

You should check out evcxr

There is also implementations of rust REPLs, like the beautifully named evcxr.