WIPO GREEN is a World Intellectual Property Organization program established in 2013 that supports global efforts to address climate change and food security through sharing of sustainable technology innovations. == WIPO GREEN database == The WIPO GREEN database is the foundation of the platform. The database is a free, solutions-oriented, global innovation catalog that connects needs for solving environmental or climate change problems with sustainable solutions from prototypes to marketable products available for sale, license, collaborations, knowledge transfer, joint ventures, or collaborations. Green technology innovators can promote their products, businesses, organizations, and governments looking for green technologies can explain their needs and seek collaboration with providers. As of July 2022, WIPO GREEN has over 120,000 technologies, needs and experts, more than 2000 users in 110 countries, and has recorded over 1000 connections made between technology providers and seekers. The database utilizes AI-assisted auto-matching, user uploads tracing and alerts, full-text search for solutions based on long need descriptions, and the Patent2Solution search function for finding commercial applications of a patent, which are some of the unique features of the database. Free registration is required for detailed record view and uploading. All technologies uploaded to the WIPO GREEN database remain the property of the rights holder. It is up to the rights holder and the collaborating parties to structure agreements in the manner they feel is most appropriate and effective. WIPO GREEN does not require that technologies or innovations uploaded to the database be patented or in the process of being patented. Therefore, technology providers can upload their technology while related patent applications are pending. Technology providers are encouraged to upload technology solutions on the WIPO GREEN database and connect with other users to explore partnerships, technology transfers, including funding and licensing opportunities. == Acceleration projects == Acceleration projects work with WIPO GREEN partners and local organizations to explore local challenges and green opportunities for particular environmental needs. These projects are organized annually in different countries or regions around and connect providers and seekers of green technologies. For example, the Latin America Acceleration Project explores innovative new technologies in the region and facilitates green technology exchange between providers and seekers in green opportunities in intensified crop rotation, soil re-carbonization, and forest management in Argentina; zero-till or conservation agriculture in Brazil; and wine production in Chile. In October 2021, a project in Indonesia on palm oil mill effluent (POME), a by-product of palm oil production that emits greenhouse gases and reportedly harms flora and fauna in local rivers, identified viable green solutions to turn the high organic content of POME wastewater into biogas and other environmentally friendly uses. Former projects took place in Cambodia, Indonesia, and the Philippines around wastewater treatment, agriculture, and water technologies. == The Green Technology Book == In November 2022 at UNFCCC COP27, WIPO introduced its new Flagship publication the Green Technology Book. This digital-first publication aims to put innovation, technology and intellectual property at the forefront in the fight against climate change. The inaugural edition of this annual publication focused on available solutions for climate-change adaptation to reduce vulnerability as well as to increase resilience to the impacts of climate change. The book was created in cooperation with the Climate Technology Center and Network (CTCN) and the Egyptian Academy of Scientific Research and Technology (ASTR). It features 200 adaptation technologies, which are also available in the WIPO GREEN database of innovative technologies and needs. == Partners Network == WIPO GREEN partners are public or private institutions that wish to collaborate to advance WIPO GREEN’s mission. The network is aimed at helping the implementation and diffusion of green technology innovations around the world. Partners include government institutions, intergovernmental organizations, academia, and businesses – from small and medium-sized enterprises to Fortune 500 companies. As of 2022, WIPO GREEN has a network of over 146 partner organizations involved in green technology.
Algorithm selection
Algorithm selection (sometimes also called per-instance algorithm selection or offline algorithm selection) is a meta-algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different performance characteristics. That is, while one algorithm performs well in some scenarios, it performs poorly in others and vice versa for another algorithm. If we can identify when to use which algorithm, we can optimize for each scenario and improve overall performance. This is what algorithm selection aims to do. The only prerequisite for applying algorithm selection techniques is that there exists (or that there can be constructed) a set of complementary algorithms. == Definition == Given a portfolio P {\displaystyle {\mathcal {P}}} of algorithms A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} , a set of instances i ∈ I {\displaystyle i\in {\mathcal {I}}} and a cost metric m : P × I → R {\displaystyle m:{\mathcal {P}}\times {\mathcal {I}}\to \mathbb {R} } , the algorithm selection problem consists of finding a mapping s : I → P {\displaystyle s:{\mathcal {I}}\to {\mathcal {P}}} from instances I {\displaystyle {\mathcal {I}}} to algorithms P {\displaystyle {\mathcal {P}}} such that the cost ∑ i ∈ I m ( s ( i ) , i ) {\displaystyle \sum _{i\in {\mathcal {I}}}m(s(i),i)} across all instances is optimized. == Examples == === Boolean satisfiability problem (and other hard combinatorial problems) === A well-known application of algorithm selection is the Boolean satisfiability problem. Here, the portfolio of algorithms is a set of (complementary) SAT solvers, the instances are Boolean formulas, the cost metric is for example average runtime or number of unsolved instances. So, the goal is to select a well-performing SAT solver for each individual instance. In the same way, algorithm selection can be applied to many other N P {\displaystyle {\mathcal {NP}}} -hard problems (such as mixed integer programming, CSP, AI planning, TSP, MAXSAT, QBF and answer set programming). Competition-winning systems in SAT are SATzilla, 3S and CSHC === Machine learning === In machine learning, algorithm selection is better known as meta-learning. The portfolio of algorithms consists of machine learning algorithms (e.g., Random Forest, SVM, DNN), the instances are data sets and the cost metric is for example the error rate. So, the goal is to predict which machine learning algorithm will have a small error on each data set. == Instance features == The algorithm selection problem is mainly solved with machine learning techniques. By representing the problem instances by numerical features f {\displaystyle f} , algorithm selection can be seen as a multi-class classification problem by learning a mapping f i ↦ A {\displaystyle f_{i}\mapsto {\mathcal {A}}} for a given instance i {\displaystyle i} . Instance features are numerical representations of instances. For example, we can count the number of variables, clauses, average clause length for Boolean formulas, or number of samples, features, class balance for ML data sets to get an impression about their characteristics. === Static vs. probing features === We distinguish between two kinds of features: Static features are in most cases some counts and statistics (e.g., clauses-to-variables ratio in SAT). These features ranges from very cheap features (e.g. number of variables) to very complex features (e.g., statistics about variable-clause graphs). Probing features (sometimes also called landmarking features) are computed by running some analysis of algorithm behavior on an instance (e.g., accuracy of a cheap decision tree algorithm on an ML data set, or running for a short time a stochastic local search solver on a Boolean formula). These feature often cost more than simple static features. === Feature costs === Depending on the used performance metric m {\displaystyle m} , feature computation can be associated with costs. For example, if we use running time as performance metric, we include the time to compute our instance features into the performance of an algorithm selection system. SAT solving is a concrete example, where such feature costs cannot be neglected, since instance features for CNF formulas can be either very cheap (e.g., to get the number of variables can be done in constant time for CNFs in the DIMACs format) or very expensive (e.g., graph features which can cost tens or hundreds of seconds). It is important to take the overhead of feature computation into account in practice in such scenarios; otherwise a misleading impression of the performance of the algorithm selection approach is created. For example, if the decision which algorithm to choose can be made with perfect accuracy, but the features are the running time of the portfolio algorithms, there is no benefit to the portfolio approach. This would not be obvious if feature costs were omitted. == Approaches == === Regression approach === One of the first successful algorithm selection approaches predicted the performance of each algorithm m ^ A : I → R {\displaystyle {\hat {m}}_{\mathcal {A}}:{\mathcal {I}}\to \mathbb {R} } and selected the algorithm with the best predicted performance a r g min A ∈ P m ^ A ( i ) {\displaystyle arg\min _{{\mathcal {A}}\in {\mathcal {P}}}{\hat {m}}_{\mathcal {A}}(i)} for an instance i {\displaystyle i} . === Clustering approach === A common assumption is that the given set of instances I {\displaystyle {\mathcal {I}}} can be clustered into homogeneous subsets and for each of these subsets, there is one well-performing algorithm for all instances in there. So, the training consists of identifying the homogeneous clusters via an unsupervised clustering approach and associating an algorithm with each cluster. A new instance is assigned to a cluster and the associated algorithm selected. A more modern approach is cost-sensitive hierarchical clustering using supervised learning to identify the homogeneous instance subsets. === Pairwise cost-sensitive classification approach === A common approach for multi-class classification is to learn pairwise models between every pair of classes (here algorithms) and choose the class that was predicted most often by the pairwise models. We can weight the instances of the pairwise prediction problem by the performance difference between the two algorithms. This is motivated by the fact that we care most about getting predictions with large differences correct, but the penalty for an incorrect prediction is small if there is almost no performance difference. Therefore, each instance i {\displaystyle i} for training a classification model A 1 {\displaystyle {\mathcal {A}}_{1}} vs A 2 {\displaystyle {\mathcal {A}}_{2}} is associated with a cost | m ( A 1 , i ) − m ( A 2 , i ) | {\displaystyle |m({\mathcal {A}}_{1},i)-m({\mathcal {A}}_{2},i)|} . == Requirements == The algorithm selection problem can be effectively applied under the following assumptions: The portfolio P {\displaystyle {\mathcal {P}}} of algorithms is complementary with respect to the instance set I {\displaystyle {\mathcal {I}}} , i.e., there is no single algorithm A ∈ P {\displaystyle {\mathcal {A}}\in {\mathcal {P}}} that dominates the performance of all other algorithms over I {\displaystyle {\mathcal {I}}} (see figures to the right for examples on complementary analysis). In some application, the computation of instance features is associated with a cost. For example, if the cost metric is running time, we have also to consider the time to compute the instance features. In such cases, the cost to compute features should not be larger than the performance gain through algorithm selection. == Application domains == Algorithm selection is not limited to single domains but can be applied to any kind of algorithm if the above requirements are satisfied. Application domains include: hard combinatorial problems: SAT, Mixed Integer Programming, CSP, AI Planning, TSP, MAXSAT, QBF and Answer Set Programming combinatorial auctions in machine learning, the problem is known as meta-learning software design black-box optimization multi-agent systems numerical optimization linear algebra, differential equations evolutionary algorithms vehicle routing problem power systems For an extensive list of literature about algorithm selection, we refer to a literature overview. == Variants of algorithm selection == === Online selection === Online algorithm selection refers to switching between different algorithms during the solving process. This is useful as a hyper-heuristic. In contrast, offline algorithm selection selects an algorithm for a given instance only once and before the solving process. === Computation of schedules === An extension of algorithm selection is the per-instance algorithm scheduling problem, in which we do not select only one solver, but we select a time budget for each algorithm
Hype (marketing)
Hype in marketing is a strategy of using extreme publicity. Hype as a modern marketing strategy is closely associated with social media. Marketing through hype often uses artificial scarcity to induce demand. Consumers of hyped products often participate as a form of conspicuous consumption to signify characteristics about themselves. Hype allows brands to promote their image above the actual quality of the product. Streetwear brands have collaborated with luxury fashion to justify charging premium prices for their goods. As an example, fashion label Vetements used social media channels to promote a limited-edition hoodie which sold 500 units in hours, recording sales of €445,000. When hype marketing is used to drive demand for limited-edition goods, consumers sometimes attempt resell those good on secondary markets for a profit (comparable to ticket scalping). The resale market is a $24 billion industry. == Method == Luxury brands may release products as a collaborate with ready-made garment brands as a way to build hype. Collaborations have been used by some luxury brands to circumvent fast fashion brands copying their designs. NYU Professor Adam Alter says that for an established brand to create a scarcity frenzy, they need to release a limited number of different products, frequently. Hype is often built via Pop-up retail. Comme des Garçons was one of the first to use this strategy, leasing a short-term vacant shop solved the storage problems of releasing product for quick sale. Hype campaigns also rely on influencer marketing, where brands enlist creators whose parasocial relationships with their followers help convert audience attention into demand for limited releases. == In popular culture == The term 'hypebeast' has been coined to define consumers vulnerable to hype marketing. The origins of the term come from the Hong Kong-based company Hypebeast. The behaviours of the hypebeast define hype marketing; the purchase of popular goods they can't afford to impress others. Hype also manifests itself in queues with brands often retailing hyped products through pop-up stores. Many luxury brands release hyped products via their online shop. This has led to the creation of companies that allow consumers to use bots to guarantee or improve their chances of purchasing a limited-edition product.
Far-Play
Far-Play (stylized fAR-Play, from augmented reality) was a software platform developed at the University of Alberta, for creating location-based, scavenger-hunt style games which use the GPS and web-connectivity features of a player's smartphone. According to the development team, "our long-term objective is to develop a general framework that supports the implementation of AARGs that are fun to play and also educational". It utilizes Layar, an augmented reality smartphone application, QR codes located at particular real-world sites, or a phone's web browser, to facilitate games which require players to be in close physical proximity to predefined "nodes". A node, referred to by the developers as a Virtual Point of Interest (vPOI), is a point in space defined by a set of map coordinates; fAR-Play uses the GPS function of a player's smartphone — or, for indoor games, which are not easily tracked by GPS satellites, specially-created QR codes— to confirm that they are adequately near a given node. Once a player is within a node's proximity, Layar's various augmented reality features can be utilized to display a range of extra content overlaid upon the physical play-space or launch another application for extra functionality. == Development and features == fAR-Play began development in 2008, emerging from a collaborative project undertaken by a group of University of Alberta students from the Computer Science and Humanities Computing departments. fAR-Play is still under development, but a beta version is available for testing by request. fAR-Play's development is managed by a team of interdisciplinary professors and students at the University of Alberta. Currently, the developing team's roster includes Supervising Professors Geoffrey Rockwell and Eleni Stroulia, Developers Lucio Gutierrez and Matthew Delaney, and Website Developers Calen Henry and Garry Wong. === Technology === fAR-Play relies on a number of open- and closed-source web technologies as tools to create, and enhance the users' experience. Layar is the recommended client-side frontend for delivering game content to the player; it is available on Android and iOS, which covers over 91% of smartphones. While Layar is not a requirement to play fAR-Play games, the application does supply additional augmented reality functionality; Layar also includes a built-in QR scanner. Depending on the design of the particular game, the player may instead use a dedicated QR code scanner; the developers recommend BeeTagg, but any such application will do. Layar or a QR code scanner are the maximum software requirements to play a fAR-Play game, making implementation of games on a wide variety of platforms relatively straightforward. fAR-Play games can also be designed for play strictly within a mobile phone's web browser. On the server side, fAR-Play's engine is composed of an Apache server which manages the system's web interface, including the mobile and desktop versions of the fAR-Play website, and a Java-based REST framework for managing the database of nodes. === Features === As a platform for designing AR games, as opposed to an AR game itself, fAR-Play offers little in the way of explicit shapes or patterns for games to take; instead, these elements are left to the game designer or players to develop. However, the nonspecific nature of nodes, the many options they offer for content delivery, and the open design of the platform are such that these elements can be developed extensively. Functionally, fAR-Play is a tool for tracking arbitrary points in space and a given player's proximity to them; what it does beyond that is up to the developers' and players' discretion. However, the fAR-Play website contains a leaderboard which tracks registered user's total scores. Players are assigned levels based on their total score, ranging from Novice — Super Player. Player profiles will display nodes that the player has recently caught, and any achievements the player has gained. Additionally, players can share their adventure progress, achievements, and the capture of vPOIs on Facebook. == How to play == In order to participate in the locative aspects of fAR-Play games, users must have an Android or iOS mobile device and access to wireless internet. Players can participate in fAR-Play anonymously, or create and sign into a fAR-Play account. Those who choose to play anonymously will lose the ability to track their progress across multiple games. When signed in, the player is presented with a list of games that are currently available for play. Each game includes a brief description and the various "adventures" available to the player. Once the game has been started, the player has three different methods for capturing nodes: they may scan a QR in the physical space, discover a node through the Layar camera virtual view, or receive a link in their device's web browser. === QR codes and Layar === QR codes can only be used as a method for capturing nodes and initiating games when there is a physical code present. In order to scan a QR code, players are required to have an application which can capture and recognize QR codes. If the player is utilizing a QR scanning application that has a built in browser, they will be required to log into fAR-Play through the app. Layar is a free to download augmented reality app, containing a built in QR code scanner, which enables its users to participate in fAR-Play games. === Capturing nodes === Layar permits the player to see nodes on their mobile device, guiding the player to their goal. Using this application, the player is able to navigate to their objective with map provided by Google Maps' API or by using their camera — Layar overlays a virtual image onto the real-world scene presented by the camera. The representations on screen expand in size as the player approaches the node destination, simulating relative distance. If the player taps any of the nodes that are presented on the screen, they will be provided additional information about that node, including the node's name and a brief description. Nodes can be captured by tapping the "capture" button. === Playing on browsers === The player can also play fAR-Play games within their mobile device's browser. By visiting https://archive.today/20131123223038/http://farplay.ualberta.ca/far-play/ on a mobile device, players will be presented with a fully realized user interface, permitting full interaction with the games. The player can capture the in game vPOIs through their browser by tapping the "nodes" button. This will bring up a list of all the accessible nodes, complete with a brief description for each location. By clicking on one of the nodes, the player is shown to a screen with a mapped location of the vPOI, an in-depth description of it, and hints. At the top of the page, the player can tap "CAPTURE THIS NODE" and advance in the game. When attempting to capture a node, the developer may or may not associate a challenge with the node. For example, in the game "Zombies ate my Campus", when players are attempting to capture a node, they're presented with a multiple choice question associated with the current node. === Game types === Players complete an adventure when they have captured all of the nodes within it. fAR-Play provides two game modes: in a Virtual Scavenger Hunt, nodes must be captured in a specific order; in a Virtual Treasure Hunt, the order is unimportant. == Existing fAR-Play games == Games currently available through fAR-Play include: Giselle Ever After Thought Hub Comics Arts Capture Challenge Pioneering Edmonton The Intelliphone Challenge A Tour of Atwater Zombies ate my Campus == For developers == fAR-Play's ultimate goal is to provide a simple, effective platform for the creation of locative augmented reality games, but the developer tools are still under active development and not openly available to the public. Access can be granted on a case-by-case basis, however, and a developer's manual is available. Users with development privileges can create new games or edit their existing games, in addition to playing their own or others' games. === Adventures === Games that are developed with fAR-Play are segmented into components called "Adventures". To progress through each game adventure, the player must reach and capture virtual points of interest, referred to in the game as vPOIs. In order to capture a vPOI, the player must travel to a physical location that is set by the developer. It is the developer's choice to include a challenge question to capture the vPOI, though it is not mandatory. A deduction of points can be implemented if the player submits an incorrect answer to a challenge question. === Points and achievements === Each of the nodes will reward the player with a predetermined number of points once they have been captured by the player. These points are added to the player's total points. Each of the adventures that are created require a predetermined number of vPOIs
Vintage computer
A vintage computer is an older computer system that is largely regarded as obsolete. The personal computer has been around since around 1971, and in that time technological advancement means existing models get replaced every few years. Nevertheless, these otherwise useless computers have spawned a sub-culture of vintage computer collectors who often spend large sums for the rarest examples, not only to display but functionally restore. This involves active software development and adaptation to modern uses. This often includes homebrew developers and hackers who add on, update and create hybrid composites from new and old computers for uses they were otherwise never intended. Ethernet interfaces have been designed for many vintage 8-bit machines to allow limited connectivity to the Internet, where users can access discussion groups, bulletin boards, and software databases. Most of this hobby centers on computers made after 1960, though some collectors also specialize in older computers. The Vintage Computer Festival, an event held by the Vintage Computer Federation for the exhibition and celebration of vintage computers, has been held annually since 1997 and has expanded internationally. == By platform == === MITS Inc. === Micro Instrumentation and Telemetry Systems (MITS) produced the Altair 8800 in 1975. According to Harry Garland, the Altair 8800 was the product that catalyzed the microcomputer revolution of the 1970s. === IMSAI === The IMSAI 8080 is a clone of the Altair 8800. It was introduced in 1975, first as a kit, and later as an assembled system. The list price was $591 (equivalent to $3,584 in 2025) for a kit, and $931 (equivalent to $5,570 in 2025) assembled. === Processor Technology === Processor Technology produced the Sol-20. This was one of the first machines to have a case that included a keyboard; a design feature copied by many of later "home computers". === SWTPC === Southwest Technical Products Corporation (SWTPC) produced the 8-bit SWTPC 6800 and later the 16-bit SWTPC 6809 kits that employed the Motorola 68xx series microprocessors. === Apple Inc. === The earliest Apple Inc. personal computers, using the MOS Technology 6502 processors, are among some of the most collectible. They are relatively easy to maintain in an operational state thanks to Apple's use of readily available off-the-shelf parts. Apple I (1976): The Apple-1 was Apple's first product and has brought some of the highest prices ever paid for a microcomputer at auction. Apple II (1977): The Apple II series of computers are some of the easiest to adapt, thanks to the original expansion architecture designed for them. New peripheral cards are still being designed by an avid thriving community, thanks to the longevity of this platform, manufactured from 1977 through 1993. Numerous websites exist to support not only legacy users but new adopters who weren't even born when the Apple II was discontinued by Apple. Macintosh (1984): The original Macintosh used a 32-bit Motorola 68000 processor running at 7.8336 MHz and came with 128 KB of RAM. The list price was $2495 (equivalent to $7,732 in 2025).Perhaps because of its friendly design and first commercially successful graphical user interface as well as its enduring Finder application that persists on the most current Macs, the Macintosh is one of the most collected and used vintage computers. With dozens of websites around the world, old Macintosh hardware and software are input into daily use. The Macintosh had a strong presence in many early computer labs, creating a nostalgia factor for former students who recall their first computing experiences. === RCA === The COSMAC Elf in 1976 was an inexpensive (about $100) single-board computer that was easily built by hobbyists. Many people who could not afford an Altair could afford an ELF, which was based on the RCA 1802 chip. Because the chips are still available from other sources, modern recreations of the ELF are fairly common and there are several fan websites. === IBM === The IBM 1130 (1965) was a desk-sized small computer. It was the often the first computer used by many college students, still has a following of interested users. Most of the remaining 1130 systems in 2023 are in museums, but an emulator is available for users who don't have access to a physical 1130. The 5100 also has an avid collector and fan base. The PC series (5150 PC, 5155 Portable PC, 5160 PC/XT, 5170 PC/AT) has become very popular in recent years, with the earliest models (PC) being considered the most collectible. === Acorn BBC & Archimedes === The Acorn BBC Micro was a very popular British computer in the 1980s with home and educational users and enjoyed near-universal usage in British schools into the mid-1990s. It was possible to use 100K 5+1⁄4-inch disks, and it had many expansion ports. The Archimedes series – the de facto successor to the BBC Micro – has also enjoyed a following in recent years, thanks to its status as the first computer to be based around ARM's RISC microprocessor. === Tandy/Radio Shack === The Tandy/RadioShack Model 100 is still widely collected and used as one of the earliest examples of a truly portable computer. Other Tandy offerings, such as the TRS-80 line, are also very popular, and early systems, like the Model I, in good condition can command premium prices on the vintage computer market. === Sinclair === The Sinclair ZX81 and ZX Spectrum series were the most popular British home computers of the early 1980s, with a wide choice of emulators available for both platforms. The Spectrum in particular enjoys a cult following due to its popularity as a games platform, with new games titles still being developed even today. Original "rubber key" Spectrums fetch the highest prices on the second-hand market, with the later Amstrad-built models attracting less of a following. The earlier ZX81 is not as popular in original hardware form due to its monochrome display and limited abilities next to the Spectrum, but still unassembled ZX81 kits still appear on eBay occasionally. === MSX === Although nearly nonexistent in the United States, the MSX architecture has strong communities of fans and hobbyists worldwide, particularly in Japan (where the standard was conceived and developed), South Korea (the only country that had an MSX-based game console, Zemmix), Netherlands, Spain, Brazil, Argentina, Russia, Chile, the Middle East, and others. New hardware and software are being actively developed to this day as well. One of the latest fundamental (from hardware and software perspectives) revivals of the MSX is the GR8BIT. === Robotron === The Robotron Z1013 was an East German home computer produced by VEB Robotron. It had a U880 processor, 16 KB RAM, and a membrane keyboard. The KC 85 series of computers was a modular 8-bit computer system used in East German schools. === Commodore === VIC-20 Commodore 64 Commodore PET Amiga === Xerox === The Xerox Alto, designed and manufactured by Xerox PARC and released in 1973, was the first personal computer equipped with a graphic user interface. In 1979, Steve Jobs of Apple Inc. arranged for his engineers to visit Xerox in order to see the Alto. The design concepts of the Alto soon appeared in the Apple Lisa and Macintosh systems. The Xerox Star, also known as the 8010/40, was made available in 1981. It followed on the Alto. Like the Alto, this machine was expensive and was only intended for corporate office usage. Therefore, being out of the price range of the average user, this product had little market penetration. === Silicon Graphics === The SGI Indy, built in 1993 for Silicon Graphics has a history of usage in the development of the Nintendo 64 as well as various CGI projects throughout the 1990s and early 2000s. The Indy and other machines in the SGI lineup have remained cult classics.
The Most Dangerous Writing App
The Most Dangerous Writing App is a web application for free writing that combats writer's block by deleting all progress if the user stops typing for five seconds. It is targeted at creative writers who want to write first drafts without worrying about editing or formatting. == Features == The app is designed to "shut down your inner editor and get you into a state of flow", referring to the psychological concept of being in a flow state. Users start a writing session by choosing a time or word limit, and can only save or download their work if they complete the set limit without interruption. An optional "hardcore mode" blurs out everything the user has written so far, making it impossible to edit before finishing the writing session. == History == The Most Dangerous Writing App was created by software engineer Manuel Ebert and was released as free, open source software on February 29, 2016. It was reviewed by Wired, Forbes, Vogue, Huffington Post, The Verge, The Next Web, and others. It has been used in free writing contests and is recommended by NaNoWriMo. In April 2019, The Most Dangerous Writing App was acquired by Squibler, but the original version remains freely accessible.
Web engineering
The World Wide Web has become a major delivery platform for a variety of complex and sophisticated enterprise applications in several domains. In addition to their inherent multifaceted functionality, these Web applications exhibit complex behaviour and place some unique demands on their usability, performance, security, and ability to grow and evolve. However, a vast majority of these applications continue to be developed in an ad hoc way, contributing to problems of usability, maintainability, quality and reliability. While Web development can benefit from established practices from other related disciplines, it has certain distinguishing characteristics that demand special considerations. In recent years, there have been developments towards addressing these considerations. Web engineering focuses on the methodologies, techniques, and tools that are the foundation of Web application development and which support their design, development, evolution, and evaluation. Web application development has certain characteristics that make it different from traditional software, information systems, or computer application development. Web engineering is multidisciplinary and encompasses contributions from diverse areas: systems analysis and design, software engineering, hypermedia/hypertext engineering, requirements engineering, human-computer interaction, user interface, data engineering, information science, information indexing and retrieval, testing, modelling and simulation, project management, and graphic design and presentation. Web engineering is neither a clone nor a subset of software engineering, although both involve programming and software development. While Web Engineering uses software engineering principles, it encompasses new approaches, methodologies, tools, techniques, and guidelines to meet the unique requirements of Web-based applications. == As a discipline == Proponents of Web engineering supported the establishment of Web engineering as a discipline at an early stage of Web. Major arguments for Web engineering as a new discipline are: Web-based Information Systems (WIS) development process is different and unique. Web engineering is multi-disciplinary; no single discipline (such as software engineering) can provide a complete theory basis, body of knowledge and practices to guide WIS development. Issues of evolution and lifecycle management when compared to more 'traditional' applications. Web-based information systems and applications are pervasive and non-trivial. The prospect of Web as a platform will continue to grow and it is worth being treated specifically. However, it has been controversial, especially for people in other traditional disciplines such as software engineering, to recognize Web engineering as a new field. The issue is how different and independent Web engineering is, compared with other disciplines. Main topics of Web engineering include, but are not limited to, the following areas: === Modeling disciplines === Business Processes for Applications on the Web Process Modelling of Web applications Requirements Engineering for Web applications B2B applications === Design disciplines, tools, and methods === UML and the Web Conceptual Modeling of Web Applications (aka. Web modeling) Prototyping Methods and Tools Web design methods CASE Tools for Web Applications Web Interface Design Data Models for Web Information Systems === Implementation disciplines === Integrated Web Application Development Environments Code Generation for Web Applications Software Factories for/on the Web Web 2.0, AJAX, E4X, ASP.NET, PHP and Other New Developments Web Services Development and Deployment === Testing disciplines === Testing and Evaluation of Web systems and Applications. Testing Automation, Methods, and Tools. === Applications categories disciplines === Semantic Web applications Document centric Web sites Transactional Web applications Interactive Web applications Workflow-based Web applications Collaborative Web applications Portal-oriented Web applications Ubiquitous and Mobile Web Applications Device Independent Web Delivery Localization and Internationalization of Web Applications Personalization of Web Applications == Attributes == === Web quality === Web Metrics, Cost Estimation, and Measurement Personalisation and Adaptation of Web applications Web Quality Usability of Web Applications Web accessibility Performance of Web-based applications === Content-related === Web Content Management Content Management System (CMS) Multimedia Authoring Tools and Software Authoring of adaptive hypermedia == Education == Master of Science: Web Engineering as a branch of study within the MSc program Web Sciences at the Johannes Kepler University Linz, Austria Diploma in Web Engineering: Web Engineering as a study program at the International Webmasters College (iWMC), Germany