Author of nnn here. I have intentionally kept nnn feature -restricted. The idea is to have the stuff one really needs. And the main goal of nnn is seamless desktop environment integration.

Also, please note that the learning curve for the additional features is quite high. E.g. to batch rename, one would rather use Thunar's integrated simple visual batch file rename mode instead.

I believe once the number of shortcuts cross a certain limit, regular users normally abandon the utility out of their comfort zone.

> The idea is to have the stuff one really needs.

One of my main uses for a file browser is to quickly go through a list of files and delete some of them when I clean up a directory. And nnn doesn't even support deletion...

Just for listing files "ls" is still usually the fastest way. Operations on the files is what's cumbersome, e.g. when I try to hit the right files for "rm" with tab completion. When nnn can't help with that its use case is very limited to me.

Understood. I do agree a single software can't fit all use cases. nnn avoids destructive operations like delete. However, one can always spawn a terminal in the current directory using `!` and delete the files from the shell.

> Just for listing files "ls" is still usually the fastest way

Not really. `ls` a dir with 20K files and see the difference with nnn. ;) And then, nnn is more about navigation. Try shortcuts like `-`, `&`, `~` (tilde), `....` and of course the `navigate-as-you-type` mode. Looking for a specific file? Try filter mode with search-as-you-type.

Indeed. When i reach for MC or the like, it is more often than not to deal with gnarly file deletions or transfers.

This because they allow me to pick the individual files i want to deal with without potentially screwing up wildcards or such.

In continuation of my earlier comment, nnn has a copy file path to clipboard action (https://github.com/jarun/nnn#file-copy-move-delete). I believe we can extend it to append multiple filenames which can then be deleted from the spawned shell.

Ideas are welcome! nnn is under active development. ;)

They claim higher performance for nnn than mc. Not sure it’s really needed in most cases though... but nnn could come in handy in edge cases of really large directories, say. BTW their docs say that they avoid div instructions in favour of floating point multiply. Is this still faster on recent CPUs?

I can't be very specific without citing examples. The following APIs may explain what I mean:

https://github.com/jarun/nnn/blob/master/nnn.c#L358

https://github.com/jarun/nnn/blob/master/nnn.c#L1267

https://github.com/jarun/nnn/blob/master/nnn.c#L1617

Most of the functions in nnn are targeted to be high-performance like these.

Hmm. I'm not convinced any of your routines are more "high-performance" than the standard-library, have you benchmarked them?

For example, I don't think your getorder() could possibly be faster than __builtin_ctz(), or even a wrapper around ffs(). You also hardcoded the size of size_t to 32 bits, so it's not more portable than those options.

Edit: I checked, ffs is a tiny branch-free routine in glibc:

  (gdb) disassemble ffs
   Dump of assembler code for function ffsl:
      0x0007e9d0 <+0>:	mov    $0xffffffff,%edx
      0x0007e9d5 <+5>:	bsf    0x4(%esp),%eax
      0x0007e9da <+10>:	cmove  %edx,%eax
      0x0007e9dd <+13>:	add    $0x1,%eax
      0x0007e9e0 <+16>:	ret    
   End of assembler dump.

> __builtin_ctz

avoided it to stay out of compiler-specific stuff. we do have plans to replace getorder() with ffsl().

Perhaps I would be more accurate if I say nnn is faster by design. Movement of data around memory is minimal. No redundant bytes are allocated. We use quicksort and optimize further by pushing non-matches down right away so they never appear in a filter comparison again. And of course, using non-lib custom functions enable using static linkage, having a controlled binary size and removing redundant checks/processing because the limits and borderline cases are known.

  > avoid div instructions in favour of floating point multiply.
  > Is this still faster on recent CPUs?

NB - >>[Taking this question out of context]<< - looks like the answer is "no". From a quick test:

- intel Broadwell: 64-bit integer div is fastest, float div is 69% slower, float mul is 50% slower;

- AMD Ryzen: 64-bit integer div is fastest, float div is 47% slower, float mul is 60% slower;

- ARM11 (Raspberry Zero): 64-bit integer div is fastest, float div is 138% slower, float mul is 159% slower.

quick-and-dirty test used: https://pastebin.com/raw/4XqnuXL5

We don't have the case of integer div. And if you take a look into the coolsize() func we don't re-compute the factor. So it's not very significant.

To get the size of a file we multiply only once with this factor.

What is significant is the while() loop where the iterations increase with the file size. There's no mul or div in it giving the API a reasonable performance benefit.