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Showing 9 open source projects for "good"
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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All-in-one security tool helps you prevent ransomware and breaches.Blumira’s detection and response platform enables faster resolution of threats to help you stop ransomware attacks and prevent data breaches. We surface real threats, providing meaningful findings so you know what to prioritize. With our 3-step rapid response, you can automatically block known threats, use our playbooks for easy remediation, or contact our security team for additional guidance. Our responsive security team helps with onboarding, triage and ongoing consultations to continuously help your organization improve your security coverage.
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Julia VS Code
Julia extension for Visual Studio Code
...A hybrid “canvas programming” style combines the exploratory power of a notebook with the productivity and static analysis features of an IDE. VS Code is a powerful editor and customizable to your heart’s content (though the defaults are pretty good too). It has power features like multiple cursors, fuzzy file finding and Vim keybindings. -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
...While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. The best case for forward-mode differentiation is a function that maps a scalar to a vector. -
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DoubleFloats.jl
Math with more good bits
Math with 85+ accurate bits. Extended precision float and complex types. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose... -
Financial reporting cloud-based software.The software is cloud based and automates complexities around consolidating and reporting for groups with multiple year ends, currencies and ERP systems with a slice and dice approach to reporting. While retaining the structure, control and validation needed in a financial reporting tool, we’ve managed to keep things flexible.
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MIRT.jl
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
...It is very much still under construction, although there are already enough tools to solve useful problems like compressed sensing MRI reconstruction. Trying the demos is a good way to get started. The documentation is even more still under construction. -
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InteractiveCodeSearch.jl
Interactively search Julia code from terminal
Julia has @edit, @less, etc. which are very handy for reading the implementation of functions. However, you need to specify a "good enough" set of (type) parameters for them to find the location of the code. -
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CUDAnative.jl
Julia support for native CUDA programming
The programming support for NVIDIA GPUs in Julia is provided by the CUDA.jl package. It is built on the CUDA toolkit and aims to be as full-featured and offer the same performance as CUDA C. The toolchain is mature, has been under development since 2014, and can easily be installed on any current version of Julia using the integrated package manager. -
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Mocha.jl
Deep Learning framework for Julia
...Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. Reworking Mocha with new technologies requires some non-trivial efforts, and new exciting solutions already exist nowadays, it is a good time for the retirement of Mocha.jl. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. -
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ParallelAccelerator.jl
ParallelAccelerator package, part of the High Performance Scripting
ParallelAccelerator is a Julia package for speeding up compute-intensive Julia programs. In particular, Julia code that makes heavy use of high-level array operations is a good candidate for speeding up with ParallelAccelerator. With the @acc macro that ParallelAccelerator provides, users may specify parts of a program to accelerate. ParallelAccelerator compiles these parts of the program to fast native code. It automatically eliminates overheads such as array bounds checking when it is safe to do so. It also parallelizes and vectorizes many data-parallel operations. ... -
Rezku Point of SaleRezku is an all-inclusive ordering platform and management solution for all types of restaurant and bar concepts. You can now get a fully custom branded downloadable smartphone ordering app for your restaurant exclusively from Rezku.
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