AI Coding Unity

AI Coding Unity — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Joseph Stanislaus Ostoja-Kotkowski

    Joseph Stanislaus Ostoja-Kotkowski

    Joseph Stanislaus Ostoja-Kotkowski AM, FRSA (also known as J. S. Ostoja-Kotkowski, Ostoja and Stan Ostoja-Kotkowski; 28 December 1922 – 2 April 1994) was best known for his ground-breaking work in chromasonics, laser kinetics and 'sound and image' productions. He earned recognition in Australia and overseas for his pioneering work in laser sound and image technology. His work included painting (instrumental in developing geometric art in Australia), photography, film-making, theatre design, fabric design, murals, kinetic and static sculpture, stained glass, vitreous enamel murals, op-collages, computer graphics, and laser art. Ostoja flourished between 1940 and 1994. Ostoja's films are still being exhibited. == Biography == Joseph Stanislaus Ostoja-Kotkowski was born in Golub, Poland, on 28 December 1922, descending from an old noble family that was part of the Clan of Ostoja. He studied drawing under Olgierd Vetesco in Przasnysz from 1940-1945. After winning a scholarship, he completed his studies at the Düsseldorf Academy of Fine Arts in Germany in 1949. In 1950 Ostoja migrated to Australia, arriving in Melbourne where he supported himself with work as a labourer. He enrolled at the Victorian School of Fine Arts National Gallery School under Alan Sumner and William Dargie 1950-1955 and there introduced the new abstract expression of Europe both to lecturers and students. He settled in the Adelaide Hills, South Australia, on the Booth estate at Stirling, living under the patronage of the Booth family for over 40 years (Freya Booth, the wife of Edward Stirling Booth, was a daughter of the artist Sir Hans Heysen). His first one-man exhibition was also in South Australia at the Royal Society of Arts, Adelaide. In 1956 Ostoja met and collaborated with Ian Davidson in the production of the short film Five South Australian Artists, and became involved in stage and theatre set design. He co-produced several experimental films again with Ian Davidson, including The Quest of Time in 1957 Ostoja's work in abstract expression began to receive accolades. He won the Cornell Prize for the canvas Form in Landscape. He started to design sets for theatre and dance including for Six Characters in Search of an Author by Luigi Pirandello (1957); the South Australian production of Samuel Beckett's Waiting for Godot (1958); Gaetano Donizetti's Elixir of Love, with novel light settings and modulations, for the Elder Conservatorium of the University of Adelaide which used his techniques for their Opera Workshops (1959); for The Egg; and for two performances of the South Australian Ballet Theatre with light/colour abstract presentations (1959). 1960 This year he designed sets for a new opera group which would eventually grow into the South Australian Opera Company. Among other theatrical events, he designed and executed the scenery for Moon on a Rainbow Shawl by Errol John, and The Teahouse of the August Moon by John Patrick, (a production by the University of Adelaide Theatre Guild). He received artistic satisfaction but little financial reward for these efforts. In this year also, he staged a visual production on the theme of Orpheus, using dance, music and voice with several projectors. This was the first attempt at quadraphonic sound in Australia, working in collaboration with Derek Jolly, who provided the sound and projection equipment. It was also the first demonstration of "Chromasonics" - the science of translating sound into visual images. Ostoja then designed innovative "abstracted" scenery for a production of The Marriage of Figaro and Benjamin Britten's The Turn of the Screw. 1961 Ostoja designed the sets for the controversial South Australian production of Patrick White's The Ham Funeral - also Alan Seymour's Swamp Creatures, both performed by the University of Adelaide Theatre Guild. He designed and constructed six stained glass windows for the Refectory at the University of Adelaide. In this period Ostoja designed special lights and gauzes for difficult effects required in an ambitious production of the opera Don Carlos by the Opera Workshop, for the Elder Conservatorium. 1962 Ostoja designed and built sets for the production of J.B, by Archibald MacLeish, for the second Adelaide Festival of Arts. He exhibited vitreous enamel works in Melbourne's Argus Gallery. Max Harris, in The Bulletin of 20 October 1962, praised Ostoja's sets for My Cousin from Fiji in Union Theatre, Adelaide, and his technique of rear screen projections as later adopted throughout Australia. 1963 Ostoja continued to develop Multi-Image projections, demonstrating for the first time in Australia the concept later to be known as 'audio-visuals!'. Ostoja gave Sir Herbert Read, the art critic, a personal viewing of one of his visual presentations. At Christmas, in the Elder Conservatorium, collaborating again with Derek Jolly, Ostoja gave what was probably the world's first "visual concert", using special projectors and incorporating music, colours and shapes. 1964 With fellow Adelaide artist John Dallwitz, Ostoja co-designed the first of several experimental dance and stage productions in the Adelaide Festival of Arts Sound and Image. The production featured Adelaide dancer Elizabeth_Cameron_Dalman. Also for the Adelaide Festival of Arts of that year, he designed the largest light mosaic ever staged up to that time, upon the facade of an 11-storey building. Ostoja was invited to New Zealand, and exhibited the first electronically generated images in Australia in Melbourne, at the Argus Gallery. His design for the 50-foot (15 m) bas-relief mural for the new B.P. building in Melbourne was the subject of a film which won the "Blue Ribbon" Award in the American Film Festival in New York. 1965 Ostoja designed and made the first light kinetic mural in Australia, and continued to evolve theatrical works using multi-screen and Multi-projector techniques. The Production of Jean Genet's The Balcony was very controversial. With Elizabeth Dalman, Ostoja produced new dance forms for Melbourne Television. He introduced Op Art to Australia, both at South Yarra Gallery in Melbourne, and Gallery A in Sydney. 1966 With John Dallwitz, Ostoja was invited by the Adelaide Festival of Arts to present more experimental theatre, Sound and image 1966. This highly acclaimed production incorporated Australian poetry into the sound, electronic music, and visual images and featured the dancer Antonio Rodrigues. The architect Robin Boyd commissioned Ostoja to design two large Op murals for the Australian Pavilion entrance at the Expo 67. Ostoja was awarded a Churchill Fellowship, which enabled him to have extensive world travel, comparing art and technology in many countries. He began to work with language, contemporary poetry and prose, and computers. 1967 John Dallwitz and Ostoja presented Sound and Image at the Festival of Perth. In Berne, Switzerland, Ostoja received the "Excellence F.I.A.P." Award for innovative photography. 1968 At the Adelaide Festival of Arts, Ostoja and John Dallwitz collaborated again to stage Sound and Image. This was the first theatre production in the world to use a laser beam. It also included the first science fiction play (The Veldt by Ray Bradbury) performed in Australia. Ostoja's theatre methods were increasingly attracting the attention of critics to how plays were staged. "Chromasonics", developed and introduced by Ostoja, was now being used extensively in the entertainment industry. 1969 Ostoja staged Krzysztof Penderecki's St. Luke Passion, a controversial, contemporary religious work. The South Australian The Advertiser wrote an extensive critique of Ostoja's work. Robin Boyd commissioned Ostoja to build a "Chromasonic" exhibit located in the Space Tube at the Australian Pavilion for Expo '70 in Osaka. 1970 Ostoja presented an Australian Aboriginal Dreamtime theme in his "Sound and Image" theatre, working with leading contemporary figures in poetry, music and dance. This was the first production of its kind in Australia, and appeared after the Festival in Melbourne, Sydney, Canberra and Perth. Ostoja's Space Scape mural, sixty feet long by ten feet high, won the Australia-wide competition for a mural for Adelaide Airport. His 120 feet (37 m) high 'light and sound' structure for the Adelaide Festival was the first of its kind in the world. 1971 Ostoja awarded a Creative Arts Fellowship at the Australian National University, Canberra. His 18-month stay resulted in the design and building of a "Chromasonics unit-laser", a 100 feet (30 m) Chromasonic tower, and a world premiere of a Synchronos concert. 1972 With Don Burrows and Don Banks, Ostoja presented Synchronos 72, where one could "hear the colours and see the sounds". Ostoja added Cymatics, developed during the Fellowship, to his workshop repertoire. He was invited to exhibit his photography in the National Gallery, Melbourne. 1973 Ostoja received a Fellowship from the Australian American Education Associatio

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  • SitePal

    SitePal

    SitePal is a speaking avatar platform for small and medium-sized businesses developed by Oddcast. SitePal allows users to deploy "virtual employees" on websites that can welcome visitors, guide them around the site and answer questions. The use of SitePal on commercial websites has been controversial because many visitors report finding them annoying. Some research has shown that they can increase sales in comparison to using static photographs. == Development == The technology used was the result of more than 4 years of research at Stanford University. The research was based on a literature review and other previous work in the field of artificial intelligence research. The SitePal AI option uses the AIML programming language, which is partially editable by users. This allows web designers to simulate normal human conversation by using keywords or key phrases that the bot can respond to. == Features == The company provides web designers with options to customize the chosen avatar. A large selection of faces, clothing, hair, backgrounds, voices and other details are available. If a web designer wants to use a particular face, Sitepal can create one from a photo. Thus, a mascot or a known face can be simulated. == Speech == Sitepal avatars talk through text-to-speech (tts) software. A short paragraph can be written (up to 900 characters) and the text-to-speech engine will compile the actual speech, which can be reproduced and edited. The tts engine is not perfect, but it comes close to actual speech and is easy to understand. Tts can be further enhanced by some commands, like /laugh and /loud which make the avatar laugh or talk loud. Even pronunciation is possible. The web designer can record and upload his or her own audio messages. Alternatively Sitepal offers professional voice acting service at extra cost. == User interaction == The company provides 5 options for visitor interaction: No interaction. The avatar simply says a pre-fixed message. FAQ mode. Questions can be configured, which are clickable and the user can hear the answer. Lead mode. The avatar prompts the user to type his email and short message, so it can be sent to the webmaster (usually used on a "contact us" page) Chatbot mode. The avatar greets the user, and he can type his questions and have a conversation with the bot. With predetermined replies, this can work as an FAQ as well. API customization. Experienced programmers can make their avatar interact with their website, making it talk when the user clicks on a link or when other triggers occur. Even dual avatar conversations can be created, like a talk show. == Posting options == The company provides five options for posting the avatar: Embed in webpage (via javascript) Embed in HTML Send by email Publish to eBay Embed in Flash == Criticism == Early reviews, such as one by Troy Dreier published in PC World in 2002 were positive and described SitePal as: "an engagingly simple and personal tool, and the price is reasonable for what it adds to a site". Although Dreier did note that the program had "bugs that suggested it hadn't been tested thoroughly". In more recent years, reaction to SitePal has been much more negative with reviews such as Tom Spring writing in a PC World review citing SitePal ads and described his reaction as "Not so nice". Paul Bissex, writing in E-Scribe News described SitePal as "heinous... and embarrassing if anyone is within earshot...they creep me out" == Research on effectiveness == In one single-website research project Anita Campbell had half the visitors to Small Business Trends see a SitePal and the other half see just a static photograph. Over 11,000 visitors the SitePal avatar improved sign-up for a newsletter 144% over the control condition.

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  • Bluelight (web forum)

    Bluelight (web forum)

    Bluelight is a web-forum, research portal, online community, and non-profit organisation dedicated to harm reduction in drug use. Its userbase includes current and former substance users, academic researchers, drug policy activists, and mental health advocates. It is believed to be the largest online international drug discussion website in the world. As of November 2025, the website claims over 475,900 registered members, the Discord community claims over 11,900 members, and additional members utilise other platforms such as Telegram. Bluelight has been utilised by academic researchers as a primary source of data in numerous publications. Researchers also utilise the site to advertise research studies, recruit study participants, and better understand the world of substance use. Research groups and organisations that have partnered with Bluelight to recruit study participants include Imperial College London, Johns Hopkins University, Health Canada, Karlstad University, Curtin University, Macquarie University, Columbia University, University of Pennsylvania, University of Michigan, Toronto Metropolitan University (then known as Ryerson University), and MAPS. Researchers have found that the most common reasons for substance users to visit Bluelight.org and similar online communities are to learn "how to use drugs safely" and "how to help others use drugs safely." Bluelight neither condemns or condones drug use, instead advocating for the principle of responsible drug use; educating and allowing individuals to make informed decisions regarding their drug use, providing information on local drug misuse services, and providing them with other drug harm reduction resources and public safety notices. == History == Bluelight.org was originally formed in 1997 as a message board on bluelight.net called the MDMA Clearinghouse. The board was created as a side project by the owner of West Palm Beach design company Bluelight Designs. 200–300 users joined the site between 1998 and 1999, but the site's servers were heavily limited and could only store a few threads at a time; this led to the creation of 'The New Bluelight' forum in May 1999 and the registration of the bluelight.nu domain in June 1999. The site began to explode in popularity in the early 2000s with the rise of MDMA in the club scene, amassing nearly 7,000 members by the year 2000 and 59,000 by the start of 2006. The site switched to the bluelight.ru domain in October 2005, and switched again to bluelight.org in January 2014. In early 2024, Bluelight was re-structured and the forum became a subsidiary of the newly formed Australian non-profit organisation & registered charity Bluelight Communities Ltd. == Partnerships == In the early 2000s, Bluelight worked with reagent test supplier EZ-Test to promote the sale of drug checking kits. In 2007, Bluelight partnered with the Multidisciplinary Association for Psychedelic Studies (MAPS), a non-profit organisation working to raise awareness and understanding of psychedelic drugs through education, clinical research, and advocacy. MAPS utilised Bluelight to recruit participants for its first MDMA-assisted psychotherapy trial for PTSD. In 2013, the official MAPS forums were migrated to Bluelight. Bluelight's other partners include Erowid, a non-profit organisation dedicated to education surrounding psychoactive drugs; TripSit, a harm reduction education website; Pill Reports, a web-based database for drug checking results that was initially formed as an offshoot of the site; and the Global Drug Survey, an independent research organisation focused on collecting data about substance use. == Notable users == Alan Woods – funded the site's maintenance costs from 1999 until his death in 2008 Hamilton Morris John McAfee – created an infamous series of troll posts about the stimulant MDPV

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  • Content adaptation

    Content adaptation

    Content adaptation is the action of transforming content to adapt to device capabilities. Content adaptation is usually related to mobile devices, which require special handling because of their limited computational power, small screen size, and constrained keyboard functionality. Content adaptation could roughly be divided to two fields: Media content adaptation that adapts media files. Browsing content adaptation that adapts a website to mobile devices. == Browsing content adaptation == Advances in the capabilities of small, mobile devices such as mobile phones (cell phones) and Personal Digital Assistants have led to an explosion in the number of types of device that can now access the Web. Some commentators refer to the Web that can be accessed from mobile devices as the Mobile Web. The sheer number and variety of Web-enabled devices poses significant challenges for authors of websites who want to support access from mobile devices. The W3C Device Independence Working Group described many of the issues in its report Authoring Challenges for Device Independence. Content adaptation is one approach to a solution. Rather than requiring authors to create pages explicitly for each type of device that might request them, content adaptation transforms an author's materials automatically. For example, content might be converted from a device-independent markup language, such as XDIME, an implementation of the W3C's DIAL specification, into a form suitable for the device, such as XHTML Basic, C-HTML, or WML. Similarly, a suitable device-specific CSS style sheet or a set of in-line styles might be generated from abstract style definitions. Likewise, a device specific layout might be generated from abstract layout definitions. Once created, the device-specific materials form the response returned to the device from which the request was made. Another way is to use the latest trend responsive design based on CSS, covered in this article (RWD). Content adaptation requires a processor that performs the selection, modification, and generation of materials to form the device-specific result. IBM's Websphere Everyplace Mobile Portal (WEMP), BEA Systems' WebLogic Mobility Server, Morfeo's MyMobileWeb, and Apache Cocoon are examples of such processors. Wurfl and WALL are popular open source tools for content adaptation. WURFL is an XML-based Device Description Repository with APIs to access the data in Java and PHP (and other popular programming languages). WALL (Wireless Abstraction Library) lets a developer author mobile pages which look like plain HTML, but converts them to WML, C-HTML, or XHTML Mobile Profile, depending on the capabilities of the device from which the HTTP request originates. GreasySpoon lets the developer build plugins for content editing, in JavaScript, Ruby (programming language), and more, just like the Firefox application GreaseMonkey. Alembik (Media Transcoding Server) is a Java (J2EE) application providing transcoding services for variety of clients and for different media types (image, audio, video, etc.). It is fully compliant with OMA's Standard Transcoder Interface specification and is distributed under the LGPL open source license. In 2007, the first large scale carrier-grade deployments of content transformation, on existing mass-market handsets, with no software download required, were deployed by Vodafone in the UK and globally for Yahoo! oneSearch, using the Novarra Vision solution. Novarra's content adaptation solution had been used in enterprise intranet deployments as early as 2003 (at that time, the platform was named "Engines for Wireless Data"). InfoGin, the 9-year-old content-adaptation company with customers like Vodafone, Orange, Telefónica and PCCW. The patented "Web to Mobile adaptation", Mobile Matrix Transcoder, Multimedia and Documents transcoders, Video adaptation supporte. Launched in 2007, Bytemobile's Web Fidelity Service was another carrier-grade, commercial infrastructure solution, which provided wireless content adaptation to mobile subscribers on their existing mass-market handsets, with no client download required.

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  • LumenVox

    LumenVox

    LumenVox is a privately held speech recognition software company based in San Diego, California. LumenVox has been described as one of the market leaders in the speech recognition software industry. == History == LumenVox was founded in 2001 as subsidiary of Progressive Computing. According to LumenVox CEO Edward Miller, when Progressive had initially looked to add speech recognition to its own phone system, it found the existing offerings too expensive and recognized a niche in the market for a more affordable speech recognition product. This led to the development of LumenVox with an aim to bring speech recognition to small-to-midsized businesses. LumenVox is one of the major providers of automatic speech recognition for telephone systems, and as of 2006, became the second largest provider of speech recognition software. == Products == The primary LumenVox product is the LumenVox Speech Engine. It is a speaker-independent automatic speech recognizer that uses the Speech Recognition Grammar Specification for building and defining grammars. It has been integrated with several of the major voice platforms, including Avaya Voice Portal/Interactive Response, Aculab, and BroadSoft's BroadWorks. The Speech Engine was originally derived from CMU Sphinx, but LumenVox has added considerable development effort to make it a commercial-ready product. LumenVox also offers a product called the Speech Tuner, which provides a graphical means of testing and troubleshooting speech recognition applications. == Open source support == LumenVox was recognized as one of the top VoIP companies in 2008 for its work in providing its offerings to the open source community, an effort by the company that began in 2006 when it partnered with Digium. At that time, Digium, maintainer of the open source Asterisk PBX, integrated the LumenVox Speech Engine into Asterisk. This made LumenVox the first commercially available speech recognition engine for Asterisk. As one of the earlier commercial software integrations with Asterisk, the LumenVox integration has been described as one of the applications that helped to mainstream Asterisk. In 2009, LumenVox also began offering access to the Speech Engine as a monthly subscription, bringing the cost of entry down even lower for open source users. LumenVox is also integrated with the open source UniMRCP project, which provides open source client and server libraries for the Media Resource Control Protocol.

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  • Full30

    Full30

    Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac

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  • FaceApp

    FaceApp

    FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. The app generates highly realistic transformations of human faces in photographs by using neural networks. The app can transform a face to make it smile, look younger, look older, or change gender. == History == FaceApp was launched on iOS in January 2017 and on Android in February 2017. It was developed by Yaroslav Goncharov, a former executive at Yandex, and created by the Russian company Wireless Lab. == Features == There are multiple options to manipulate the photo uploaded such as editor options of adding an impression, make-up, smiles, hair colors, hairstyles, glasses, age or beards. Filters, lens blur and backgrounds along with overlays, tattoos, and vignettes are also a part of the app. The gender change transformations of FaceApp have attracted particular interest from the LGBT and transgender communities, due to their ability to realistically simulate the appearance of a person as the opposite gender. == Criticism == In 2017, FaceApp faced criticism for a "hot" filter that appeared to lighten users' skin tones, prompting accusations of racial bias. The feature was briefly renamed "spark" before being removed. Founder Yaroslav Goncharov attributed the issue to training data bias and apologized. In August of that year, more criticism arose when it featured "ethnicity filters" depicting "White", "Black", "Asian", and "Indian". The filters were immediately removed from the app. In 2019, FaceApp faced criticism over its handling of user data, including concerns that it stored users' photos on its servers and could use them for commercial purposes. Founder Yaroslav Goncharov stated that images were processed on cloud servers like Google Cloud Platform and Amazon Web Services, not transferred to Russia, and were temporarily stored only to support editing functions before being deleted. U.S. Senator Chuck Schumer raised concerns about data privacy and called for an FBI investigation.

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  • Fansly

    Fansly

    Fansly is a subscription-based social media platform that allows content creators to monetize exclusive content, including photos, videos, live streams, and direct messages. Operated by Select Media LLC, the platform is headquartered in Baltimore, Maryland. While the platform hosts a variety of content genres, it is primarily known for adult content and is frequently compared to OnlyFans. == History == Fansly was launched in 2020 by Micheal Etelis under Select Media LLC, which was incorporated in February 2020. The platform also operates through CY Media LTD, registered in Kamares, Cyprus, established in May 2021. The company has remained privately held with no disclosed external funding rounds or official valuation, operating as a bootstrapped entity. Based on Fansly's social media presence, which was created in November 2020, the platform did not begin gaining traction until early 2021 when creators started to become concerned about potential content policy changes at OnlyFans. In August 2021, OnlyFans announced it would ban sexually explicit content effective October 2021, citing pressure from banks involved in its payment processing. Although OnlyFans reversed the decision six days later, the announcement triggered a massive influx of users to Fansly; the platform received nearly 4,000 new creator applications in a single hour, causing its servers to crash from the surge in traffic. By August 21, 2021, Fansly had reached 2.1 million users. == Features and business model == Fansly operates as a B2C marketplace, taking a 20% commission on all transactions conducted on the platform, with creators retaining the remaining 80%. This commission rate is the same as that charged by its main competitor, OnlyFans. A distinguishing feature of Fansly is its tiered subscription model, which allows creators to set multiple subscription levels at different price points, each offering different perks such as exclusive content, chat access, or custom requests. By contrast, OnlyFans historically relied on a single-tier subscription model. Revenue streams on the platform include recurring subscriptions, one-time pay-per-view content purchases, tips, paid messaging, and live-streaming fees. The platform also features an algorithmic "For You" feed that helps users discover new creators, addressing a limitation of competitors that lack internal content promotion mechanisms. Additional features include content watermarking, geolocation blocking to control where content is visible, two-factor authentication, community polls, 24-hour stories, and social media integration with platforms such as Twitter and Twitch. Payouts are processed within one to two business days and support multiple methods, including bank transfers, Skrill, Paxum, and cryptocurrency. In December 2025, Fansly expanded its live-streaming capabilities, introducing ticketed access, private list gating, configurable chat permissions, stream goals, and interactive device integration. == Controversies == === OnlyFans anti-competitive allegations === In August 2022, a series of lawsuits were filed in the United States alleging that OnlyFans had bribed employees of Meta Platforms to place Instagram accounts of creators who also sold content on competitor platforms, including Fansly, onto a terrorist blacklist. The lawsuits alleged that adult performers had traffic driven away from their Instagram accounts after being falsely tagged as terror-related. OnlyFans denied awareness of such activity. The plaintiffs withdrew the bribery claim in July 2023, and the case was dismissed in August 2023. === Privacy class action === In June 2025, Select Media LLC (operating as Fansly) was the subject of a digital privacy class action lawsuit filed in Massachusetts District Court. The lawsuit alleged that the platform secretly collected and shared users' sensitive viewing data with Google and other third parties without consent. The case was brought on behalf of an estimated class of over 10,000 users across multiple states.

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  • Deep Learning Super Sampling

    Deep Learning Super Sampling

    Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available in a number of video games. The goal of these technologies is to allow the majority of the graphics pipeline to run at a lower resolution for increased performance, and then infer a higher resolution image from this that approximates the same level of detail as if the image had been rendered at this higher resolution. This allows for higher graphical settings or frame rates for a given output resolution, depending on user preference. All generations of DLSS are available on all RTX-branded cards from Nvidia in supported titles. However, the Frame Generation feature is only supported on RTX 40 series GPUs or newer and Multi Frame Generation is only available on 50 series GPUs. == History == Nvidia advertised DLSS as a key feature of GeForce RTX 20 series GPUs when they launched in September 2018. At that time, the results were limited to a few video games, namely Battlefield V, or Metro Exodus, because the algorithm had to be trained specifically on each game on which it was applied and the results were usually not as good as simple resolution upscaling. In 2019, Control shipped with ray tracing and an image processing algorithm that approximated DLSS, which did not use the Tensor Cores. In April 2020, Nvidia advertised and shipped an improved version of DLSS named DLSS 2 with driver version 445.75. DLSS 2.0 was available for a few existing games including Control and Wolfenstein: Youngblood, and would later be added to many newly released games and game engines such as Unreal Engine and Unity. This time Nvidia said that it used the Tensor Cores again, and that the AI did not need to be trained specifically on each game. Despite sharing the DLSS branding, the two iterations of DLSS differ significantly and are not backwards-compatible. In January 2025, Nvidia stated that there are over 540 games and apps supporting DLSS, and that over 80% of Nvidia RTX users activate DLSS. In March 2025, there were more than 100 games that support DLSS 4, according to Nvidia. By May 2025, over 125 games supported DLSS 4. The first video game console to use DLSS, the Nintendo Switch 2, was released on June 5, 2025. Nvidia announced DLSS 4.5 at CES 2026. In January 2026, Nvidia stated that over 250 games and applications support Multi Frame Generation. On March 16, 2026, at GTC 2026, Nvidia CEO Jensen Huang presented DLSS 5, a real-time AI model based on neural rendering that realistically enhances lighting and material surfaces at up to 4K resolution while retaining the developer's intended art style. It is planned to release in fall of 2026. In a blog post on its website, Nvidia has announced that DLSS 5 will be available in such games as Assassin's Creed Shadows, Delta Force, Hogwarts Legacy, Naraka: Bladepoint, Phantom Blade Zero, Resident Evil Requiem, Starfield, The Elder Scrolls IV: Oblivion Remastered, and more. On May 31, 2026, Nvidia announced an updated version of Ray Reconstruction for DLSS 4.5 in a blog post, scheduled for release on all RTX GPUs in August of the same year. They said it is designed to better embed spatial awareness into scenes and analyze engine data on movements and lighting conditions, resulting in a sharper, more stable, and less noisy image. === Release timeline === == Technology == === DLSS 1 === The first iteration of DLSS is a predominantly spatial image upscaler with two stages, both relying on convolutional auto-encoder neural networks. The first step is an image enhancement network which uses the current frame and motion vectors to perform edge enhancement, and spatial anti-aliasing. The second stage is an image upscaling step which uses the single raw, low-resolution frame to upscale the image to the desired output resolution. Using just a single frame for upscaling means the neural network itself must generate a large amount of new information to produce the high-resolution output, which can result in slight hallucinations such as leaves that differ in style to the source content. The neural networks are trained on a per-game basis by generating a "perfect frame" using traditional supersampling to 64 samples per pixel, as well as the motion vectors for each frame. The data collected must be as comprehensive as possible, including as many levels, times of day, graphical settings, resolutions, etc. as possible. This data is also augmented using common augmentations such as rotations, colour changes, and random noise to help generalize the test data. Training is performed on Nvidia's Saturn V supercomputer. This first iteration received a mixed response, with many criticizing the often soft appearance and artifacts along with glitches in certain situations; likely a side effect of the limited data from only using a single frame input to the neural networks which could not be trained to perform optimally in all scenarios and edge-cases. Nvidia also demonstrated the ability for the auto-encoder networks to learn the ability to recreate depth-of-field and motion blur, although this functionality has never been included in a publicly released product. === DLSS 2 === DLSS 2 is a temporal anti-aliasing upsampling (TAAU) implementation, using data from previous frames extensively through sub-pixel jittering to resolve fine detail and reduce aliasing. The data DLSS 2 collects includes: the raw low-resolution input, motion vectors, depth buffers, and exposure / brightness information. It can also be used as a simpler TAA implementation where the image is rendered at 100% resolution, rather than being upsampled by DLSS, Nvidia brands this as DLAA (Deep Learning Anti-Aliasing). TAA(U) is used in many modern video games and game engines; however, all previous implementations have used some form of manually written heuristics to prevent temporal artifacts such as ghosting and flickering. One example of this is neighborhood clamping which forcefully prevents samples collected in previous frames from deviating too much compared to nearby pixels in newer frames. This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method. DLSS 2 uses a convolutional auto-encoder neural network trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above. Because of this, DLSS 2 can generally resolve detail better than other TAA and TAAU implementations, while also removing most temporal artifacts. This is why DLSS 2 can sometimes produce a sharper image than rendering at higher, or even native resolutions using traditional TAA. However, no temporal solution is perfect, and artifacts (ghosting in particular) are still visible in some scenarios when using DLSS 2. Because temporal artifacts occur in most art styles and environments in broadly the same way, the neural network that powers DLSS 2 does not need to be retrained when being used in different games. Despite this, Nvidia does frequently ship new minor revisions of DLSS 2 with new titles, so this could suggest some minor training optimizations may be performed as games are released, although Nvidia does not provide changelogs for these minor revisions to confirm this. The main advancements compared to DLSS 1 include: Significantly improved detail retention, a generalized neural network that does not need to be re-trained per-game, and ~2x less overhead (~1–2 ms vs ~2–4 ms). It should also be noted that forms of TAAU such as DLSS 2 are not upscalers in the same sense as techniques such as ESRGAN or DLSS 1, which attempt to create new information from a low-resolution source; instead, TAAU works to recover data from previous frames, rather than creating new data. In practice, this means low resolution textures in games will still appear low-resolution when using current TAAU techniques. This is why Nvidia recommends game developers use higher resolution textures than they would normally for a given rendering resolution by applying a mip-map bias when DLSS 2 is enabled. === DLSS 3 === Augments DLSS 2 with improved image quality and the introduction of a new motion interpolation feature, called Frame Generation. The DLSS Frame Generation algorithm takes two rendered frames from the rendering pipeline and generates a new frame that smoothly transitions between them. For every frame rendered, one additional frame is generated. DLSS 3.0 makes use of a new generation Optical Flow Accelerator (OFA) included in the Ada Lovelace architecture of GeForce RTX 40 series GPUs and with that is exclusive to them. The new OFA is said to be faster and more accurate than the one already available in previous Turing and Ampere RTX GPUs. === DLSS 3.5 === DLSS 3.5 adds Ray Reconstruction, replacing multiple denoising algorithms with a single AI model trained o

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  • History of operating systems

    History of operating systems

    Computer operating systems (OSes) provide a set of functions needed and used by most application programs on a computer, and the links needed to control and synchronize computer hardware. On the first computers, with no operating system, every program needed the full hardware specification to run correctly and perform standard tasks, and its own drivers for peripheral devices like printers and punched paper card readers. The growing complexity of hardware and application programs eventually made operating systems a necessity for everyday use. == Background == Early computers lacked any form of operating system. Instead, the user (rarely also the computer operator), had sole use of the machine for a scheduled period of time. The user would deliver his program to a computer operator who would be responsible for loading the computer with the program and data needed for its 'run'. Eventually, the end of a user's program could be detected and a control program automatically loaded which would load the next user's program, relieving the operator of having to load in each user's program individually and introducing the era of 'batched' programming. That is, a number of user programs could all be loaded together in a batch. Loading of program and data was accomplished in various ways including toggle switches (only used by a user on the earliest of computers, but later used by the computer operator to control the computer, e.g., to start it up, to shut it down, to 'pause', to 'dump' its RAM contents, and/or to control its input and/or its output), punched paper cards and magnetic or paper tape. Once loaded, the machine would be set to execute each program singly until that program completed, crashed, exceeded its time limit or went into a(n infinite) loop. In those early days, there were only 'Control Program' units for providing the software necessary to control the computers and ancillary hardware, e.g., for such semi hardware functions as I/O . None of the early 'Control Programs' were sufficiently sophisticated to recognize a looping user program or initiate a recovery action. Detection and recovery from a looping program was another critical operator function and was usually detected by the sound of the looping computer, whereupon the operator would simply initiate a complete dump of the executing program (for later debugging by the programmer) and then load in (or instruct the computer to go on to) the next user's program. Programs could sometimes be debugged via a control panel using dials, toggle switches and panel lights, making it a very manual and error-prone process. But, this was quite rare, since the high cost of even the simplest of the early computers prohibited such exclusive use of a computer by an individual programmer. Almost all program debugging was done away from any computer by the original programmer perusing the program and the dump of its execution obtained, e.g., by the computer operator or automatically by some computer hardware exception detection (such as a timeout, an attempt to divide by zero, or an over or underflow). Programmers then could only very rarely have more than one computer 'run' per day! Symbolic languages, e.g., assemblers and compilers were developed for programmers to translate symbolic program code into machine code that previously would have been hand-encoded. Later machines came with libraries of support code on punched cards or magnetic tape, which would be linked to the user's program to assist in operations such as input and output. This was the genesis of the modern-day operating system; however, machines still ran a single program or job at a time. At Cambridge University in England the job queue was at one time a string from which tapes attached to corresponding job tickets were hung with stationery pegs. == Mainframes == The first operating system used for real work was GM-NAA I/O, produced in 1956 by General Motors' Research division for its IBM 704. Most other early operating systems for IBM mainframes were also produced by customers. Early operating systems were very diverse, with each vendor or customer producing one or more operating systems specific to their particular mainframe computer. Every operating system, even from the same vendor, could have radically different models of commands, operating procedures, and such facilities as debugging aids. Typically, each time the manufacturer brought out a new machine, there would be a new operating system, and most applications would have to be manually adjusted, recompiled, and retested. === Systems on IBM hardware === Building on customer experience and requirements, IBM took on a more active role in developing operating systems for the 709, 1410, 7010, 7040, 7044, 7090 and 7094. IBM also collaborated with universities. The state of affairs continued until the mid 1960s when IBM, already a leading hardware vendor, stopped work on existing systems and put all its effort into developing the System/360 series of machines, all of which used the same instruction and input/output architecture. IBM intended to develop a single operating system for the new hardware, the OS/360. The problems encountered in the development of the OS/360 are legendary, and are described by Fred Brooks in The Mythical Man-Month—a book that has become a classic of software engineering. Because of performance differences across the hardware range and delays with software development, a whole family of operating systems was introduced instead of a single OS/360. IBM wound up releasing a series of stop-gaps followed by two longer-lived operating systems: OS/360 for mid-range and large systems. This was available in three system generation options: PCP for early users and for those without the resources for multiprogramming. MFT for mid-range systems, replaced by MFT-II in OS/360 Release 15/16. This had one successor, OS/VS1, which was discontinued in the 1980s. MVT for large systems. This was similar in most ways to PCP and MFT (most programs could be ported among the three without being re-compiled), but has more sophisticated memory management and a time-sharing facility, TSO. MVT had several successors including the current z/OS. DOS/360 for small System/360 models had several successors including the current z/VSE. It was significantly different from OS/360. IBM maintained full compatibility with the past, so that programs developed in the sixties can still run under z/VSE (if developed for DOS/360) or z/OS (if developed for MFT or MVT) with no change. IBM also developed TSS/360, a time-sharing system for the System/360 Model 67. Overcompensating for their perceived importance of developing a timeshare system, they set hundreds of developers to work on the project. Early releases of TSS were slow and unreliable; by the time TSS had acceptable performance and reliability, IBM wanted its TSS users to migrate to OS/360 and OS/VS2; while IBM offered a TSS/370 PRPQ, they dropped it after 3 releases. Several operating systems for the IBM S/360 and S/370 architectures were developed by third parties, including the Michigan Terminal System (MTS) and MUSIC/SP. === Other mainframe operating systems === Control Data Corporation developed the SCOPE operating systems in the 1960s, for batch processing and later developed the MACE operating system for time sharing, which was the basis for the later Kronos. In cooperation with the University of Minnesota, the Kronos and later the NOS operating systems were developed during the 1970s, which supported simultaneous batch and time sharing use. Like many commercial time sharing systems, its interface was an extension of the DTSS time sharing system, one of the pioneering efforts in timesharing and programming languages. In the late 1970s, Control Data and the University of Illinois developed the PLATO system, which used plasma panel displays and long-distance time sharing networks. PLATO was remarkably innovative for its time; the shared memory model of PLATO's TUTOR programming language allowed applications such as real-time chat and multi-user graphical games. For the UNIVAC 1107, UNIVAC, the first commercial computer manufacturer, produced the EXEC I operating system, and Computer Sciences Corporation developed the EXEC II operating system and delivered it to UNIVAC. EXEC II was ported to the UNIVAC 1108. Later, UNIVAC developed the EXEC 8 operating system for the 1108; it was the basis for operating systems for later members of the family. Like all early mainframe systems, EXEC I and EXEC II were a batch-oriented system that managed magnetic drums, disks, card readers and line printers; EXEC 8 supported both batch processing and on-line transaction processing. In the 1970s, UNIVAC produced the Real-Time Basic (RTB) system to support large-scale time sharing, also patterned after the Dartmouth BASIC system. Burroughs Corporation introduced the B5000 in 1961 with the MCP (Master Control Program) operating system. The B5000

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  • Full30

    Full30

    Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac

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  • Brand networking

    Brand networking

    Brand networking is the engagement of a social networking service around a brand by providing consumers with a platform of relevant content, elements of participation, and a currency, score, or ranking. Brand networking creates communities that serve as interactive destinations to encourage brand participation online and off. This evolved level of user participation with the brand facilitates strong relationships with consumers, leverages sales, and generates fan equity. The concept builds on the marketing literature on brand communities, which describes specialized, non-geographically bound groups of consumers organized around shared interest in a brand, and on subsequent research on social-media-based brand communities that examines how such groups operate when embedded in general-purpose networking platforms. == History == The development and growth of social networking in the early 2000s gave birth to brand networking. Brands saw the immediate potential to reach and interact with consumers through online platforms like Facebook and MySpace. At first, the ability to reach consumers through these platforms was inadequate; brands had the option to join as members or simply advertise on these sites. The potential existed to not only display advertisements to consumers, but to encourage them to interact with the brand. This is when brands made the shift to create their own networking platforms. Less evolved attempts to connect brands with consumers via networking are typically built as online platforms meant only to complement a product/service and are limited in functionality. Typically these sites offer consumers the opportunity to interact through discussion boards and group pages. The Guiding Light Community was built to complement the popular CBS television soap opera. The site offers members reward points for contributing content to discussion boards and blogs (which is all geared toward the show). == Structure == Brand networking is more than the utilization of a social networking platform; it is connecting consumers together and constructing relationships directly with the brand. Three key elements, in unity, create effective brand networking: relevant content, elements of participation, and a competitive currency. Websites in conjunction with other media types (television, radio, print) present content around a vertical industry, sector of interest, or cultural and social issues for a brand. This can be in areas such as health, marketing, or business, or any content relevant to the brand message. Such content is not only provided by the brand but also in the form of consumer-generated media. Research on brand-related user-generated content across major platforms suggests that the form and tone of consumer contributions vary by platform, with promotional content more common on some networks and response-oriented content on others. A brand provides participation with consumers online and offline. This is accomplished through the combination of typical social networking features online, such as personalised pages, friend lists, groups, and messaging, alongside elements of involvement offline. This is not simply connecting an online platform with mobile devices, but providing separate mobile features jointly with a secondary media type to drive online usage and build relationships with the brand on the go. By participating in mobile campaigns, users are interacting with the brand outside of traditional brick and mortar or e-commerce destinations. Empirical work on consumer brand engagement in social media frames such participation along cognitive, affective, and behavioural dimensions. The final element of brand networking involves incentivising participation with the other two elements. The addition of a currency or point system acts as an anchor to the brand and network and creates a competitive dynamic between consumers. These points are distributed for activity carried out outside of the networking site. By incentivising usage offline, the brand image is reinforced for the consumer and strengthens the relationship. Consumers are turned into promoters for both the brand and the users' benefit. The use of points, badges, leaderboards, and similar mechanics is described in the marketing literature as gamification, and has been linked to higher participation rates in mobile and loyalty programmes. == Fan equity == Fan equity is the idea that by locking in consumers to a brand, they are turned into fans of the brand. As fans, they promote, interact, and consume on a daily basis and become assets. Apple Inc. is one example of a company often cited as possessing fan equity. Customers of Apple are extremely brand loyal and are assets to the company. Creating a fan-generated brand is a difficult but effective method of business. Through the use of brand networking, a company is able to build a consumer or fan base that provides a strong relationship between business and consumers. The trust is formed and fans do a lot of work for the brand by word of mouth. Peer-to-peer channels are the strongest means of communication for a brand, but also one in which the brand can only influence and not control. Subsequent research links community engagement with brand trust, identifying community engagement as a mediator between social-media brand community participation and trust. This method of business is argued to be a relationship handled by the brand generally for its own gain. Many fans do not realise the work they are doing for companies by using their product or service. Facebook is a fan-based brand that has become a global phenomenon through customer use, with social media features such as sharing and commenting. With the growth of social media, marketing and advertising through social media has continued to expand. Brands can display and promote their products or services at a fast rate, with consumers sharing and contributing to the brand on a global scale. This can also be seen as online word of mouth exposure that can produce positive or negative feedback for brands. Once consumers become fans they are typically loyal, which can create positive word of mouth for a brand. Fans become a valuable asset, boosting the status and reputation of a brand. Different perceptions of brands can be linked to a person's origin or religion, which creates a difficulty when trying to enter a market or gain market share. Businesses need to be aware of the types of products or services they introduce to a specific market, ensuring they are culturally sensitive. Fan pages are created on social media to maintain the relationship between brands and consumers. By engaging and interacting with consumers, brands obtain fans and produce positive imaging. Some fans become attached to brands and are often encouraged to remain as fans through the use of celebrities endorsing the brand. Research on parasocial interaction in social-media environments suggests that one-sided emotional bonds that consumers form with endorsers and brand personae help convert ordinary followers into engaged fans.

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  • Certified social engineering prevention specialist

    Certified social engineering prevention specialist

    Certified Social Engineering Prevention Specialist (CSEPS) is a social engineering security-awareness training and professional certification program originally developed by Kevin Mitnick and Alexis Kasperavičius. == Course structure == The original CSEPS program was structured as a multi-module corporate security-awareness course designed to teach employees, managers, and IT personnel how social engineers manipulate human behavior to bypass technical security systems. The curriculum combined case studies, psychological analysis, attack demonstrations, pretexting exercises, and operational security scenarios. The course materials described social engineering as the exploitation of "the human factor" in information security and argued that traditional technical defenses alone were insufficient to protect organizations from deception-based attacks. The training program was divided into instructional modules covering topics such as: social engineering methodology and threat analysis intelligence gathering and reconnaissance dumpster diving pretexting elicitation technique telephone-system exploitation and caller-ID spoofing psychological influence techniques industrial espionage identity theft organizational vulnerabilities security policy development and employee awareness training The course also analyzed historical and contemporary case studies involving information theft, corporate espionage, fraudulent wire transfers, and telephone-based impersonation attacks. Training exercises required participants to analyze how attackers established credibility, manipulated trust, overcame objections, and exploited organizational procedures. According to The Wall Street Journal, CSEPS was delivered as a two-day "boot camp" course costing approximately US$1,500 per attendee. Clients reportedly included the United States Air Force and the United States Marine Corps. The certification examination included multiple-choice and written-response sections dealing with social-engineering defense scenarios and mitigation strategies. == History == In 2003, Mitnick and Kasperavičius partnered with the Florida-based IT training company Intense School Inc. to offer CSEPS classes throughout the United States. In 2020, Mitnick partnered with security-awareness training company KnowBe4, and elements of the original CSEPS material became incorporated into KnowBe4's social-engineering awareness training offerings.

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  • HtmlUnit

    HtmlUnit

    HtmlUnit is a headless web browser written in Java. It allows high-level manipulation of websites from other Java code, including filling and submitting forms and clicking hyperlinks. It also provides access to the structure and the details within received web pages. HtmlUnit emulates parts of browser behaviour including the lower-level aspects of TCP/IP and HTTP. A sequence such as getPage(url), getLinkWith("Click here"), click() allows a user to navigate through hypertext and obtain web pages that include HTML, JavaScript, Ajax and cookies. This headless browser can deal with HTTPS security, basic HTTP authentication, automatic page redirection and other HTTP headers. It allows Java test code to examine returned pages either as text, an XML DOM, or as collections of forms, tables, and links. The goal is to simulate real browsers; namely Chrome, Firefox and Edge. The most common use of HtmlUnit is test automation of web pages, but sometimes it can be used for web scraping, or downloading website content. == Benefits == Provides high-level API, taking away lower-level details away from the user. Compared to other WebDriver implementations, HtmlUnitDriver is the fastest to implement. It can be configured to simulate a specific browser. == Drawbacks == Element layout and rendering can not be tested. The JavaScript support is not complete, which is one of the areas of ongoing enhancements. == Used technologies == W3C DOM HTTP connection, using Apache HttpComponents JavaScript, using forked Rhino HTML Parsing, NekoHTML CSS: using CSS Parser XPath support, using Xalan == Libraries using HtmlUnit == Selenium WebDriver Spring MVC Test Framework Google Web Toolkit tests WebTest Wetator

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  • Mashup (web application hybrid)

    Mashup (web application hybrid)

    A mashup (computer industry jargon), in web development, is a web page or web application that uses content from more than one source to create a single new service displayed in a single graphical interface. For example, a user could combine the addresses and photographs of their library branches with a Google map to create a map mashup. The term implies easy, fast integration, frequently using open application programming interfaces (open API) and data sources to produce enriched results that were not necessarily the original reason for producing the raw source data. The term mashup originally comes from creating something by combining elements from two or more sources. The main characteristics of a mashup are combination, visualization, and aggregation. It is important to make existing data more useful, for personal and professional use. To be able to permanently access the data of other services, mashups are generally client applications or hosted online. In the past years, more and more Web applications have published APIs that enable software developers to easily integrate data and functions the SOA way, instead of building them by themselves. Mashups can be considered to have an active role in the evolution of social software and Web 2.0. Mashup composition tools are usually simple enough to be used by end-users. They generally do not require programming skills and rather support visual wiring of GUI widgets, services and components together. Therefore, these tools contribute to a new vision of the Web, where users are able to contribute. The term "mashup" is not formally defined by any standard-setting body. == History == The broader context of the history of the Web provides a background for the development of mashups. Under the Web 1.0 model, organizations stored consumer data on portals and updated them regularly. They controlled all the consumer data, and the consumer had to use their products and services to get the information. The advent of Web 2.0 introduced Web standards that were commonly and widely adopted across traditional competitors and which unlocked the consumer data. At the same time, mashups emerged, allowing mixing and matching competitors' APIs to develop new services. The first mashups used mapping services or photo services to combine these services with data of any kind and therefore to produce visualizations of data. In the beginning, most mashups were consumer-based, but recently the mashup is to be seen as an interesting concept useful also to enterprises. Business mashups can combine existing internal data with external services to generate new views on the data. There was also the free Yahoo! Pipes to build mashups for free using the Yahoo! Query Language. == Types of mashup == There are many types of mashup, such as business mashups, consumer mashups, and data mashups. The most common type of mashup is the consumer mashup, aimed at the general public. Business (or enterprise) mashups define applications that combine their own resources, application and data, with other external Web services. They focus data into a single presentation and allow for collaborative action among businesses and developers. This works well for an agile development project, which requires collaboration between the developers and customer (or customer proxy, typically a product manager) for defining and implementing the business requirements. Enterprise mashups are secure, visually rich Web applications that expose actionable information from diverse internal and external information sources. Consumer mashups combine data from multiple public sources in the browser and organize it through a simple browser user interface. (e.g.: Wikipediavision combines Google Map and a Wikipedia API) Data mashups, opposite to the consumer mashups, combine similar types of media and information from multiple sources into a single representation. The combination of all these resources create a new and distinct Web service that was not originally provided by either source. === By API type === Mashups can also be categorized by the basic API type they use but any of these can be combined with each other or embedded into other applications. ==== Data types ==== Indexed data (documents, weblogs, images, videos, shopping articles, jobs ...) used by metasearch engines Cartographic and geographic data: geolocation software, geovisualization Feeds, podcasts: news aggregators ==== Functions ==== Data converters: language translators, speech processing, URL shorteners... Communication: email, instant messaging, notification... Visual data rendering: information visualization, diagrams Security related: electronic payment systems, ID identification... Editors == Mashup enabler == In technology, a mashup enabler is a tool for transforming incompatible IT resources into a form that allows them to be easily combined, in order to create a mashup. Mashup enablers allow powerful techniques and tools (such as mashup platforms) for combining data and services to be applied to new kinds of resources. An example of a mashup enabler is a tool for creating an RSS feed from a spreadsheet (which cannot easily be used to create a mashup). Many mashup editors include mashup enablers, for example, Presto Mashup Connectors, Convertigo Web Integrator or Caspio Bridge. Mashup enablers have also been described as "the service and tool providers, [sic] that make mashups possible". === History === Early mashups were developed manually by enthusiastic programmers. However, as mashups became more popular, companies began creating platforms for building mashups, which allow designers to visually construct mashups by connecting together mashup components. Mashup editors have greatly simplified the creation of mashups, significantly increasing the productivity of mashup developers and even opening mashup development to end-users and non-IT experts. Standard components and connectors enable designers to combine mashup resources in all sorts of complex ways with ease. Mashup platforms, however, have done little to broaden the scope of resources accessible by mashups and have not freed mashups from their reliance on well-structured data and open libraries (RSS feeds and public APIs). Mashup enablers evolved to address this problem, providing the ability to convert other kinds of data and services into mashable resources. === Web resources === Of course, not all valuable data is located within organizations. In fact, the most valuable information for business intelligence and decision support is often external to the organization. With the emergence of rich web applications and online Web portals, a wide range of business-critical processes (such as ordering) are becoming available online. Unfortunately, very few of these data sources syndicate content in RSS format and very few of these services provide publicly accessible APIs. Mashup editors therefore solve this problem by providing enablers or connectors. == Mashups versus portals == Mashups and portals are both content aggregation technologies. Portals are an older technology designed as an extension to traditional dynamic Web applications, in which the process of converting data content into marked-up Web pages is split into two phases: generation of markup "fragments" and aggregation of the fragments into pages. Each markup fragment is generated by a "portlet", and the portal combines them into a single Web page. Portlets may be hosted locally on the portal server or remotely on a separate server. Portal technology defines a complete event model covering reads and updates. A request for an aggregate page on a portal is translated into individual read operations on all the portlets that form the page ("render" operations on local, JSR 168 portlets or "getMarkup" operations on remote, WSRP portlets). If a submit button is pressed on any portlet on a portal page, it is translated into an update operation on that portlet alone (processAction on a local portlet or performBlockingInteraction on a remote, WSRP portlet). The update is then immediately followed by a read on all portlets on the page. Portal technology is about server-side, presentation-tier aggregation. It cannot be used to drive more robust forms of application integration such as two-phase commit. Mashups differ from portals in the following respects: The portal model has been around longer and has had greater investment and product research. Portal technology is therefore more standardized and mature. Over time, increasing maturity and standardization of mashup technology will likely make it more popular than portal technology because it is more closely associated with Web 2.0 and lately Service-oriented Architectures (SOA). New versions of portal products are expected to eventually add mashup support while still supporting legacy portlet applications. Mashup technologies, in contrast, are not expected to provide support for portal standards. == Business mashups

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