AI Code Fixer

AI Code Fixer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Healthy Together

Healthy Together is a health technology company that provides software for Health & Humans Services Departments. Healthy Together supports a “One Door” approach to eligibility, enrollment, and management for programs like Medicaid, Supplemental Nutrition Assistance Program, TANF and WIC, as well as behavioral health (988), disease surveillance, vital records, child welfare and more. The platform's use is to increase the reach and efficacy of program initiatives, improve health equity and reduce cost. Software is available in the United States of America with current deployments in Florida, Oklahoma. The United States Department of Veterans Affairs also utilizes Healthy Together's mobile platform. == Development == Healthy Together launched in March 2020 and builds software for public health and health and human services departments. The Florida Department of Health began using the platform in September 2020 to deliver real-time test results to residents. Over 50% of households in Florida have adopted the mobile application. On December 6, 2022, the Advanced Technology Academic Research Center (ATARC) awarded Healthy Together and the State of Florida's Department of Health with a Digital Experience Award at their 2022 GITEC Emerging Technology Award Ceremony in Washington, D.C. to recognize success of the project. The partnership was also highlighted on the Federal News Network's show Federal Drive. The platform is also used at universities in Oklahoma. In November 2022, the United States Department of Veterans Affairs and Healthy Together announced a collaboration to expand access to health records for Veterans. The platform provides 18 million Veterans with access to their health information through their smartphones and mobile devices. In December 2022, the integration was recognized as one of Healthcare IT News' Top 10 stories of 2022.
Read more →
GCube system

gCube is an open source software system specifically designed and developed to enact the building and operation of a Data Infrastructure providing their users with a rich array of services suitable for supporting the co-creation of Virtual Research Environments and promoting the implementation of open science workflows and practices. It is at the heart of the D4Science Data Infrastructure. == Overview == It is primarily organised in a number of web service called to offer functionality supporting the phases of knowledge production and sharing. In addition, it consists of a set of software libraries supporting service development, service-to-service integration, and service capabilities extension, and a set of portlets dedicated to realise user interface constituents facilitating the exploitation of one or more services. It is designed and conceived to enact system of systems. In fact, its gCube services rely on standards and mediators to interact with other services as well as are made available by standard and APIs to make it possible for clients to use them. For instance, the DataMiner service implements the Web Processing Service protocol to facilitate clients to execute processes. The set of components dealing with Identity and Access Management rely on Keycloak and federates other IDMs thus making the overall Authentication and the Authorization management compliant with open standards such as OAuth2, User-Managed Access (UMA), and OpenID Connect (OIDC)protocols. The Catalogue relies on DCAT, OAI-PMH, and Catalogue Service for the Web to collect contents from other catalogues and data sources and offers its content by DCAT, OAI-PMH, and a proprietary REST API (gCat REST API). Its Continuous Integration/Continuous Delivery pipeline implemented by Jenkins represents an innovative approach to software delivering conceived to be scalable and easy to maintain and upgrade at a minimal cost. == History == gCube has been developed in the context of the D4Science initiative with the support of several EU projects.
Read more →
Foundry VTT

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.
Read more →
Content as a service

Content as a service (CaaS) or managed content as a service (MCaaS) is a service-oriented model, where the service provider delivers the content on demand to the service consumer via web services that are licensed under subscription. The content is hosted by the service provider centrally in the cloud and offered to a number of consumers that need the content delivered into any applications or system, hence content can be demanded by the consumers as and when required. Content as a Service is a way to provide raw content (in other words, without the need for a specific human compatible representation, such as HTML) in a way that other systems can make use of it. Content as a Service is not meant for direct human consumption, but rather for other platforms to consume and make use of the content according to their particular needs. This happens usually on the cloud, with a centralized platform which can be globally accessible and provides a standard format for your content. With Content as a Service, you centralize your content into a single repository, where you can manage it, categorize it, make it available to others, search for it, or do whatever you wish with it. == Overview == The content delivered typically could be one or more of the following The technical terminology related to equipment or spares that is required to procure or design the materials The industrial terminology of the equipment or spares Technical values pertaining to various types, specifications, applications, characteristics of equipment or spares Sourcing information which will help in procurement or supply-chain management of equipment or spares Descriptive specifications of equipment or spares based on the product reference number or identifier UNSPSC codes or industry practiced classifications ISO, IEC compliant terminology Ontology or Technical Dictionary of products & services Predefined content for specific business needs The term "Content as a service" (CaaS) is considered to be part of the nomenclature of cloud computing service models & Service-oriented architecture along with Software as a service (SaaS), Infrastructure as a service (IaaS), and Platform as a service (PaaS).
Read more →
TensorFlow Hub

TensorFlow Hub (also styled TF Hub) is an open-source machine learning library and online repository that provides TensorFlow model components, called modules. It is maintained by Google as part of the TensorFlow ecosystem and allows developers to discover, publish, and reuse pretrained models for tasks such as computer vision, natural language processing, and transfer learning. == Overview == TensorFlow Hub provides a central platform where developers and researchers can access pre-trained models and integrate them directly into TensorFlow workflows. Each module encapsulates a computation graph and its trained weights, with standardized input and output signatures. Modules can be loaded using the hub.load() function or through Keras integration via hub.KerasLayer, enabling users to perform transfer learning or feature extraction. == History == TensorFlow Hub was announced by Google in March 2018, with the first public version released shortly after. Its introduction coincided with the growing adoption of transfer learning techniques and the need for standardized model packaging. Over time, the hub expanded to include models such as the BERT family, MobileNet, EfficientNet, and the Universal Sentence Encoder. In 2020, research on “Regret selection in TensorFlow Hub” explored the problem of identifying optimal models for downstream tasks given a large repository of alternatives. == Applications == TensorFlow Hub hosts a variety of models across machine learning domains: Natural language processing: BERT, ALBERT language model, and Universal Sentence Encoder. Computer vision: ResNet, Inception (deep learning), MobileNet, EfficientNet. Speech and audio: spectrogram feature extractors and automatic speech recognition models. Multilingual embeddings: cross-lingual and sentence-level representations for machine translation and semantic similarity. Modules are widely used in education, academic research, and industry for prototyping and production deployment.
Read more →
Toad Data Modeler

Toad Data Modeler is a database design tool allowing users to visually create, maintain, and document new or existing database systems, and to deploy changes to data structures across different platforms. It is used to construct logical and physical data models, compare and synchronize models, generate complex SQL/DDL, create and modify scripts, and reverse and forward engineer databases and data warehouse systems. Toad's data modelling software is used for database design, maintenance and documentation. == Product History == Toad Data Modeler was previously called "CASE Studio 2" before it was acquired from Charonware by Quest Software in 2006. Quest Software was acquired by Dell on September 28, 2012. On October 31, 2016, Dell finalized the sale of Dell Software to Francisco Partners and Elliott Management, which relaunched on November 1, 2016 as Quest Software. == Features/Usages == Multiple database support - Connect multiple databases natively and simultaneously, including Oracle, SAP, MySQL, SQL Server, PostgreSQL, Db2, Ingres, and Microsoft Access. Data modelling tool - Create database structures or make changes to existing models automatically and provide documentation on multiple platforms. Logical and physical modelling - Build complex logical and physical entity relationship models and reverse, forward, and engineer databases. Reporting - Generate detailed reports on existing database structures. Model customization - Add logical data to user diagrams to customize user models. All Toad products typically have 2 releases per year. == Other features == Model Actions (Compare Models, Convert Model, Merge Models, Generate Change Script) Version Control System (Apache Subversion) Naming Conventions Auto Layout Multiple Workspaces Scripting and Customization Automation Object Gallery Full Unicode Support Integration with Toad for Oracle == Related Software == Erwin Data Modeler Oracle SAP MySQL SQL Server PostgreSQL IBM Db2 Ingres Microsoft Access
Read more →
View model

A view model or viewpoints framework in systems engineering, software engineering, and enterprise engineering is a framework which defines a coherent set of views to be used in the construction of a system architecture, software architecture, or enterprise architecture. A view is a representation of the whole system from the perspective of a related set of concerns. Since the early 1990s there have been a number of efforts to prescribe approaches for describing and analyzing system architectures. A result of these efforts have been to define a set of views (or viewpoints). They are sometimes referred to as architecture frameworks or enterprise architecture frameworks, but are usually called "view models". Usually a view is a work product that presents specific architecture data for a given system. However, the same term is sometimes used to refer to a view definition, including the particular viewpoint and the corresponding guidance that defines each concrete view. The term view model is related to view definitions. == Overview == The purpose of views and viewpoints is to enable humans to comprehend very complex systems, to organize the elements of the problem and the solution around domains of expertise and to separate concerns. In the engineering of physically intensive systems, viewpoints often correspond to capabilities and responsibilities within the engineering organization. Most complex system specifications are so extensive that no single individual can fully comprehend all aspects of the specifications. Furthermore, we all have different interests in a given system and different reasons for examining the system's specifications. A business executive will ask different questions of a system make-up than would a system implementer. The concept of viewpoints framework, therefore, is to provide separate viewpoints into the specification of a given complex system in order to facilitate communication with the stakeholders. Each viewpoint satisfies an audience with interest in a particular set of aspects of the system. Each viewpoint may use a specific viewpoint language that optimizes the vocabulary and presentation for the audience of that viewpoint. Viewpoint modeling has become an effective approach for dealing with the inherent complexity of large distributed systems. Architecture description practices, as described in IEEE Std 1471-2000, utilize multiple views to address several areas of concerns, each one focusing on a specific aspect of the system. Examples of architecture frameworks using multiple views include Kruchten's "4+1" view model, the Zachman Framework, TOGAF, DoDAF, and RM-ODP. == History == In the 1970s, methods began to appear in software engineering for modeling with multiple views. Douglas T. Ross and K.E. Schoman in 1977 introduce the constructs context, viewpoint, and vantage point to organize the modeling process in systems requirements definition. According to Ross and Schoman, a viewpoint "makes clear what aspects are considered relevant to achieving ... the overall purpose [of the model]" and determines How do we look at [a subject being modelled]? As examples of viewpoints, the paper offers: Technical, Operational and Economic viewpoints. In 1992, Anthony Finkelstein and others published a very important paper on viewpoints. In that work: "A viewpoint can be thought of as a combination of the idea of an “actor”, “knowledge source”, “role” or “agent” in the development process and the idea of a “view” or “perspective” which an actor maintains." An important idea in this paper was to distinguish "a representation style, the scheme and notation by which the viewpoint expresses what it can see" and "a specification, the statements expressed in the viewpoint's style describing particular domains". Subsequent work, such as IEEE 1471, preserved this distinction by utilizing two separate terms: viewpoint and view, respectively. Since the early 1990s there have been a number of efforts to codify approaches for describing and analyzing system architectures. These are often termed architecture frameworks or sometimes viewpoint sets. Many of these have been funded by the United States Department of Defense, but some have sprung from international or national efforts in ISO or the IEEE. Among these, the IEEE Recommended Practice for Architectural Description of Software-Intensive Systems (IEEE Std 1471-2000) established useful definitions of view, viewpoint, stakeholder and concern and guidelines for documenting a system architecture through the use of multiple views by applying viewpoints to address stakeholder concerns. The advantage of multiple views is that hidden requirements and stakeholder disagreements can be discovered more readily. However, studies show that in practice, the added complexity of reconciling multiple views can undermine this advantage. IEEE 1471 (now ISO/IEC/IEEE 42010:2011, Systems and software engineering — Architecture description) prescribes the contents of architecture descriptions and describes their creation and use under a number of scenarios, including precedented and unprecedented design, evolutionary design, and capture of design of existing systems. In all of these scenarios the overall process is the same: identify stakeholders, elicit concerns, identify a set of viewpoints to be used, and then apply these viewpoint specifications to develop the set of views relevant to the system of interest. Rather than define a particular set of viewpoints, the standard provides uniform mechanisms and requirements for architects and organizations to define their own viewpoints. In 1996 the ISO Reference Model for Open Distributed Processing (RM-ODP) was published to provide a useful framework for describing the architecture and design of large-scale distributed systems. == View model topics == === View === A view of a system is a representation of the system from the perspective of a viewpoint. This viewpoint on a system involves a perspective focusing on specific concerns regarding the system, which suppresses details to provide a simplified model having only those elements related to the concerns of the viewpoint. For example, a security viewpoint focuses on security concerns and a security viewpoint model contains those elements that are related to security from a more general model of a system. A view allows a user to examine a portion of a particular interest area. For example, an Information View may present all functions, organizations, technology, etc. that use a particular piece of information, while the Organizational View may present all functions, technology, and information of concern to a particular organization. In the Zachman Framework views comprise a group of work products whose development requires a particular analytical and technical expertise because they focus on either the “what,” “how,” “who,” “where,” “when,” or “why” of the enterprise. For example, Functional View work products answer the question “how is the mission carried out?” They are most easily developed by experts in functional decomposition using process and activity modeling. They show the enterprise from the point of view of functions. They also may show organizational and information components, but only as they relate to functions. === Viewpoints === In systems engineering, a viewpoint is a partitioning or restriction of concerns in a system. Adoption of a viewpoint is usable so that issues in those aspects can be addressed separately. A good selection of viewpoints also partitions the design of the system into specific areas of expertise. Viewpoints provide the conventions, rules, and languages for constructing, presenting and analysing views. In ISO/IEC 42010:2007 (IEEE-Std-1471-2000) a viewpoint is a specification for an individual view. A view is a representation of a whole system from the perspective of a viewpoint. A view may consist of one or more architectural models. Each such architectural model is developed using the methods established by its associated architectural system, as well as for the system as a whole. === Modeling perspectives === Modeling perspectives is a set of different ways to represent pre-selected aspects of a system. Each perspective has a different focus, conceptualization, dedication and visualization of what the model is representing. In information systems, the traditional way to divide modeling perspectives is to distinguish the structural, functional and behavioral/processual perspectives. This together with rule, object, communication and actor and role perspectives is one way of classifying modeling approaches === Viewpoint model === In any given viewpoint, it is possible to make a model of the system that contains only the objects that are visible from that viewpoint, but also captures all of the objects, relationships and constraints that are present in the system and relevant to that viewpoint. Such a model is said to be a viewpoint model, or a view of the
Read more →
Microsoft Azure

Microsoft Azure, sometimes stylized Azure, and formerly Windows Azure, is the cloud computing platform developed by Microsoft. It offers management, access and development of applications and services to individuals, companies, and governments through its global infrastructure. Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. Azure was first introduced at the Professional Developers Conference (PDC) in October 2008 under the codename "Project Red Dog". It was officially launched as Windows Azure in February 2010 and later renamed to Microsoft Azure on March 25, 2014. == Services == Microsoft Azure uses large-scale virtualization at Microsoft data centers worldwide and offers more than 600 services. Microsoft Azure offers a service level agreement (SLA) that guarantees 99.9% availability for applications and data hosted on its platform, subject to specific terms and conditions outlined in the SLA documentation. === Computer services === Virtual machines, infrastructure as a service (IaaS), allowing users to launch general-purpose Microsoft Windows and Linux virtual machines, software as a service (SaaS), as well as preconfigured machine images for popular software packages. Starting in 2022, these virtual machines are now powered by Ampere Cloud-native processors. Most users run Linux on Azure, some of the many Linux distributions offered, including Microsoft's own Linux-based Azure Sphere. App services, platform as a service (PaaS) environment, letting developers easily publish and manage websites. Azure Web Sites allows developers to build sites using ASP.NET, PHP, Node.js, Java, or Python, which can be deployed using FTP, Git, Mercurial, Azure DevOps, or uploaded through the user portal. This feature was announced in preview form in June 2012 at the Meet Microsoft Azure event. Customers can create websites in PHP, ASP.NET, Node.js, or Python, or select from several open-source applications from a gallery to deploy. This comprises one aspect of the platform as a service (PaaS) offerings for the Microsoft Azure Platform. It was renamed Web Apps in April 2015. Web Jobs are applications that can be deployed to an App Service environment to implement background processing that can be invoked on a schedule, on-demand, or run continuously. The Blob, Table, and Queue services can be used to communicate between Web Apps and Web Jobs and to provide state. Azure Kubernetes Service (AKS) provides the capability to deploy production-ready Kubernetes clusters in Azure. In July 2023, watermarking support on Azure Virtual Desktop was announced as an optional feature of Screen Capture to provide additional security against data leakage. === Identity === Entra ID connect is used to synchronize on-premises directories and enable SSO (Single Sign On). Entra ID B2C allows the use of consumer identity and access management in the cloud. Entra Domain Services is used to join Azure virtual machines to a domain without domain controllers. Azure information protection can be used to protect sensitive information. Entra ID External Identities is a set of capabilities that allow organizations to collaborate with external users, including customers and partners. On July 11, 2023, Microsoft announced the renaming of Azure AD to Microsoft Entra ID. The name change took place four days later. === Mobile services === Mobile Engagement collects real-time analytics that highlight users' behavior. It also provides push notifications to mobile devices. HockeyApp can be used to develop, distribute, and beta-test mobile apps. === Storage services === Storage Services provides REST and SDK APIs for storing and accessing data on the cloud. Table Service lets programs store structured text in partitioned collections of entities that are accessed by the partition key and primary key. Azure Table Service is a NoSQL non-relational database. Blob Service allows programs to store unstructured text and binary data as object storage blobs that can be accessed by an HTTP(S) path. Blob service also provides security mechanisms to control access to data. Queue Service lets programs communicate asynchronously by message using queues. File Service allows storing and access of data on the cloud using the REST APIs or the SMB protocol. === Communication services === Azure Communication Services offers an SDK for creating web and mobile communications applications that include SMS, video calling, VOIP and PSTN calling, and web-based chat. === Data management === Azure Data Explorer provides big data analytics and data-exploration capabilities. Azure Search provides text search and a subset of OData's structured filters using REST or SDK APIs. Cosmos DB is a NoSQL database service that implements a subset of the SQL SELECT statement on JSON documents. Azure Cache for Redis is a managed implementation of Redis. StorSimple manages storage tasks between on-premises devices and cloud storage. Azure SQL Database works to create, scale, and extend applications into the cloud using Microsoft SQL Server technology. It also integrates with Active Directory, Microsoft System Center, and Hadoop. Azure Synapse Analytics is a fully managed cloud data warehouse. Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. Azure Data Lake is a scalable data storage and analytic service for big data analytics workloads that require developers to run massively parallel queries. Azure HDInsight is a big data-relevant service that deploys Hortonworks Hadoop on Microsoft Azure and supports the creation of Hadoop clusters using Linux with Ubuntu. Azure Stream Analytics is a Serverless scalable event-processing engine that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, websites, social media, and other applications. === Messaging === The Microsoft Azure Service Bus allows applications running on Azure premises or off-premises devices to communicate with Azure. This helps to build scalable and reliable applications in a service-oriented architecture (SOA). The Azure service bus supports four different types of communication mechanisms: Event Hubs, which provides event and telemetry ingress to the cloud at a massive scale, with low latency and high reliability. For example, an event hub can be used to track data from cell phones such as coordinating with a GPS in real time. Queues, which allows one-directional communication. A sender application would send the message to the service bus queue and a receiver would read from the queue. Though there can be multiple readers for the queue, only one would process a single message. Topics, which provides one-directional communication using a subscriber pattern. It is similar to a queue; however, each subscriber will receive a copy of the message sent to a Topic. Optionally, the subscriber can filter out messages based on specific criteria defined by the subscriber. Relays, which provides bi-directional communication. Unlike queues and topics, a relay does not store in-flight messages in its memory; instead, it just passes them on to the destination application. === Media services === A PaaS offering that can be used for encoding, content protection, streaming, or analytics. === CDN === Azure has a worldwide content delivery network (CDN) designed to efficiently deliver audio, video, applications, images, and other static files. It improves the performance of websites by caching static files closer to users, based on their geographic location. Users can manage the network using a REST-based HTTP API. Azure has 118 point-of-presence locations across 100 cities worldwide (also known as Edge locations) as of January 2023. === Developer === Application Insights Azure DevOps === Management === With Azure Automation, users can easily automate repetitive and time-consuming tasks, often prone to cloud or enterprise setting errors. They can accomplish it using runbooks or desired state configurations for process automation. Microsoft SMA === Azure AI === Microsoft Azure Machine Learning (Azure ML) provides tools and frameworks for developers to create their own machine learning and artificial intelligence (AI) services. Azure AI Services by Microsoft comprises prebuilt APIs, SDKs, and services developers can customize. These services encompass perceptual and cognitive intelligence features such as speech recognition, speaker recognition, neural speech synthesis, face recognition, computer vision, OCR/form understanding, natural language processing, machine translation, and business decision services. Many AI characteristics in Microsoft's products and services, namely Bing, Office, Teams, Xbox, and Windows, are driven by Azure AI Services. Microsoft Foundry (formerly known as Azure AI Studio)
Read more →
Level-set method

The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes. LSM can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. LSM makes it easier to perform computations on shapes with sharp corners and shapes that change topology (such as by splitting in two or developing holes). These characteristics make LSM effective for modeling objects that vary in time, such as an airbag inflating or a drop of oil floating in water. == Overview == The figure on the right illustrates several ideas about LSM. In the upper left corner is a bounded region with a well-behaved boundary. Below it, the red surface is the graph of a level set function φ {\displaystyle \varphi } determining this shape, and the flat blue region represents the X-Y plane. The boundary of the shape is then the zero-level set of φ {\displaystyle \varphi } , while the shape itself is the set of points in the plane for which φ {\displaystyle \varphi } is positive (interior of the shape) or zero (at the boundary). In the top row, the shape's topology changes as it is split in two. It is challenging to describe this transformation numerically by parameterizing the boundary of the shape and following its evolution. An algorithm can be used to detect the moment the shape splits in two and then construct parameterizations for the two newly obtained curves. On the bottom row, however, the plane at which the level set function is sampled is translated upwards, on which the shape's change in topology is described. It is less challenging to work with a shape through its level-set function rather than with itself directly, in which a method would need to consider all the possible deformations the shape might undergo. Thus, in two dimensions, the level-set method amounts to representing a closed curve Γ {\displaystyle \Gamma } (such as the shape boundary in our example) using an auxiliary function φ {\displaystyle \varphi } , called the level-set function. The curve Γ {\displaystyle \Gamma } is represented as the zero-level set of φ {\displaystyle \varphi } by Γ = { ( x , y ) ∣ φ ( x , y ) = 0 } , {\displaystyle \Gamma =\{(x,y)\mid \varphi (x,y)=0\},} and the level-set method manipulates Γ {\displaystyle \Gamma } implicitly through the function φ {\displaystyle \varphi } . This function φ {\displaystyle \varphi } is assumed to take positive values inside the region delimited by the curve Γ {\displaystyle \Gamma } and negative values outside. == The level-set equation == If the curve Γ {\displaystyle \Gamma } moves in the normal direction with a speed v {\displaystyle v} , then by chain rule and implicit differentiation, it can be determined that the level-set function φ {\displaystyle \varphi } satisfies the level-set equation ∂ φ ∂ t = v | ∇ φ | . {\displaystyle {\frac {\partial \varphi }{\partial t}}=v|\nabla \varphi |.} Here, | ⋅ | {\displaystyle |\cdot |} is the Euclidean norm (denoted customarily by single bars in partial differential equations), and t {\displaystyle t} is time. This is a partial differential equation, in particular a Hamilton–Jacobi equation, and can be solved numerically, for example, by using finite differences on a Cartesian grid. However, the numerical solution of the level set equation may require advanced techniques. Simple finite difference methods fail quickly. Upwinding methods such as the Godunov method are considered better; however, the level set method does not guarantee preservation of the volume and shape of the set level in an advection field that maintains shape and size, for example, a uniform or rotational velocity field. Instead, the shape of the level set may become distorted, and the level set may disappear over a few time steps. Therefore, high-order finite difference schemes, such as high-order essentially non-oscillatory (ENO) schemes, are often required, and even then, the feasibility of long-term simulations is questionable. More advanced methods have been developed to overcome this; for example, combinations of the leveling method with tracking marker particles suggested by the velocity field. == Example == Consider a unit circle in R 2 {\textstyle \mathbb {R} ^{2}} , shrinking in on itself at a constant rate, i.e. each point on the boundary of the circle moves along its inwards pointing normally at some fixed speed. The circle will shrink and eventually collapse down to a point. If an initial distance field is constructed (i.e. a function whose value is the signed Euclidean distance to the boundary, positive interior, negative exterior) on the initial circle, the normalized gradient of this field will be the circle normal. If the field has a constant value subtracted from it in time, the zero level (which was the initial boundary) of the new fields will also be circular and will similarly collapse to a point. This is due to this being effectively the temporal integration of the Eikonal equation with a fixed front velocity. == Applications == In mathematical modeling of combustion, LSM is used to describe the instantaneous flame surface, known as the G equation. Level-set data structures have been developed to facilitate the use of the level-set method in computer applications. Computational fluid dynamics Trajectory planning Optimization Image processing Computational biophysics Discrete complex dynamics (visualization of the parameter plane and the dynamic plane) == History == The level-set method was developed in 1979 by Alain Dervieux, and subsequently popularized by Stanley Osher and James Sethian. It has since become popular in many disciplines, such as image processing, computer graphics, computational geometry, optimization, computational fluid dynamics, and computational biology.
Read more →
Mobile simulator

A mobile simulator is a software application for a personal computer which creates a virtual machine version of a mobile device, such as a mobile phone, iPhone, other smartphone, or calculator, on the computer. This may sometimes also be termed an emulator. The mobile simulator allows the user to use features and run applications on the virtual mobile on their computer as though it was the actual mobile device. A mobile simulator lets you test a website and determine how well it performs on various types of mobile devices. A good simulator tests mobile content quickly on multiple browsers and emulates several device profiles simultaneously. This allows analysis of mobile content in real-time, locate errors in code, view rendering in an environment that simulates the mobile browser, and optimize the site for performance. Mobile simulators may be developed using programming languages such as Java, .NET and JavaScript.
Read more →
LiveChat

LiveChat is an AI customer service software with chatbot, online chat, help desk software, and web analytics capabilities. LiveChat is used by over 76,000 companies. It was first launched in 2002 and is offered via a SaaS (software as a service) business model by Text. Organizations use LiveChat as a single point of contact to manage customer service and online sales activities with a single program. == Product == LiveChat is proprietary software. LiveChat's website chat widget can be embedded on customers' websites as a small chat box, often displayed in the bottom right corner of the web browser. It can be used to conduct chats, share files and save transcripts. The agent application is used by company employees to respond to questions asked by the customers. This is available through both web-based application, desktop applications, and mobile apps. Web chat sessions can be initiated by the visiting customer, or by the agent, either manually or automatically by the LiveChat system when the visitor meets the predefined criteria (i.e. searched keyword, time on website, encountered error, etc.). LiveChat's system attempts to identify the best prospects visiting a website based on data gathered from past purchasing decisions. Other features include real-time website traffic monitoring, built-in ticketing system and agents' efficiency analytics. LiveChat is available in 48 languages. == Research and reception == Reviewing LiveChat's usefulness for online learning in 2020, psychologist Jaclyn Broadbent said "LiveChat occurs as a real-time conversation, it can be time-consuming for staff and disruptive to other tasks." However, using it has resulted in reduced communication traffic from other channels, such as the discussion boards or email. As a teacher, the best time to be available on LiveChat is when you are doing other administrative jobs." Since 2014 LiveChat has been publishing Customer Service Report - an annual study of customer satisfaction and analysis of online business communication trends. It includes research of thousands of companies and millions of customer service email and live support interactions.
Read more →
Microsoft Azure

Microsoft Azure, sometimes stylized Azure, and formerly Windows Azure, is the cloud computing platform developed by Microsoft. It offers management, access and development of applications and services to individuals, companies, and governments through its global infrastructure. Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems. Azure was first introduced at the Professional Developers Conference (PDC) in October 2008 under the codename "Project Red Dog". It was officially launched as Windows Azure in February 2010 and later renamed to Microsoft Azure on March 25, 2014. == Services == Microsoft Azure uses large-scale virtualization at Microsoft data centers worldwide and offers more than 600 services. Microsoft Azure offers a service level agreement (SLA) that guarantees 99.9% availability for applications and data hosted on its platform, subject to specific terms and conditions outlined in the SLA documentation. === Computer services === Virtual machines, infrastructure as a service (IaaS), allowing users to launch general-purpose Microsoft Windows and Linux virtual machines, software as a service (SaaS), as well as preconfigured machine images for popular software packages. Starting in 2022, these virtual machines are now powered by Ampere Cloud-native processors. Most users run Linux on Azure, some of the many Linux distributions offered, including Microsoft's own Linux-based Azure Sphere. App services, platform as a service (PaaS) environment, letting developers easily publish and manage websites. Azure Web Sites allows developers to build sites using ASP.NET, PHP, Node.js, Java, or Python, which can be deployed using FTP, Git, Mercurial, Azure DevOps, or uploaded through the user portal. This feature was announced in preview form in June 2012 at the Meet Microsoft Azure event. Customers can create websites in PHP, ASP.NET, Node.js, or Python, or select from several open-source applications from a gallery to deploy. This comprises one aspect of the platform as a service (PaaS) offerings for the Microsoft Azure Platform. It was renamed Web Apps in April 2015. Web Jobs are applications that can be deployed to an App Service environment to implement background processing that can be invoked on a schedule, on-demand, or run continuously. The Blob, Table, and Queue services can be used to communicate between Web Apps and Web Jobs and to provide state. Azure Kubernetes Service (AKS) provides the capability to deploy production-ready Kubernetes clusters in Azure. In July 2023, watermarking support on Azure Virtual Desktop was announced as an optional feature of Screen Capture to provide additional security against data leakage. === Identity === Entra ID connect is used to synchronize on-premises directories and enable SSO (Single Sign On). Entra ID B2C allows the use of consumer identity and access management in the cloud. Entra Domain Services is used to join Azure virtual machines to a domain without domain controllers. Azure information protection can be used to protect sensitive information. Entra ID External Identities is a set of capabilities that allow organizations to collaborate with external users, including customers and partners. On July 11, 2023, Microsoft announced the renaming of Azure AD to Microsoft Entra ID. The name change took place four days later. === Mobile services === Mobile Engagement collects real-time analytics that highlight users' behavior. It also provides push notifications to mobile devices. HockeyApp can be used to develop, distribute, and beta-test mobile apps. === Storage services === Storage Services provides REST and SDK APIs for storing and accessing data on the cloud. Table Service lets programs store structured text in partitioned collections of entities that are accessed by the partition key and primary key. Azure Table Service is a NoSQL non-relational database. Blob Service allows programs to store unstructured text and binary data as object storage blobs that can be accessed by an HTTP(S) path. Blob service also provides security mechanisms to control access to data. Queue Service lets programs communicate asynchronously by message using queues. File Service allows storing and access of data on the cloud using the REST APIs or the SMB protocol. === Communication services === Azure Communication Services offers an SDK for creating web and mobile communications applications that include SMS, video calling, VOIP and PSTN calling, and web-based chat. === Data management === Azure Data Explorer provides big data analytics and data-exploration capabilities. Azure Search provides text search and a subset of OData's structured filters using REST or SDK APIs. Cosmos DB is a NoSQL database service that implements a subset of the SQL SELECT statement on JSON documents. Azure Cache for Redis is a managed implementation of Redis. StorSimple manages storage tasks between on-premises devices and cloud storage. Azure SQL Database works to create, scale, and extend applications into the cloud using Microsoft SQL Server technology. It also integrates with Active Directory, Microsoft System Center, and Hadoop. Azure Synapse Analytics is a fully managed cloud data warehouse. Azure Data Factory is a data integration service that allows creation of data-driven workflows in the cloud for orchestrating and automating data movement and data transformation. Azure Data Lake is a scalable data storage and analytic service for big data analytics workloads that require developers to run massively parallel queries. Azure HDInsight is a big data-relevant service that deploys Hortonworks Hadoop on Microsoft Azure and supports the creation of Hadoop clusters using Linux with Ubuntu. Azure Stream Analytics is a Serverless scalable event-processing engine that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, websites, social media, and other applications. === Messaging === The Microsoft Azure Service Bus allows applications running on Azure premises or off-premises devices to communicate with Azure. This helps to build scalable and reliable applications in a service-oriented architecture (SOA). The Azure service bus supports four different types of communication mechanisms: Event Hubs, which provides event and telemetry ingress to the cloud at a massive scale, with low latency and high reliability. For example, an event hub can be used to track data from cell phones such as coordinating with a GPS in real time. Queues, which allows one-directional communication. A sender application would send the message to the service bus queue and a receiver would read from the queue. Though there can be multiple readers for the queue, only one would process a single message. Topics, which provides one-directional communication using a subscriber pattern. It is similar to a queue; however, each subscriber will receive a copy of the message sent to a Topic. Optionally, the subscriber can filter out messages based on specific criteria defined by the subscriber. Relays, which provides bi-directional communication. Unlike queues and topics, a relay does not store in-flight messages in its memory; instead, it just passes them on to the destination application. === Media services === A PaaS offering that can be used for encoding, content protection, streaming, or analytics. === CDN === Azure has a worldwide content delivery network (CDN) designed to efficiently deliver audio, video, applications, images, and other static files. It improves the performance of websites by caching static files closer to users, based on their geographic location. Users can manage the network using a REST-based HTTP API. Azure has 118 point-of-presence locations across 100 cities worldwide (also known as Edge locations) as of January 2023. === Developer === Application Insights Azure DevOps === Management === With Azure Automation, users can easily automate repetitive and time-consuming tasks, often prone to cloud or enterprise setting errors. They can accomplish it using runbooks or desired state configurations for process automation. Microsoft SMA === Azure AI === Microsoft Azure Machine Learning (Azure ML) provides tools and frameworks for developers to create their own machine learning and artificial intelligence (AI) services. Azure AI Services by Microsoft comprises prebuilt APIs, SDKs, and services developers can customize. These services encompass perceptual and cognitive intelligence features such as speech recognition, speaker recognition, neural speech synthesis, face recognition, computer vision, OCR/form understanding, natural language processing, machine translation, and business decision services. Many AI characteristics in Microsoft's products and services, namely Bing, Office, Teams, Xbox, and Windows, are driven by Azure AI Services. Microsoft Foundry (formerly known as Azure AI Studio)
Read more →
Verbot

The Verbot (short for Verbal-Robot) was a chatbot program and artificial intelligence software development kit (SDK) designed for Windows and web platforms. == Early beginning == The origin of verbot traces back to Michael Mauldin's research during his time as a graduate student and post-doctoral fellow at Carnegie Mellon University. The creative foundation also stems from Peter Plantec's work in personality psychology and art direction. === Historic outline === In 1994, Michael Loren Mauldin, founder of Lycos, Inc., developed a prototype chatbot, Julia, which competed in the internationally known Turing test, for the coveted Loebner Prize. The Turing test matches computer scientist judges against machines to see if they can distinguish a computer from a real human. Julia was refined and developed, and in 1997, Dr. Mauldin and Peter Plantec, a clinical psychologist and animator, formed Virtual Personalities, Inc. (now Conversive, Inc.) in order to create a virtual human interface that would incorporate real-time animation as well as speech and natural language processing. The initial release, a stand-alone virtual person called Sylvie, was beta-tested to the public. This release was well received, and finally, after several versions, the production release (deemed version 3) of the Verbally Enhanced Software Robot, or Verbot, was deployed in fall 2000. The grandfather of all Verbots is Rog-O-Matic, which, although it could not talk, could and did explore a virtual world. Julia has been active on the internet in one form or another since 1989. A close cousin of Julia is Lycos, a robot that explores the World Wide Web and answers questions about it. Sylvie was the first Verbot with a face and a voice. Sylvie was the first Virtual Human with advanced, flexible interfacing capability. === Beginnings === The Virtual Personalities story goes back to 1978, where Mauldin was attending Rice University. Fascinated by the idea of ELIZA, he proceeded to write a program called "PET" for his 8 kilobyte Commodore PET Computer. PET included simple induction as a way to post new information, for example: Subject: I like my friend (later) Subject: I like food. PET: I have heard that food is your friend. Meanwhile, Plantec was separately designing a personality for "Entity", a theoretical virtual human that would interact comfortably with humans without pretending to be one. At that time the technology was not advanced enough to realize Entity. Mauldin got so involved with this that he majored in Computer Science and minored in Linguistics. === Rogue === In the late seventies and early eighties, a popular computer game at universities was Rogue, an implementation of Dungeons and Dragons where the player would descend 26 levels in a randomly created dungeon, fighting monsters, gathering treasure, and searching for the elusive "Amulet of Yendor". Mauldin was one of four grad students who devoted a large amount of time to building a program called "Rog-O-Matic" capable of retrieving the amulet and emerging victorious from the dungeon. === TinyMUD === In 1989, when James Aspnes at Carnegie Mellon created the first TinyMUD (a descendant of MUD and AberMUD), Mauldin was one of the first to create a computer player that would explore the text-based world of TinyMUD. But his first robot, Gloria, gradually accreted more and more linguistic ability, to the point that it could pass the "unsuspecting" Turing test. In this version of the test, the human has no reason to suspect that one of the other occupants of the room is controlled by a computer, and so is more polite and asks fewer probing questions. The second generation of Mauldin's TinyMUD robots was Julia, created on Jan. 8, 1990. Julia slowly developed into a more and more capable conversational agent, and assumed useful duties in the TinyMUD world, including tour guide, information assistant, note-taker, and message-relayer. She could even play the card game hearts along with the other human players. In 1991, Julia attended the first Loebner Prize contest in Boston, Massachusetts. Although she only finished third, she was ranked by one judge as more human than one of the human confederates, winning a coveted certificate of humanness in the world's first restricted Turing test. Julia continued to log in to various TinyMUD's and TinyMucks for the next seven years, and chatted with hundreds of people a month over the internet. === Lycos === Julia's job was to explore a virtual world consisting of pages of textual descriptions, with links between them, and to construct an internal map of that world and answer questions about it (including path information such as the shortest route from one room to another, and matching information, such as which rooms contained a certain kind of object or textual description). It was therefore only a very short cognitive leap from Julia to Lycos, another robotic agent that explores a virtual world made of hyperlinked pages of text, and which answers questions about those pages. Sylvie was born and her abilities were expanded greatly to include interfacing with computers and control systems via her serial ports. === Sylvie === Sylvie was the first intelligent animated virtual human. She was designed both as a conversation agent and as a virtual human interface that would form a bridge between the two. She became more popular as a conversation agent, but her designers believe she serves as a prototype for future virtual human interface design that will help us all cope with the increasing complexity of technology. As an aside, Plantec noticed that a large number of Sylvies have been sold in Southeast Asia. Upon investigation, he found out that students had discovered a "test" mode that would allow them to type in English sentences that Sylvie would pronounce in her somewhat stylized English. == Ownership == In 1997, Dr. Mauldin and Peter Plantec formed Virtual Personalities, Inc. to create Natural Language Processing solutions for companies. In 2001 Virtual Personalities, Inc. became Conversive, Inc. to reflect the focus on providing Customer Service and Marketing to the Enterprise Market. In late 2012 Avaya, Inc. acquired Conversive's assets including Verbots. == Verbot versions == The Verbot 4 version was created and released in 2004. In 2005 Version 4.1 of the Verbot Software was released with many feature enhancements and bug fixes, including built-in support for embedding C# code in outputs and conditionals. In early 2006 Conversive launched Verbots Online allowing Verbot 4 users to upload their knowledge and show off their bots to the world. In 2009 Version 5 was released, completely free and fully featured. In early 2012 the last version of Verbot, 5.0.1.2, was released to the general public with support for Windows 7. Later in 2012 Verbots Online completely shut down. == Verbots today == Verbots.com, its community of users, and its forums no longer exist, but the software and users can still be found. There has been no active development since the early 2012 release of Verbot 5.0.1.2.
Read more →
Zhura

Zhura ( ZUR-ə) is a free, web-based screenwriting software application for writing and formatting screenplays to the film industry standard, as well as other formats. Zhura allows users to collaborate on scripts in public or private groups and uses Creative Commons Licensing for all work in the public workspace. On March 29, 2010, Zhura announced its merger with Scripped. Scripped's CEO, Sunil Rajaraman, remains the company's Chief Executive Officer (CEO) as of 2022. The Zhura CEO was Eric MacDonald, a former Cascade Communications engineer. Scripped later closed on April 1, 2015 after a catastrophic, irrecoverable data loss. == Script editor == Screenplay Template – The script editor provides a built-in screenplay template which formats the document to a standard for scripts as recommended by the AMPAS. The screenplay document is composed of seven elements: scene, action, character, dialogue, parenthetical, transition, and shot (see image). Each element has a specific style to which the script editor conforms as you type.Script Formats – Other major script formats for stage play, sitcom, audio drama and comic book are also supported as well as the ability to switch between them.Auto-Complete – Characters, scene headings and custom transitions are “remembered” as they are written and “recalled” with tab-completion when a writer starts a new character, scene heading or transition, respectively.Multiple Editors – With a collaborative editing model comparable to Google Docs, two or more users can edit the same script simultaneously, regardless of having a different operating system or web browser. Import/Export – A screenplay written in another program can be imported into the script editor and automatically conformed to the screenplay template. The closer the original script has adhered to the standard format, the better it will appear when imported. Supported import/export formats include Text (.txt) Word (.doc) Rich Text (.rtf) and OpenDocument (.odt). Scripts can also be exported as a PDF file with additional options.Tracking Changes – Similar to the “tracking” feature in Microsoft Word, a user can review all changes made to a script in the revision history as well as highlight the contributions of each writer. Offline Mode – The Google Gears-based offline functionality is in the process of being updated and is not available for new subscribers, according to the company founders. == Community == Scripped supports typical social networking features such as discussion boards, comments, user profiles, public and private writing groups, internal web mail and instant messaging within the script editor. There is also the option to share scripts with others outside of Scripped by making scripts externally viewable. Scripped is made up entirely of user-generated scripts that other users can share, critique and edit, offering creative support to a community of writers. == Licensing of user-created work == There are three types of work-spaces on Scripped (personal, group and public) with unique copyright and licensing management for the work created in each area. Any work a user originates may be moved from the personal area to a public or group area at any time. Once another user edits a script, however, it cannot be moved into the originator’s personal area. Personal Workspace – Any script created or video uploaded in the user’s personal workspace remains copyrighted to that user. Until the user moves that script or video from their personal area into a group or public area, no other user shares a copyright or license to that work. Private Group Workspace – The copyright to any script created or video uploaded in a private group workspace is allocated by the individual members of the group, however they see fit. Public Workspace – Any script created or video uploaded in the public workspace is assigned a Creative Commons license by the originator of that work. The originator of a script may select one of four Creative Commons licenses before introducing that script to the public. The selection of the license is determined by what the author wants to allow others to do with the work. Below is a list of Creative Commons licenses available for all scripts and videos in the public workspace. Share Alike (BY-SA) This license lets others remix, tweak, and build upon your work even for commercial reasons, as long as they credit the original user and license their new creations under the identical terms. This license is often compared to open source software licenses. All new works based on the original user's will carry the same license, so any derivatives will also allow commercial use. No Derivatives (BY-ND) This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the original user. Non-Commercial, No Derivatives (BY-NC-ND) This license is the most restrictive of the four licenses, allowing redistribution. This license is often called the "free advertising" license because it allows others to download the original user work and share them with others as long as they mention the original user and link back to them, but they can't change them in any way or use them commercially. Non-Commercial, Share Alike (BY-NC-SA) This license lets others remix, tweak, and build upon the original user's work non-commercially, as long as they credit the original user and license their new creations under the identical terms. Others can download and redistribute the original user's work just like the BY-NC-ND license, but they can also translate, make remixes, and produce new stories based on the original user's work. All new work based on the original user's work will carry the same license, so any derivatives will also be non-commercial in nature. == Events == In April 2008, Zhura partnered with Improv Asylum, a comedy troupe in Boston, Massachusetts to produce a live sketch comedy show called "You Wrote It, Live" entirely written by the public on Zhura. Another show was produced in June.
Read more →
Teamwork (project management)

Teamwork.com is an Irish, privately owned, web-based software company headquartered in Cork, Ireland. Teamwork creates task management and team collaboration software. Founded in 2007, as of 2016 the company stated that its software was in use by over 370,000 organisations worldwide (including Disney, Spotify and HP), and that it had over 2.4m users. == History == Peter Coppinger and Dan Mackey founded a company, Digital Crew, in 2007. This company built websites, intranets and custom web-based solutions for clients in Cork, Ireland. Frustrated by whiteboards and software management tools, Coppinger wanted a software system that would help manage client projects and which would be easy to use and generic enough to be used by different types of companies. Originally 37signals Basecamp users themselves, Coppinger and Mackey were frustrated by the limited feature set, and by Basecamp's apparent inaction on their feedback. In October 2007, Coppinger and Mackey launched Teamwork Project Manager, nicknamed TeamworkPM. In March 2015, this was renamed as Teamwork Projects. In 2014, after two years of negotiations, TeamworkPM bought the domain name 'Teamwork.com' for US$675,000 (€500,000). At the time this was one of the most expensive domain name purchases by an Irish company, and involved the transfer of a domain name which had been dormant since it was first acquired by the original owner in 1999. In 2015, Teamwork.com was named by Gartner to be one of their "Cool Vendors" in the Program and Portfolio Management Category. This was followed by the launch of a new real-time messaging product, Teamwork Chat, in January 2015. In June 2015, the company announced a drive to recruit for 40 positions by the end of the year. This was followed by the announcement that the company was investing more than €1 million in a new office, and had leased office space in Park House, Blackpool. In June 2016, Teamwork.com undertook a further recruitment drive to entice developers to Cork. In July 2021, the company announced that it had raised an investment of $70 million (€59.1 million) from venture capital firm Bregal Milestone to fund further growth. == Products == Teamwork markets a number of cloud-based applications, including Teamwork, Teamwork Desk, Teamwork Spaces, Teamwork CRM and Teamwork Chat. Teamwork was launched on 4 October 2007, at which time it had time management, milestone management, file sharing, time tracking, and messaging features. Teamwork's platform reportedly integrates with martech software like HubSpot, as well as other productivity tools like Slack, G Suite, MS Teams, Zapier, Dropbox and QuickBooks. == Awards == In 2016, Teamwork was awarded Cork's Best SME in the Cork Chamber of Commerce "Company of the Year" awards. In 2016, Teamwork was named number 7 in Deloitte's Fast 50 tech companies hit €1.6bn turnover. In 2015, Teamwork was identified as a Gartner "Cool Vendor" in the Program and Portfolio Management Category.
Read more →