cells
dafny
cells | dafny | |
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6 | 32 | |
206 | 2,763 | |
- | 4.4% | |
0.0 | 9.7 | |
about 1 year ago | 6 days ago | |
Common Lisp | C# | |
- | GNU General Public License v3.0 or later |
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cells
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Spreadsheet Lisp (v0.0.1)
Here is a project that you may be interested in: https://github.com/kennytilton/cells/wiki
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Interesting examples of visual programming?
For implementation in Common Lisp, I considered using something like Kenny Tilton's Cells.
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Six programming languages I’d like to see
The reactive programming idea reminded me of Ken Tilton and "Cells", which exploits the flexibility of CLOS (the Common Lisp Object System) to create a reactive programming language on top of Common Lisp.
https://github.com/kennytilton/cells
and he has slides from a talk
https://github.com/kennytilton/cells/blob/main/Lisp-NYC-2018...
to give context.
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Simple mechanism for CLOS slot dependencies
Nice post. Another way that uses cells.
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Ask HN: Would Spreadsheets and Lisps be much more powerful?
See Kenny Tilton's Cells project, which more or less does the inverse.
https://github.com/kennytilton/cells
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😔Non-imaginative person: “So, no choice, I have to keep my logic in my code… There is no other solution…” 😎 Smart person: “Why not create a programming language? 🚀”
I saw Concat without returning a value and got excited that they had reinvented Prolog cause they obviously were going to run Concat backwards, but no, this is bad cells for webshits or something idk
dafny
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Verified Rust for low-level systems code
For those that are interested but perhaps not aware in this similar project, Dafny is a "verification-aware programming language" that can compile to rust: https://github.com/dafny-lang/dafny
- Dafny is a verification-aware programming language
- Candy – a minimalistic functional programming language
- Dafny – a verification-aware programming language
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Lean4 helped Terence Tao discover a small bug in his recent paper
Code correctness is a lost art. I requirement to think in abstractions is what scares a lot of devs to avoid it. The higher abstraction language (formal specs) focus on a dedicated language to describe code, whereas lower abstractions (code contracts) basically replace validation logic with a better model.
C# once had Code Contracts[1]; a simple yet powerful way to make formal specifications. The contracts was checked at compile time using the Z3 SMT solver[2]. It was unfortunately deprecated after a few years[3] and once removed from the .NET Runtime it was declared dead.
The closest thing C# now have is probably Dafny[4] while the C# dev guys still try to figure out how to implement it directly in the language[5].
[1] https://www.microsoft.com/en-us/research/project/code-contra...
[2] https://github.com/Z3Prover/z3
[3] https://github.com/microsoft/CodeContracts
[4] https://github.com/dafny-lang/dafny
[5] https://github.com/dotnet/csharplang/issues/105
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The Deep Link Equating Math Proofs and Computer Programs
I don't think something that specific exists. There are a very large number of formal methods tools, each with different specialties / domains.
For verification with proof assistants, [Software Foundations](https://softwarefoundations.cis.upenn.edu/) and [Concrete Semantics](http://concrete-semantics.org/) are both solid.
For verification via model checking, you can check out [Learn TLA+](https://learntla.com/), and the more theoretical [Specifying Systems](https://lamport.azurewebsites.net/tla/book-02-08-08.pdf).
For more theory, check out [Formal Reasoning About Programs](http://adam.chlipala.net/frap/).
And for general projects look at [F*](https://www.fstar-lang.org/) and [Dafny](https://dafny.org/).
- Dafny
- The Dafny Programming and Verification Language
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In Which I Claim Rich Hickey Is Wrong
Dafny and Whiley are two examples with explicit verification support. Idris and other dependently typed languages should all be rich enough to express the required predicate but might not necessarily be able to accept a reasonable implementation as proof. Isabelle, Lean, Coq, and other theorem provers definitely can express the capability but aren't going to churn out much in the way of executable programs; they're more useful to guide an implementation in a more practical functional language but then the proof is separated from the implementation, and you could also use tools like TLA+.
https://dafny.org/
https://whiley.org/
https://www.idris-lang.org/
https://isabelle.in.tum.de/
https://leanprover.github.io/
https://coq.inria.fr/
http://lamport.azurewebsites.net/tla/tla.html
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Programming Languages Going Above and Beyond
> I think we can assume it won't be as efficient has hand written code
Actually, surprisingly, not necessarily the case!
If you'll refer to the discussion in https://github.com/dafny-lang/dafny/issues/601 and in https://github.com/dafny-lang/dafny/issues/547, Dafny can statically prove that certain compiler branches are not possible and will never be taken (such as out-of-bounds on index access, logical assumptions about whether a value is greater than or less than some other value, etc). This lets you code in the assumptions (__assume in C++ or unreachable_unchecked() under rust) that will allow the compiler to optimize the codegen using this information.
What are some alternatives?
letlang - Functional language with a powerful type system.
tlaplus - TLC is a model checker for specifications written in TLA+. The TLA+Toolbox is an IDE for TLA+.
om-sharp - OM#: Visual Programming | Computer-assisted Music Compositon
FStar - A Proof-oriented Programming Language
Lazy - Lazily evaluated (late-binding) definition for Dyalog APL
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266
DataLang - Specification and refernce implementation of DataLang
koka - Koka language compiler and interpreter
power-fx-host-samples - Samples for hosting Power Fx engine.
Rust-for-Linux - Adding support for the Rust language to the Linux kernel.
ODS_OpenExposureData - Open data standards curated by Oasis.
interactive - .NET Interactive combines the power of .NET with many other languages to create notebooks, REPLs, and embedded coding experiences. Share code, explore data, write, and learn across your apps in ways you couldn't before.