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lisa | xvm | |
---|---|---|
6 | 110 | |
198 | 189 | |
0.5% | 0.0% | |
9.7 | 9.8 | |
9 days ago | 1 day ago | |
Jupyter Notebook | Java | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lisa
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So, I wrote a Maybe monad in Python 3
You might be interested in that: https://github.com/ARM-software/lisa/blob/master/lisa/monad.py
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Parca Agent rewrites eBPF in-kernel C code in Rust (using Aya-rs)
This is to replace the current flow purely based on pandas dataframe and offline trace.dat parsing used in LISA: https://github.com/ARM-software/lisa (collecting a trace.dat is nice for debugging but limits to small durations, and pandas does not allow running computations in constant memory, which is an issue for very big traces)
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Languages with integrated dependency injection
The module added by this PR seems to be a pretty good fit: https://github.com/ARM-software/lisa/pull/1722
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What tools are missing in Python?
I made that thing taking some vague inspiration from SML module system: https://github.com/ARM-software/lisa/pull/1722/files
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The pipe-operator to python |>
import builtins from operator import add import functools # These functions can be found at: # https://github.com/ARM-software/lisa/blob/master/lisa/utils.py#L147 # Note: my implementation of curry() seems to be broken wrt named parameters (or for parameters with defaults, haven't looked at the details) for some reason but for this example it does not matter from lisa.utils import compose, curry def even(x): return x % 2 == 0 # The builtin functions don't have a signature, which will upset curry() so we # redefine it here def map(f, iterable): return builtins.map(f, iterable) def filter(f, iterable): return builtins.filter(f, iterable) # Swapped init and iterable to be curry-friendly def reduce(f, init, iterable): return functools.reduce(f, iterable, init) def pipeline(*items): # Add a currying layer so that we spare the user the need to do it return compose(*(curry(f)(*args) for (f, *args) in items)) # x = filter(even, list) |> map(lambda x: x+1) |> reduce(+) f = pipeline( (filter, even), (map, lambda x: x+1), (reduce, add, 0), ) l = [1,2,3,4] x = f(l) print(x)
xvm
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Implementing arrays (and hash tables and ..) in a minimal ML with a C API
Have a look at the ecstasy library for the language definitions of these types.
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Polymorphic static members
2) Funky interfaces: This is an Ecstasy interface that declares abstract static members (e.g. functions), which can then be implemented on any class and overridden on any sub-class, such that they can be invoked by type (instead of this), and virtually resolved (late bound at runtime) based on the type known at compile time. The best known example, of course, is Hashable, because it has to guarantee that a type implements both equals() and hashCode() on the same class, and the implementation is tied to the type, and not to the this. (C# added a similar feature last year in version 11.)
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How do you parse function calls?
I'm just going to warn you in advance that invocation is one of the hardest things in the compiler to make easy. In other words, the nicer your language's "developer experience" is around invocation, the more hell you're going to have to go through to get there. The AST nodes for Name( (NameExpression) and Invoke( (InvocationExpression) alone are 7kloc in the Ecstasy implementation, for example -- but the result is well worth it.
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What are some important differences between the popular versions of OOP (e.g. Java, Python) vs. the purist's versions of OOP (e.g. Smalltalk)?
Ecstasy uses message passing automatically behind the scenes for asynchronous calls, but the message passing isn't visible at the language level (i.e. there is no "message object" or something like that visible). Basically, all Ecstasy code is executing on a fiber inside a service, and services are all running concurrently, so from any service realm to any service realm, the communication is by message.
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Is your language solving a real world problem?
Regarding Ecstasy, we did not set out to build a new language; we actually set out to solve a real world problem. Specifically, we wanted to be able to dramatically improve the density of workloads in data centers, by at least two orders of magnitude in the case of lightly used applications. Our initial goal was to create a runtime design that would support 10,000 stateful application instances on a single server. Let's call it the "a10k" problem 🤣 ... a tribute to the c10k problem from 1999. We refer to our goal as "zero carbon compute", i.e. we want to push the power and hardware cost for an application to as close to zero as possible; you can't reach zero, but you can get close. If we succeed, we will help reduce the electricity used in data centers over the next few decades by a significant percentage.
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How do you tokenize multi char tokens.
Generally, left to right, one character at a time. If you’re looking for example code, here’s a simple hand-built lexer.
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Have you written your own language in itself yet?
Parts of Ecstasy are now implemented in Ecstasy. Here's the Lexer, for example.
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Top programming languages created in the 2010's on GitHub by stars
Ecstasy
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What languages have been created *specifically* for the purpose of being JIT-compiled?
Ecstasy and the xvm were designed assuming an adaptive runtime compiler (similar in concept to the Hotspot compiler for Java), but not necessarily using a JIT.
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What are you doing about async programming models? Best? Worst? Strengths? Weaknesses?
A Future reference has the various capabilities that you'd imagine, taking lambdas for thenDo(), whenComplete(), etc. The reference, in the above example, is a local variable, so you just obtain it using the C-style & operator:
What are some alternatives?
PyInstaller - Freeze (package) Python programs into stand-alone executables
seed7 - Source code of Seed7
parca-agent - eBPF based always-on profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed!
list-exp - Regular expression-like syntax for list operations [Moved to: https://github.com/phenax/elxr]
PyFunctional - Python library for creating data pipelines with chain functional programming
kuroko - Dialect of Python with explicit variable declaration and block scoping, with a lightweight and easy-to-embed bytecode compiler and interpreter.
blazon - A python library for assuring data structure and format via schemas like JSON Schema
TablaM - The practical relational programing language for data-oriented applications
datoviz - âš¡ High-performance GPU interactive scientific data visualization with Vulkan
ghc - Mirror of the Glasgow Haskell Compiler. Please submit issues and patches to GHC's Gitlab instance (https://gitlab.haskell.org/ghc/ghc). First time contributors are encouraged to get started with the newcomers info (https://gitlab.haskell.org/ghc/ghc/wikis/contributing).
awesome-functional-python - A curated list of awesome things related to functional programming in Python.
RustScript2 - RustScript is a functional scripting language with as much relation to Rust as Javascript has to Java.