Foundry Virtual Tabletop, commonly shortened to Foundry VTT or FVTT, is a commercial, self-hosted virtual tabletop application for role-playing games. It provides a stage for visualizing the game environment and tools allowing the game master and players to organize and track statistics and notes. The software is highly modular and depends on the community-maintained ecosystem of add-on modules that modify the software's behavior and implement different game systems. Perpetual licenses, which include updates, are offered for a one-time fee. == Features == Foundry Virtual Tabletop is a highly modular Node.js web application that is run locally by the Gamemaster or hosted on a remote server. Players connect to their gamemaster's Foundry VTT instance over the network using their web browser. It is system-agnostic in that its core feature-set is not restricted to a specific game system. Systems, specific features and game content are implemented as add-on modules, which can be individually downloaded from a public repository. The module repository contains paid, official content, as well as freely available community-made modules that enhance functionality of the software. As of May 2025, 350 individual game systems are implemented as modules. Individual settings created by the Game Master are termed Worlds in the interface and contain the list of modules that should be loaded as well as world-specific content, which can be added by the gamemaster. This content is grouped into Scenes, Actors, Items and Journals. Battle and world maps are created as Scenes, which contain the backdrop and data on placement of walls, light sources and other entities. Tokens representing Actors, which are player characters, vehicles or NPCs, can be placed on these Scenes to be moved by the user that owns them. Other entities that interact or integrate with actors are termed Items; these can be objects, but also game system-specific concepts such as character classes. Journals are text documents that can link to other entities present in the World or modules. Viewing and editing permissions can be set individually for each entity. The software features a custom lighting engine that determines visibility of certain areas on each battle map depending on the position of players' characters, also revealing areas covered by fog of war. It also contains tools for map creation and comes with a small asset library. == History == Foundry Gaming LLC founder Andrew Clayton, commonly known under his online nickname Atropos, began development of Foundry VTT in 2018 for personal use after becoming dissatisfied with the feature set and business models of other virtual tabletops. Foundry VTT was initially developed for Linux, which remains its primary platform, with support for other platforms having been developed later. Foundry Gaming LLC was incorporated in Spokane, Washington on October 9, 2018, with the software remaining in private beta-testing until May 2020, when it was publicly released. In November 2020, Cubicle 7 partnered with Foundry to bring official content modules for its game system Warhammer Fantasy Roleplay to Foundry VTT. Later, in 2025, Clayton would state that this first major publisher deal was of significant importance to Foundry VTT's growth and credits the community developers of the WFRP system module for making it possible in the first place. In November 2023, Paizo partnered with Foundry to bring official content modules for Pathfinder Roleplaying Game to Foundry VTT. In January 2024, Foundry publicly announced its partnership with Wizards of the Coast in bringing official Dungeons & Dragons content to Foundry VTT, with the first official module, Phandelver and Below: The Shattered Obelisk, having been released in February 2024. == Development == As of 2023, the Foundry VTT software itself is being developed and managed by a team of 9 people, while a content team of 12 people is working with partnered publishers to compile content into downloadable modules. The content team also develops in-house content published by Foundry Gaming LLC. Stated goals are to create a virtual tabletop software that offers a one-time purchase and content ownership, make use of modern web technologies, and provide a platform for developers to build upon. Clayton has stated that integration of Generative AI into Foundry VTT is not planned, citing ethical and legal concerns and calling its usage within the industry a "betrayal of the creative people who made the TTRPG industry what it is in the first place". == Reception == Foundry VTT is one of the most popular virtual tabletops for TTRPGs; in particular, as a self-hosted web-based VTT, it is known as a modern alternative to the software as a service Roll20. Wargamer named it one of the three "best virtual tabletops for D&D in 2023", noting its active community and high degree of technical complexity, which allows for customization not seen in other products at the cost of a much steeper learning curve. Comic Book Resources called it an "underrated gem" and "incredibly versatile" for similar reasons, while also praising its lighting engine and visual fidelity. As the previously mentioned outlets do, Foundry's modular ecosystem and technical implementation are often mentioned as good features, but also as a source of frustration for new users. In a video interview, Clayton acknowledges this issue and affirms that the development team intends to make usage of more technical features "friction-less" and will reduce module breakage between updates in the future.
Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. Statistics and mathematical optimisation methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical viewpoint, probably approximately correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms can be described as empirical risk minimisation under this framework. == History == The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used during this time period. The earliest machine learning program was introduced in the 1950s, when Samuel invented a computer program that calculated the chance of winning in checkers for each side, but the history of machine learning is rooted in decades of efforts to study human cognitive processes. In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells. The Hebbian theory of neuron interaction set the groundwork for how many machine learning algorithms work, with connected artificial neurons changing the strength of their connections based on data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including Walter Pitts and Warren McCulloch, who proposed the first mathematical model of neural networks including algorithms that mirror human thought processes. By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "goof" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nils Nilsson's book "Learning Machines", dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981, a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned is fundamentally operational rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question, "Can machines think?", is replaced by asking whether machines can convincingly imitate a human in its responses to human-posed questions. In 2014 Ian Goodfellow and others introduced generative adversarial networks (GANs) which could produce realistic synthetic data. By 2016 AlphaGo had won against top human players in Go using reinforcement learning techniques. == Relationships to other fields == === Artificial intelligence === As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favour. Work on symbolic/knowledge-based learning continued within AI, leading to inductive logic programming (ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural network research was abandoned by AI and computer science around the same time. This subfield, termed "connectionism", was continued by researchers from other disciplines, including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation. Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. === Data compression === === Data mining === Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. Machine learning also has intimate ties to optimization: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). === Generalization === Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms. === Statistics === Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be. Leo Breiman distinguished two statistical modelling paradigms: the data model and the algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, producing the field of statistical learning. === Statistical physics === Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks. Statistical physics is thus
Web application
A web application (or web app) is application software that is created with web technologies and runs via a web browser. Web applications emerged during the late 1990s and allowed for the server to dynamically build a response to the request, in contrast to static web pages. Web applications are commonly distributed via a web server. There are several different tier systems that web applications use to communicate between the web browsers, the client interface, and server data. Each system has its own uses as they function in different ways. However, there are many security risks that developers must be aware of during development; proper measures to protect user data are vital. Web applications are often constructed with the use of a web application framework. Single-page applications (SPAs) and progressive web apps (PWAs) are two architectural approaches to creating web applications that provide a user experience similar to native apps, including features such as smooth navigation, offline support, and faster interactions. Web applications are often fully hosted on remote cloud services, can require a constant connection to them, and can replace conventional desktop applications for operating systems such as Microsoft Windows, thus facilitating the operation of software as a service as it grants the developer the power to tightly control billing based on use of the remote services as well as vendor lock-in by hosting data remotely. Modern browsers such as Chrome offer sandboxing for every browser tab which improves security and restricts access to local resources. No software installation is required as the app runs within the browser which reduces the need for managing software installations. With the use of remote cloud services, customers do not need to manage servers as that can be left to the developer and the cloud service and can use the software with a relatively low power, low-resource PC such as a thin client. The source code of the application can stay the same across operating systems and devices of users with the use of responsive web design, since it only needs to be compatible with web browsers which adhere to web standards, making the code highly portable and saving on development time. Numerous JavaScript frameworks and CSS frameworks facilitate development. == History == The concept of a "web application" was first introduced in the Java language in the Servlet Specification version 2.2, which was released in 1999. At that time, both JavaScript and XML had already been developed, but the XMLHttpRequest object had only been recently introduced on Internet Explorer 5 as an ActiveX object. Beginning around the early 2000s, applications such as "Myspace (2003), Gmail (2004), Digg (2004), [and] Google Maps (2005)," started to make their client sides more and more interactive. A web page script is able to contact the server for storing/retrieving data without downloading an entire web page. The practice became known as Ajax in 2005. Eventually this was replaced by web APIs using JSON, accessed via JavaScript asynchronously on the client side. In earlier computing models like client-server, the processing load for the application was shared between code on the server and code installed on each client locally. In other words, an application had its own pre-compiled client program which served as its user interface and had to be separately installed on each user's personal computer. An upgrade to the server-side code of the application would typically also require an upgrade to the client-side code installed on each user workstation, adding to the support cost and decreasing productivity. Additionally, both the client and server components of the application were bound tightly to a particular computer architecture and operating system, which made porting them to other systems prohibitively expensive for all but the largest applications. Later, in 1995, Netscape introduced the client-side scripting language called JavaScript, which allowed programmers to add dynamic elements to the user interface that ran on the client side. Essentially, instead of sending data to the server in order to generate an entire web page, the embedded scripts of the downloaded page can perform various tasks such as input validation or showing/hiding parts of the page. "Progressive web apps", the term coined by designer Frances Berriman and Google Chrome engineer Alex Russell in 2015, refers to apps taking advantage of new features supported by modern browsers, which initially run inside a web browser tab but later can run completely offline and can be launched without entering the app URL in the browser. == Structure == Traditional PC applications are typically single-tiered, residing solely on the client machine. In contrast, web applications inherently facilitate a multi-tiered architecture. Though many variations are possible, the most common structure is the three-tiered application. In its most common form, the three tiers are called presentation, application and storage. The first tier, presentation, refers to a web browser itself. The second tier refers to any engine using dynamic web content technology (such as ASP, CGI, ColdFusion, Dart, JSP/Java, Node.js, PHP, Python or Ruby on Rails). The third tier refers to a database that stores data and determines the structure of a user interface. Essentially, when using the three-tiered system, the web browser sends requests to the engine, which then services them by making queries and updates against the database and generates a user interface. The 3-tier solution may fall short when dealing with more complex applications, and may need to be replaced with the n-tiered approach; the greatest benefit of which is how business logic (which resides on the application tier) is broken down into a more fine-grained model. Another benefit would be to add an integration tier, which separates the data tier and provides an easy-to-use interface to access the data. For example, the client data would be accessed by calling a "list_clients()" function instead of making an SQL query directly against the client table on the database. This allows the underlying database to be replaced without making any change to the other tiers. There are some who view a web application as a two-tier architecture. This can be a "smart" client that performs all the work and queries a "dumb" server, or a "dumb" client that relies on a "smart" server. The client would handle the presentation tier, the server would have the database (storage tier), and the business logic (application tier) would be on one of them or on both. While this increases the scalability of the applications and separates the display and the database, it still does not allow for true specialization of layers, so most applications will outgrow this model. == Security == Security breaches on these kinds of applications are a major concern because it can involve both enterprise information and private customer data. Protecting these assets is an important part of any web application, and there are some key operational areas that must be included in the development process. This includes processes for authentication, authorization, asset handling, input, and logging and auditing. Building security into the applications from the beginning is sometimes more effective and less disruptive in the long run. == Development == Writing web applications is simplified with the use of web application frameworks. These frameworks facilitate rapid application development by allowing a development team to focus on the parts of their application which are unique to their goals without having to resolve common development issues such as user management. In addition, there is potential for the development of applications on Internet operating systems, although currently there are not many viable platforms that fit this model.
SAP StreamWork
SAP StreamWork is an enterprise collaboration tool from SAP SE released in March 2010, and discontinued in December 2015. StreamWork allowed real-time collaboration like Google Wave, but focused on business activities such as analyzing data, planning meetings, and making decisions. It incorporated technology from Box.net and Evernote to allow users to connect to online files and documents, and document-reader technology from Scribd allowed users to view documents directly within its environment. StreamWork supported the OpenSocial set of application programming interfaces (APIs), allowing it to connect to tools built by third-party developers, such as Google Docs. A version of StreamWork intended for large enterprises used a virtual appliance based on Novell's SUSE Linux Enterprise to connect it to business systems, including those from SAP.
WorkingPoint
WorkingPoint is a web-based application that provides a suite of small business management tools. It is designed to serve as a single point of access for various business operations, featuring a user-friendly interface. WorkingPoint's functionalities include double-entry bookkeeping, contact management, inventory management, invoicing, and bill and expense management. == Company == WorkingPoint, formerly Netbooks Inc, is a privately held corporation based in San Francisco, CA. The company is backed by CMEA Capital, also based in San Francisco. WorkingPoint has about ten employees and is led by CEO Tate Holt and Chairman Tom Proulx. Proulx is a co-founder of Intuit and an original author of that company’s Quicken personal finance software. The company was founded in 2007 under its original name Netbooks by co-creator Ridgely Evers. Evers set out to design a product that was more user-friendly than Intuit’s Quickbooks, which he also co-created. In mid-2009 the company officially rebranded itself and its flagship product “WorkingPoint”. The purpose of the re-branding was to disassociate the company from the product category of small laptops also known as netbooks. == Social Media Presence == WorkingPoint maintains a daily blog geared toward small business owners and managers. Each week the blog is updated with 3 WorkingPoint product feature or “how-to” posts, 2 subscriber company profiles, and 2 small business coaching posts. The company also maintains a Twitter page and a Facebook page. == Product Description (Free Version) == WorkingPoint allows businesses to invoice up to five customers (repeatedly) and provides account access for up to two individual users free of charge. Online Invoicing WorkingPoint allows users to create customized quotes and invoices online. The invoices can be used to bill customers via email or hardcopy post. WorkingPoint compiles the info from these invoices so users can track customer payments, inventory costs, shipping charges, accounts receivable and sales taxes. Users can also manage customer overpayments, provide customer loyalty discounts, and view a customer invoice history. Bill & Expense Management Users can track their bills and expenses by entering info into the WorkingPoint interface. WorkingPoint compiles this info so users can track categorized expenses, accounts paid, accounts payable, and vendor purchase history. The interface also allows users to add to their inventory while entering billing info. Double-Entry Bookeeping WorkingPoint automatically records entries under the double-entry bookkeeping system (also known as debits and credits) when the user completes invoicing and expense forms. Users can view transactions in general ledger format and perform closing entries if necessary. This functionality is designed for users who do not have an accounting background. Business Contact Management WorkingPoint provides an interface for users to manage their customer and vendor contact info. The software automatically tracks the user’s relationship with contacts, so users can track a contact’s sales and purchase history. Contacts can be imported and exported via numerous email clients including Microsoft Outlook, Yahoo! Mail, Google Gmail, and Mac Address Book. Inventory Management The software automatically adjusts inventory quantities after every purchase and sale. Users can track their current inventory quantity, average cost of inventory on-hand, cost of goods sold (COGS) and top-selling products. Users can also make manual adjustments to inventory when necessary. Financial Reporting Users can view a balance sheet, income statement, or cash flow statement pertaining to their business. The software automatically manages accruals to produce the balance sheet and income statement. Users can choose a data range from which to draw any of these reports. Financial reports can be converted to pdf format or exported (with formulas intact) to OpenOffice or Microsoft Excel. Cash Management WorkingPoint enables users to monitor cash balances on their bank accounts. The software automatically tracks cash inflows and outflows when users manage their accounts payable and accounts receivable. Business Dashboard The Business Dashboard visually and graphically displays key real-time business data. Users can customize the Dashboard to display data of their choosing. Online Company Profile Users can create an online company profile in order to have a presence on the Internet and as a basis for participation in WorkingPoint’s small business community features. Public profiles are featured in the WorkingPoint Company Directory and can be viewed externally using the URL format: https://businessname.workingpoint.com. == Product Description (Premium Version) == The premium version of WorkingPoint costs $10 per month. It includes all of the functionalities of the free version, allowing unlimited invoicing and account access. It also offers the following functions: 1099 Tax Reporting, invoice payment collection via PayPal, Email Marketing via VerticalResponse, and the Premium Reports & Accounting Package. 1099 Tax Reporting Users can identify qualifying companies and individuals for IRS Form 1099 or IRS Form 1096 reporting. WorkingPoint automatically tracks payments made to these companies and individuals. Users can then generate 1099 reports for distribution. Premium Reports & Accounting Package This includes: a Daily Operating Report providing users with sales and cash flow information, customizable accounts categorization, and cash flow statements using the indirect method of reporting. Invoice Payment Collection via PayPal Users can collect payment on their invoices via PayPal. Email Marketing via VerticalResponse The WorkingPoint premium package includes 500 email credits with the email marketing firm VerticalResponse.
Kubity
Kubity is a cloud-based 3D communication tool that works on desktop computers, the web, smartphones, tablets, augmented reality gear, and virtual reality glasses. Kubity is powered by several proprietary 3D processing engines including "Paragone" and "Etna" that prepare the 3D file for transfer over mobile devices. Kubity has practical applications for architecture, interior design, engineering, product design, film, and video games among others. The majority of its users create 3D models using SketchUp or Autodesk Revit software. Kubity products include the Kubity web app and Kubity Go (a mobile application for iOS and Android). Kubity is compatible across many platforms, devices and operating systems including: iOS, Android, Firefox, Chrome, Windows, MacOS, and Linux. == History == Kubity was created by SPK Technology (ex Kubity S.A.S.), a Paris-based software company specializing in automatic 3D data optimization and visualization. Founded in 2012 by a group of software engineers and an urban projects developer, they united around a simple idea: create a way for anyone, anywhere to simply and intuitively explore 3D models on smartphones and computers. In order to bring architects, engineers and designers together with their clients around a 3D model, it was essential to develop an interactive platform that supported multiple desktop and mobile devices for instantaneous and fluid 3D navigation. With specifications in place, 15 engineers fused together several technologies: 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and virtual reality headset integration. In January 2014, the first public Kubity prototype (1.0 Amethyst) was launched to a small group of beta testers with a plug-in that allowed users to import 3D models from SketchUp to their browser. A global release was announced in April 2014 at the SketchUp Basecamp in Vail, Colorado. In May 2015, Kubity launched a web application that worked using WebGL technology (2.0 Citrine). For the first time, users were able to drag and drop any SketchUp file in a web browser without having to install a plug-in. In December 2015, Kubity launched a mobile application on the App Store for iPhone, iPod, and iPad as well as on Google Play for Android devices (3.0 Druzy). In November 2016, Kubity launched support for Oculus Rift and HTC Vive (4.0 Emerald). Beginning in November 2017, Kubity launched a full suite rollout of mobile applications over six months that included Kubity AR for augmented reality, Kubity VR for virtual reality, and Kubity Mirror for remote presentations and screen mirroring (5.0 Feldspar). In September 2018, a one-click plugin for SketchUp and Revit (Kubity PRO), along with a mobile-first revamp of Kubity Go was launched, allowing PRO-to-Go device pairing for automatic mobile sync (6.0 Gypsum). In early 2019, the Kubity Go application was updated to include fully integrated AR, VR, and screen mirroring functionalities, killing off the dedicated companion apps Kubity AR, Kubity VR and Kubity Mirror in the process (7.0 Heliotrope). In January 2020, support for the Kubity PRO plugin for SketchUp and Revit was migrated to a SketchUp-only web app. == Technology == Kubity is powered by a proprietary 3D crystallization engine known as "Paragone"; a technology developed by SPK Technology. Paragone takes constrained information from a 3D file and runs it through the "BlockWave" algorithm (US Patent 10,482.629), also developed by SPK Technology. BlockWave is a multiphase optimization algorithm that combines 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and mixed reality headset integration to create a crystallized universal format of the original file. One phase of the BlockWave algorithm is based on the quadric-based polygonal surface simplification algorithm, performed using predefined heuristics, and is associated with a plurality of simplified versions of the 3D model, each version being associated with a predefined level of detail adapted to the user specific end device. BlockWave extracts data content, geometry and textures, then sets quadrics for each top of the original 3D model, and identifies pairs of adjacent tops linked by vertices. The algorithm uses a local collapsing operator and a top-plan error metric to obtain a fixed number of faces or a maximum defined error; 3D meshing is simplified by replacing two points with one, then deleting the degrading faces and updating adjacent relations. Once decimation is completed, texture optimization is set using texture target parameters allowing maximized GPU memory to improve computing time. With texture encoding completed, the crystallized universal 3D file can now be easily opened on any user-specific end device and played across most digital devices with real-time rendering. == Features == === 3D Crystallization === A user converts (or crystallizes) a 3D file by exporting it with the Kubity web app. Crystallization adds features like AR/VR and cinematic fly-through tour as well as assigns the model a dedicated QR code. === Automatic Mobile Sync === When a 3D model is exported, it is automatically synced to Kubity Go on the user's mobile device. From there, it can be accessed, explored, and shared with others with or without an internet connection. === Security and Management === User models can be managed all in one place on Kubity Go or in a browser from their account. Models can be renamed, password-protected, shared, and played. === Augmented Reality === Developed using Apple ARKit and Google ARCore technology, Kubity Go's augmented reality feature maps the environment in a room detecting horizontal planes like tables and floors to track and place 3D objects. By blending digital objects and information with the environment, Kubity allows users to interact with 3D models in true augmented reality. Built-in communication features allows users to instantly share 3D models with anyone over text, email, social media, or direct device-to-device with a QR Code. Platform Support AR supports devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017). AR for Android requires Android 7.0 or later and access to the Google Play Store. === Virtual Reality === VR allows users to explore SketchUp models and Revit projects on-the-go right from a mobile device using Oculus Go, Google Cardboard, Samsung Gear VR, or the glasses-free Magic Window feature. Kubity's virtual reality feature is compatible with Oculus Go, Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the mobile virtual reality headset, Samsung Gear VR. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018), and Samsung Galaxy S9/Galaxy S9+. === Screen Mirroring === Screen mirroring allows a user to sync the sender device to a receiver on a webpage, then control from the sender device to give a remote presentation of the 3D model. Devices are easily synced by entering a six-digit number displayed on the receiving computer. == Platform support == On iOS, the Kubity application is compatible with devices running on the version 9.0 or higher. On Android, Kubity is compatible with devices running on the version 4.4 “Kit Kat” or higher. The web version of Kubity applications currently support web browsers compatible with WebGL2 : Mozilla Firefox and Google Chrome. AR is compatible with devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017), and Android devices. Requires Android 7.0 or later and access to the Google Play Store. VR is compatible with Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the Samsung Gear VR and Oculus Go. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018) and Samsung Galaxy S9/Galaxy S9+.
ActivityPub
ActivityPub is a protocol and open standard for decentralized social networking. It provides a client-to-server (C2S) API for creating and modifying content, as well as a federated server-to-server (S2S) protocol for delivering notifications and content to other servers. ActivityPub is the defining standard of the Fediverse, a decentralised social network of various social interaction models, and content types, which consists of independently managed instances of software such as Mastodon, Pixelfed and PeerTube, among others. ActivityPub is considered to be an update to the ActivityPump protocol used in pump.io, and the official W3C repository for ActivityPub is identified as a fork of ActivityPump. The creation of a new standard for decentralized social networking was prompted by the complexity of OStatus, the most commonly used protocol at the time. OStatus was built using a multitude of technologies (such as Atom, Salmon, WebSub and WebFinger), a product of the infrastructure used in GNU social (the originator and largest user of the OStatus protocol), which made it difficult to implement the protocol into new software. OStatus was also only designed to work with microblogging services, with little flexibility to the types of data that it could hold. The standard was first published by the World Wide Web Consortium (W3C) as a W3C Recommendation in January 2018 by the Social Web Working Group (SocialWG), a working group chartered to build the protocols and vocabularies needed to create a standard for social functionality. Shortly after, further development was moved to the Social Web Community Group (SocialCG), the successor to the SocialWG. == Design == ActivityPub uses the ActivityStreams 2.0 format for building its content, which itself uses JSON-LD. The three main data types used in ActivityPub are Objects, Activities and Actors. Objects are the most common data type, and can be images, videos, or more abstract items such as locations or events. Activities are actions that create and modify objects, for example a Create activity creates an object. Actors are representative of an individual, a group, an application or a service, and are the owners of objects. Every actor type contains an inbox and outbox stream, which sends and receives activities for a user. In order to publish data (for example liking an article), a user creates an activity that declares that they liked an Article object and publishes it to their outbox, where it is then delivered by the ActivityPub server via a POST request to the inboxes listed in the activity's to, bto, cc and bcc fields. The receiving servers then account for the newly received activity and update the article by adding the like action to it. === Example data === An example actor object that represents a user account: An example activity that likes an article object: An example article object: == Project status == The SocialCG previously organized a yearly free conference called ActivityPub Conf about the future of ActivityPub. Triages are held regularly to review issues pertaining to the ActivityPub and ActivityStreams 2.0 specifications as part of the SocialCG. In 2023, Germany's Sovereign Tech Fund donated €152,000 to socialweb.coop with the goal of building a new suite for testing various ActivityPub implementations and their compliance with the specification. === Adoption === The initial wave of adoption for ActivityPub (circa 2016–2018) came from software that was already using OStatus as their federation protocol, such as Mastodon, GNU social and Pleroma. Following the acquisition of Twitter by Elon Musk in 2022, many groups of users that were critical of the acquisition migrated to Mastodon, bringing new attention to the ActivityPub protocol with it. Various major social media platforms and corporations have since pledged to implement ActivityPub support, including Tumblr, Flipboard and Meta Platforms' Threads. Threads introduced crossposting to ActivityPub in 2024 for users outside of the European Economic Area, however full 2-way compatibility remains incomplete as of 2025. == Criticism == === Accidental denial-of-service attacks === Poorly optimized ActivityPub implementations can cause unintentional distributed denial-of-service (DDOS) attacks on other websites and servers, due to the decentralized nature of the network. An example would be Mastodon's implementation of OpenGraph link previews, wherein every instance that receives a post that contains a link with OpenGraph metadata will download the associated data, such as a thumbnail, in a very short timeframe, which can slow down or crash servers as a result of the sudden burst of requests. === Account migration === ActivityPub has been criticized for not natively supporting moving accounts from one server to another, forcing implementations to build their own solutions. While there has been work on building a standardized system for migrating accounts using the Move activity via the Fediverse Enhancement Proposal organization, the current proposal only allows for basic follower migration, with all other data remaining linked to the original account. === Missing content and data === ActivityPub implementations have been criticized for missing replies and parts of reply threads from remote posts, and presenting outdated statistics (e.g. likes and reposts) about remote posts. However, this isn't a problem with the ActivityPub protocol itself, but with implementations not refreshing their content for updated data when needed. == Software using ActivityPub == === Future implementations === Flarum, an internet forum software Forgejo, a Git forge and development platform === Uncertain future implementations === GitLab, a Git forge and development platform which had previously had an open issue discussing the topic, but was later closed due to the development team moving focus to other areas. Tumblr, a microblogging platform. Despite previous statements from Automattic CEO Matt Mullenweg, ActivityPub integration has been delayed indefinitely. The integration would have been implemented with its WordPress migration, as the first-party plugin for interoperability would have been used for federation. Flickr, an image and video hosting site.