figmagic - Generate design tokens, export graphics, and extract design token-driven React components from your Figma documents

•    TypeScript

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Figmagic promotes a structured way of assembling design systems. Following the primary principle of atomic design, Figmagic wants you to build from the bottom up, beginning with decomposing the tokens. Tokens shape elements, which form components, that are ordered in compositions, which get presented in views... You know the drill, though I am switching Brad Frost's nomenclature into something more front-end friendly. When it comes to code generation, Figmagic tries to do this in a better way. Instead of over-eagerly promoting hardcoded code generation from huge and complex components, Figmagic tries to competently, or at least "mostly-right", handle automation for things that should not be too context-dependent or manual input-heavy. Code should also be as close to "normal" as possible—meaning that generated code binds values to tokens and not some random px values as other platforms do. All of this can happen because Figmagic assumes a way of working that respects standard CSS and HTML: no magic trickery! Figmagic avoids some of the cognitive and technical overhead by introducing a concept called Elements, which are Figmagic-compliant components that can be output into code. By composing larger components out of simple Elements, code generation is made much more manageable while also promoting much more structured design.

ux tokens design-tokens figma theo design-specs figma-api design-system salesforce-theo

Open Door Logistics Studio - Intelligient software for Vechile routing and Territory mangement

•    Java

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Open Door Logistics Studio is an easy-to-use standalone open source desktop application for performing (a) analysis of your customer locations, (b) sales territory design and mapping and (c) vehicle fleet routing & scheduling - all using an Excel spreadsheet. It supports Territory design, Territory mapping, Vehicle routing & scheduling.

logistics territory-design territory-management territory-mapping

psychopy - For running psychology and neuroscience experiments

•    Python

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PsychoPy is an open-source package for creating psychology stimuli in Python (A real and free alternative to Matlab). PsychoPy combines the graphical strengths of OpenGL with the easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. The goal is to provide, for the busy scientist (including me!), tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. The code is platform independent, using Python and C libraries that are widely available.

science neuroscience experiment experimental-design experiment-control psychophysics psycholinguistics linguistics psychopy psychology

Hypertable - A high performance, scalable, distributed storage and processing system for structured

•    C++

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Hypertable is based on Google's Bigtable Design, which is a proven scalable design that powers hundreds of Google services. Many of the current scalable NoSQL database offerings are based on a hash table design which means that the data they manage is not kept physically ordered. Hypertable keeps data physically sorted by a primary key and it is well suited for Analytics.

no-sql distributed-database column-store analytics database distributed scalable cloud-database

digital-wellbeing-experiments-toolkit - Code components for starting your own Digital Wellbeing experiments

•    Kotlin

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This repository contains example code for some of the Android features used to create Experiments for the Digital Wellbeing collection on the Experiments with Google site. Each directory contains a simple building block that you could use to start creating your own Digital Wellbeing Experiment, or integrate into an existing project. The platform is a collection of ideas and tools that help people find a better balance with technology - exploring themes like how we find focus, avoid distractions and spend more quality time with others. We hope to inspire you to make digital wellbeing a key aspect to everything you design and make. Code from the experiments is open source, and we have created helpful guides to get you started. The more people that get involved the more we can all learn about building better technology for everyone.

snappydata - SnappyData - The Spark Database. Stream, Transact, Analyze, Predict in one cluster

•    Scala

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Apache Spark is a general purpose parallel computational engine for analytics at scale. At its core, it has a batch design center and is capable of working with disparate data sources. While this provides rich unified access to data, this can also be quite inefficient and expensive. Analytic processing requires massive data sets to be repeatedly copied and data to be reformatted to suit Spark. In many cases, it ultimately fails to deliver the promise of interactive analytic performance. For instance, each time an aggregation is run on a large Cassandra table, it necessitates streaming the entire table into Spark to do the aggregation. Caching within Spark is immutable and results in stale insight. At SnappyData, we take a very different approach. SnappyData fuses a low latency, highly available in-memory transactional database (GemFireXD) into Spark with shared memory management and optimizations. Data in the highly available in-memory store is laid out using the same columnar format as Spark (Tungsten). All query engine operators are significantly more optimized through better vectorization and code generation. The net effect is, an order of magnitude performance improvement when compared to native Spark caching, and more than two orders of magnitude better Spark performance when working with external data sources.

snappydata spark memory-database analytics stream transaction scale

TDengine - Big data platform designed and optimized for the Internet of Things

•    C

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TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.

iot database monitoring time-series bigdata full-stack connected-vehicles industrial-iot time-series-database analytics real-time-analytics column-store columnar-database

The Latin Project

•    Python

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The Latin Project's goal is to provide tools to create complex Latin programs. The main goal is the development of PyLatin, a collection of Python modules capable of things such as generation paradigms, creating vocabulary lists, and so on.

generator-style-prototype - Yeoman Generator for Style Prototypes

•    Javascript

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Style Prototypes are a Pattern Library tool for designing in browser. Style prototyping is a technique used to create a typical instance of a design from which a final site can be assembled. Style Prototypes are a unique tool because, not only are they responsive by default (meaning your client will be able to sign off on styles they've been able to see natively on all browsers and devices), they encourage Style and Color Guide driven Style Tile and Component Guide generation. This means that after you've built out your Style Tile, you'll be on your way to having finished your Style Guide, which you need to for Style Guide Driven Design (and coincidentally takes lots of design decisions off of the shoulders of Front End Developers). You'll also never need to have someone guess at what colors they can use with a fully built out Color Guide with both hex and Sass values.

yeoman-generator web app front-end design-in-browser style-guide sass

react-experiments - React components for implementing UI experiments

•    Javascript

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react-experiments is a set of React components for implementing UI experiments.react-experiments was built to work with PlanOut.js and most of its constructs are inspired by the structure of PlanOut.js. This library will work out of the box if you pass it an instantiated PlanOut Namespace or Experiment class, but if you want to use your own methods of assigning experiment parameters and logging exposure then you can extend the base experiment class and pass that as the experiment class prop.

react react-experiments planout ab-testing ab a/b split-testing ui-experiments experiments-react react-component component

Kendo Tournament Generator

•    Java

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Tool for managing a kendo tournament

Apache Mnemonic - Non-volatile hybrid memory storage oriented library

•    Java

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Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.

memory non-volatile-memory cache

Blade - Lightning fast and elegant MVC framework for Java8

•    Java

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Blade is a pursuit of simple, efficient Web framework, so that Java web development even more powerful, both in performance and flexibility. The design is simple, easy to understand and doesn't introduce many layers between you and the standard library. The goal of this project is that the users should be able to understand the whole framework in a single day. It supports RESTful style routing interface. It has no invasive interceptors and provides the writing of DSL grammar.

mvc-framework restful blade netty4 template-engine java8 web-framework web-application-framework

bsuite - bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

•    Python

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bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent with two main objectives. This library automates evaluation and analysis of any agent on these benchmarks. It serves to facilitate reproducible, and accessible, research on the core issues in RL, and ultimately the design of superior learning algorithms.

Fyne - Cross platform GUI in Go based on Material Design

•    Go

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The Fyne toolkit is an easy to learn, free and open source, platform for building graphical applications for desktop, mobile and beyond. Combining the power and simplicity of the Go programming language with a carefully crafted library of widgets it is now easier than ever before to build your application and deploy it across all platforms and stores.

gui desktop-application gui-framework application-development app-development widget material-design mobile-development

PyPattyrn - A simple library for implementing common design patterns.

•    Python

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PyPattyrn is a python package aiming to make it easier and faster to implement design patterns into your own projects. Design patterns by nature cannot be directly translated into code as they are just a description of how to solve a particular problem. However, many of the common design patterns have boilerplate code that is common throughout all implementations of the pattern. This package captures that common code and makes it easy to use so that you dont have to write it yourself in all your projects.

design-patterns library

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

•    Jupyter

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"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networks

nfstream - NFStream: a Flexible Network Data Analysis Framework.

•    Python

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NFStream is a Python framework providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data analytics framework for researchers providing data reproducibility across experiments. Binary installers for the latest released version are available on Pypi.

data-science machine-learning data-mining netflow pcap packet-analyser traffic-analysis artificial-intelligence cybersecurity network-monitoring data-analysis dataset-generation network-analysis packet-capture ndpi network-security deep-packet-inspection traffic-classification

migen - A Python toolbox for building complex digital hardware

•    Python

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Despite being faster than schematics entry, hardware design with Verilog and VHDL remains tedious and inefficient for several reasons. The event-driven model introduces issues and manual coding that are unnecessary for synchronous circuits, which represent the lion's share of today's logic designs. Counter- intuitive arithmetic rules result in steeper learning curves and provide a fertile ground for subtle bugs in designs. Finally, support for procedural generation of logic (metaprogramming) through "generate" statements is very limited and restricts the ways code can be made generic, reused and organized. To address those issues, we have developed the Migen FHDL library that replaces the event-driven paradigm with the notions of combinatorial and synchronous statements, has arithmetic rules that make integers always behave like mathematical integers, and most importantly allows the design's logic to be constructed by a Python program. This last point enables hardware designers to take advantage of the richness of the Python language - object oriented programming, function parameters, generators, operator overloading, libraries, etc. - to build well organized, reusable and elegant designs.

GoatCounter - Easy web analytics. No tracking of personal data

•    Go

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GoatCounter is an open source web analytics platform available as a hosted service (free for non-commercial use) or self-hosted app. It aims to offer easy to use and meaningful privacy-friendly web analytics as an alternative to Google Analytics or Matomo.

analytics web-analytics google-analytics-alternative metrics page-views statistics