FStar
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FStar | onnx | |
---|---|---|
42 | 38 | |
2,552 | 16,641 | |
1.2% | 2.1% | |
9.9 | 9.5 | |
8 days ago | 5 days ago | |
F* | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
FStar
- Lean4 helped Terence Tao discover a small bug in his recent paper
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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/).
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If You've Got Enough Money, It's All 'Lawful'
Don't get me wrong, there are times when Microsoft got it right the first time that was technically far superior to their competitors. Windows IOCP was theoretically capable of doing C10K as far back in 1994-95 when there wasn't any hardware support yet and UNIX world was bickering over how to do asynchronous I/O. Years later POSIX came up with select which was a shoddy little shit in comparison. Linux caved in finally only as recently as 2019 and implemented io_uring. Microsoft research has contributed some very interesting things to computer science like Z3 SAT solver and in collaboration with INRIA made languages like F* and Low* for formal specification and verification. But all this dwarfs in comparison to all the harm they did.
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What are the current hot topics in type theory and static analysis?
Most of the proof assistants out there: Lean, Coq, Dafny, Isabelle, F*, Idris 2, and Agda. And the main concepts are dependent types, Homotopy Type Theory AKA HoTT, and Category Theory. Warning: HoTT and Category Theory are really dense, you're going to really need to research them.
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Why is there no simple C-like functional programming language?
F* is a dependently typed language that can be transpiled to idiomatic C via the KReMLin compiler. It’s very ML-ish to write and you can leave out some proofs. It also has the benefit of being used to write a formally verified TLS implementation that’s in wide use throughout industry.
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[Media] Genetic algorithm simulation - Smart rockets (code link in comments)
As I said, dependent types attempt to solve this problem. F* is a language where you can express complex logic as a type. The catch is, these types are checked by an SMT solver. If the solver can satisfy the type checking, then great, and you move on. If it can’t, you have no idea why, and either have to guess or manually write the proof anyway. Contrast this with Standard ML which has a proof of the soundness of its type system.
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Prop v0.42 released! Don't panic! The answer is... support for dependent types :)
So kind of like F*? https://www.fstar-lang.org/
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old languages compilers
F*
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Pegasus spyware was used to hack reporters’ phones. I’m suing its creators; When you’re infected by Pegasus, spies effectively hold a clone of your phone – we’re fighting back.
Nevermind that academia has come up with far safer ways to do a few things but social norms & inertia prevent their wider adoption (well okay, it also has a barrier to entry in the education required to use it but I don't think someone with the knowledge to meaningfully contribute to an OS kernel can be considered uneducated nor unable to learn).
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[Hobby] Amateur Generalist Programmer Seeking to Put Bugfixing Skills to Good Use
Maybe that's a little off topic here, but if you like fixing bugs, i suspect you might also enjoy showing that there are no bugs at all. Check out languages like F* https://www.fstar-lang.org/ It's a proof-oriented programming language. You can use it to write code that has no bugs at all. And you once you're done, can convert F* to C or other languages.
onnx
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.
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Object detection with ONNX, Pipeless and a YOLO model
ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
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Operationalize TensorFlow Models With ML.NET
ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Async behaviour in python web frameworks
This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.
- Pickle safety in Python
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Unpaint: a compact, fully C++ implementation of Stable Diffusion with no dependency on python
Sounds interesting, ONNX runtime - what I use - can also be run with WebAssembly and on CPU, on all major GPUs, and it supports many programming languages, though C++ is its direct form.
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AMD Accelerates AI Adoption on Windows 11 With New Developer Tools for Ryzen AI
No, it's that AMD doesn't fucking need to. If anything, it the owners of ONNX that needs to convince developers that their technology is worth learning and using it to implement solutions, whether it's on laptops or phones or Raspberry Fucking Pi.
What are some alternatives?
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
coq - Coq is a formal proof management system. It provides a formal language to write mathematical definitions, executable algorithms and theorems together with an environment for semi-interactive development of machine-checked proofs.
lean - Lean Theorem Prover
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
tensorflow-directml - Fork of TensorFlow accelerated by DirectML
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
dafny - Dafny is a verification-aware programming language