femtolisp
cling
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femtolisp | cling | |
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10 | 19 | |
1,550 | 3,323 | |
- | 1.5% | |
0.0 | 8.6 | |
about 4 years ago | 24 days ago | |
Scheme | C++ | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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femtolisp
- Petalisp: Elegant High Performance Computing
- fe: A tiny, embeddable language implemented in ANSI C
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From Common Lisp to Julia
> In short, Julia is very similar to Common Lisp, but brings a lot of extra niceties to the table
This probably because Jeff Bezanson, the creator of Julia, created a Lisp prior to Julia, which I think still exists inside Julia in some fashion
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Modern Python Performance Considerations
Well let's flip this around: do you think you could write a performant minimal Python in a weekend? Scheme is a very simple and elegant idea. Its power derives from the fact that smart people went to considerable pains to distill computation to limited set of things. "Complete" (i.e. rXrs) schemes build quite a lot of themselves... in scheme, from a pretty tiny core. I suspect Jeff Bezanson spent more than a weekend writing femtolisp, but that isn't really important. He's one guy who wrote a pretty darned performant lisp that does useful computation as a passion project. Check out his readme; it's fascinating: https://github.com/JeffBezanson/femtolisp
You simply can't say these things about Python (and I generally like Python!). It's truer for PyPy, but PyPy is pretty big and complex itself. Take a look at the source for the scheme or scheme-derived language of your choice sometime. I can't claim to be an expert in any of what's going on in there, but I think you'll be surprised how far down those parens go.
The claim I was responding to asserted that lisps and smalltalks can only be fast because of complex JIT compiling. That is trueish in practice for Smalltalk and certainly modern Javascript... but it simply isn't true for every lisp. Certainly JIT-ed lisps can be extremely fast, but it's not the only path to a performant lisp. In these benchmarks you'll see a diversity of approaches even among the top performers: https://ecraven.github.io/r7rs-benchmarks/
Given how many performant implementations of Scheme there are, I just don't think you can claim it's because of complex implementations by well-resourced groups. To me, I think the logical conclusion is that Scheme (and other lisps for the most part) are intrinsically pretty optimizable compared to Python. If we look at Common Lisp, there are also multiple performant implementations, some approximately competitive with Java which has had enormous resources poured into making it performant.
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CppCast: Julia
While it uses an Algol inspired syntax, it has the same approach to OOP programing as CLOS(Common Lisp Object System), with multi-methods and protocols, it has a quite powerfull macro system like Lisp, similar REPL experience, and underneath it is powerered by femtolisp.
- Julia and the Incarceration of Lisp
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What is the smallest x86 lisp?
For a real answer, other replies have already mentioned KiloLisp, but there's also femtolisp. Also, not exactly what you're asking for, but Maru is a very compact and elegant self-hosting lisp (compiles to x86).
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lisp but small and low level?Does it make sense?
Take a look at femtolisp It has some low level features and is quite small. There is also a maintenance fork at lambdaconservatory
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Lispsyntax.jl: A Clojure-like Lisp syntax for julia
A fun Julia easter egg I recently discovered.
Running 'julia --lisp' launches a femtolisp (https://github.com/JeffBezanson/femtolisp) interpreter.
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Wisp: A light Lisp written in C++
Reminds me of the femtolisp README :)
Almost everybody has their own lisp implementation. Some programmers' dogs and cats probably have their own lisp implementations as well. This is great, but too often I see people omit some of the obscure but critical features that make lisp uniquely wonderful. These include read macros like #. and backreferences, gensyms, and properly escaped symbol names. If you're going to waste everybody's time with yet another lisp, at least do it right damnit.
cling
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Interactive GCC (igcc) is a read-eval-print loop (REPL) for C/C++
More recent activity, but based on clang: https://github.com/jupyter-xeus/xeus-cling https://github.com/root-project/cling
Similar to Cling[1] from ROOT.
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It's 2023, so of course I'm learning Common Lisp
> The repl driven workflow is amazing and the lisp images are rock solid and highly performant.
do people not realize that basically everything vm/interpreted language has a repl these days?
https://www.digitalocean.com/community/tutorials/java-repl-j...
https://github.com/waf/CSharpRepl
https://pub.dev/packages/interactive
not to mention ruby, python, php, lua
hell even c++ has a janky repl https://github.com/root-project/cling
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dont want online ones
Want to see your mind blown? Check out cling, a (sort of) C and C++ interpreter (it's a REPL). Or the work in progress, live-developed clauf, a real C interpreter.
- Fête à bord d’un avion de Sunwing | L’organisateur s’explique sur l’origine de sa fortune
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Interpreter vs Compiler
"Exclusively" may be a tough claim. C++ has the Cling interpreter, for example. You could say that "most C++ implementations are compilers". My understanding with Python is that it is challenging to write a compiler for because it's a "dynamic" language. For example, it's possible to create a new datatype at runtime, or even to build strings and tell the interpreter to execute them as source code.
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Python switch statement ftw (finally)
https://root.cern/cling/ https://github.com/root-project/cling
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Getting information about classes, methods and variables in C++?
cling(https://github.com/root-project/cling) a c++ interpreter may help, or you can use an IDE or https://en.cppreference.com/ (on duckduckgo you can search directly on it with the !cpp bang, or use firefox 'add a keyword for this search' feature which is really great)
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Wisp: A light Lisp written in C++
It has been done several times, at least.
http://www.hanno.jp/gotom/Cint.html
https://github.com/root-project/cling
https://www.softintegration.com
You can argue whether some of those are strictly interpreters, versus just a REPL hooked up to a compiler (as in the case of Cling). But they do exist.
What are some alternatives?
termux-ndk - android-ndk for termux
small-lisp - A very small lisp interpreter, that I may one day get working on my 8-bit AVR microcontroller.
julia - The Julia Programming Language
Carp - A statically typed lisp, without a GC, for real-time applications.
Fennel - Lua Lisp Language
sectorlisp - Bootstrapping LISP in a Boot Sector
xeus-cling - Jupyter kernel for the C++ programming language
awesome-lisp-companies - Awesome Lisp Companies
hissp - It's Python with a Lissp.
cppreference-doc - C++ standard library reference
LispSyntax.jl - lisp-like syntax in julia