AI Face Combiner

AI Face Combiner — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Embedding (machine learning)

In machine learning, embedding is a representation learning technique that maps complex, high-dimensional data into a lower-dimensional vector space of numerical vectors. == Technique == It also denotes the resulting representation, where meaningful patterns or relationships are preserved. As a technique, it learns these vectors from data like words, images, or user interactions, differing from manually designed methods such as one-hot encoding. This process reduces complexity and captures key features without needing prior knowledge of the domain. == Similarity == In natural language processing, words or concepts may be represented as feature vectors, where similar concepts are mapped to nearby vectors. The resulting embeddings vary by type, including word embeddings for text (e.g., Word2Vec), image embeddings for visual data, and knowledge graph embeddings for knowledge graphs, each tailored to tasks like NLP, computer vision, or recommendation systems. This dual role enhances model efficiency and accuracy by automating feature extraction and revealing latent similarities across diverse applications. To measure the distance between two embeddings, a similarity measure can be used to find the overall similarity of the concepts represented by the embeddings. If the vectors are normalized to have a magnitude of 1, then the similarity measures are proportional to cos ⁡ ( θ a b ) {\displaystyle \cos \left(\theta _{ab}\right)} . The cosine similarity disregards the magnitude of the vector when determining similarity, so it is less biased towards training data that appears very frequently. The dot product includes the magnitude inherently, so it will tend to value more popular data. Generally, for high-dimensional vector spaces, vectors tend to converge in distance, so Euclidean distance becomes less reliable for large embedding vectors.
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IBM Retail Store Systems

This article describes IBM point of sale equipment from 1973 with the introduction of the IBM 3650 till 1986 with the introduction of the IBM 4680. IBM continued to announced new retail products until the sale of the IBM Retail Store Solutions business to Toshiba TEC, announced on 17 April 17 2012. == Background == IBM began selling retail point of sale systems starting in 1973 with the IBM 3650 Retail Store System aimed at department and chain stores and the IBM 3660 Supermarket System designed for supermarkets. The IBM 3650 was announced alongside other IBM vertical industry systems such as the IBM 3600 Finance Communication System, and the IBM 3790 communications system, the combination of which IBM described as a "revolution in terminal based systems". All of these systems relied on a significant number of developments across IBM: New chips: Large Scale Integration allowed advanced Field Effect Transistor logic chips that packed far more transistors onto a new metalized one-inch square ceramic substrate Gas panels: Developed as an alternative to cathode ray tubes, the neon argon gas panel provided clear and flicker-free images. Modem communications: Synchronous Data Link Control provided lower-cost communications over telephone lines New disks: The "Gulliver" disk file that supplied a hard drive smaller than three cubic feet and also the "Igar" diskette drive Smaller printers: A disk printer system called "spica" that used a rotating disk print element with engraved print elements that are struck by a single hammer as the disk rotates Belt printers: A new system, known as "Lynx," using a removable belt that was significantly cheaper, quieter and simpler than earlier chain printers Keyboards: New keyboard technology called "Calico" that could build a wide variety of keyboards using common manufacturing facilities Power supplies: Transistorised Switching Regulators or TsRs: compact power supplies that are one third to one-fourth the size of previous generations === Store Loop (SLOOP) architecture === The 36xx retail terminals are connected to the store controller via a loop also called a Store Loop, similar to that used by the IBM 3600 Finance System. If a terminal detects an error, it runs a self-diagnosis routine, displays an error code to the operator, and uses bypass circuitry to remove itself from the loop and allow the loop to continue operating. If the loop fails, the most downstream terminal transmits an error code to the controller. Intermittent errors are written to disk on the store controller. === Supplies Manufacturing === While IBM's Data Processing Division created the retail store systems, it's Information Record Division (IRD) also saw signifiant opportunity in manufacturing supplies for retail systems. As an example in their Dayton NJ plant they used a high-speed Webtron press to create up to 1 million magnet merchandise tags per shift. == IBM 3650 Retail Store System == The 3650 System is a family of products designed to computerise a retail store, both at the point of sale and for back office store management functions. It includes a method to generate encoded tickets for merchandise, rather than use the Universal Product Code (UPC). The key devices for the system were as follows: === Shop Floor === ==== 3653 Point of Sale Terminal ==== Designed for the store floor, it is a loop attached device with: a wire matrix printer with 3 stations: cash receipt, sales-check and transaction journal. a keyboard with 10 numeric keys and 19 function keys an 8 digit display and description lights. in addition to the 8 digits it also displays the following characters: "$", "." and "-" operator guidance panel with 20 backlit captions status indicators a cash drawer a check verification station. Options include a wand magnet label reader with a 4 foot flexible cord, and locks for the journal tape and the till cover. The terminal effectively loads its software remotely from the 3651 over the loop, which IBM calls an IML (initial microcode load). It can also be IMLed locally using a tape cassette recorder. IBM later offered a choice of OEM Wand Attachments that could be ordered by RPQ that could use OCR or scan UPCs, instead of a wand magnet label reader. Only one wand could be attached to a specific 3653. There are two models: Model 1, which is not programmable. Was announced 10 August 1973. Model P1, which is customer programmable. Has 36 KB of storage expandable to 60 KB. Was announced 13 October 1978. === Back office equipment === ==== 3651 Store Controller ==== Controls data flow inside either a single store or multiple stores and sends retail transactions to a mainframe using a modem. For point of sale it performed functions such as: Automatic price lookup from a master price file Automatic distribution of net sales by up to 54 departments Automatic application of applicable discounts and sales taxes Automatic control of food stamp maximums Check authorization facilities For back office it also helped report preparation such as: store summary individual cashier performance store office reconciliation sales by up to 54 departments Current inquiries for department sales; cashier performance & cash position; store cash position. Inquiries and changes to the master price records and operator authorization control records. Setting the time and date for the internal clock. Running the customer checkouts in training mode. Printing of messages received from the host mainframe Entry of messages to send to the host mainframe Reporting of customer stock returns Updating the system with data received from the mainframe Preparing shelf Labels Basic features include: Each loop attaches up to 63 or 64 terminals depending on traffic volumes and desired response times Has an error and operator panel. There were many models including: A25 Has a 5 MB internal disk. Has 60K of memory expandable to 76KB. Supports one store loop. Attaches to 3275, 3653 and 3663. Announced 19 May 1978, withdrawn 19 February 1981 B25 Same as a A25 with a 9.2 MB internal disk. Announced 19 May 1978 C25 Announced 15 May 1981, withdrawn 15 December 1987 A50 Has a 5 MB internal disk. Announced 5 May 1975. Announced 10 August 1973, withdrawn 15 December 1987 B50 Same as B50 with a 9.2 MB internal disk. Announced 5 May 1975, withdrawn 15 December 1987 A60 Has a 5 MB internal disk. Has an integrated 3669. Attaches up to 24 3663 terminals. Announced 11 October 1973, withdrawn 15 December 1987 B60 Same as A60 with a 9.3 MB internal disk. Announced 17 November 1975, withdrawn 15 December 1987 A75 Has 5 MB internal disk. Has 60K of memory expandable to 124KB. Supports one to three store loops. Attaches to 3275, 3653, 3657, 3784 and 3663 terminals. Announced 19 May 1978 B75 Same as A75 with 9.3 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 C75 Same as A75 with 18.6 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 D75 Same as A75 with 27.9 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 There were also two additional models that could be used instead of the 3651: 7480 Model 1: Has a 18.6 MB internal disk 7480 Model 2: Has a 27.9 MB internal disk ==== 3872 Modem ==== Used to attach to a 3659 for remote loops. Each 3872 can attach three 3659s. ==== 3659 Remote Communication Unit ==== Connected to an IBM 3872 and provides a remote loop for up to 64 point of sale terminals. Announced 10 August 1973, withdrawn 15 December 1987 (Model 2, announced 17 March 1976, withdrawn 20 December 1982) Intended to be used in a back office location like the store manager's office or the data entry office ==== 3275-3 Display Station ==== It is a loop attached display terminal with printer attachment hardware ==== 3784 Line Printer ==== A belt printer for higher-volume end-of-day reporting. The maximum print speed is 155 Ipm using a 48 character set. ==== 3657 Ticket Unit ==== Used to print tickets and encoded labels to attach to store merchandise. It is a loop attached device. It prints the following: 1" by 1" adhesive backed labels with up to 11 characters at 500 tickets per minute. IBM sold these in rolls of 9000 1" x 2" tickets with up to 42 encoded characters and two lines of print of up to 21 characters at 250 tickets per minute. IBM sold these in rolls of 2800 1" x 3" tickets with up to 79 encoded characters and two lines of print of up to 32 characters at 167 tickets per minute. IBM sold these in rolls of 1900 It can also batch read the tickets for validation, separating good tickets from bad ones into two cartridges. Announced 10 August 1973, withdrawn 15 December 1987 ==== 7481 Data Storage Unit ==== This optional unit is used to record transaction data and initialize terminals if the store controller is not available. It uses a built in tape drive to store this data. === Early deployments === The first customer installation of a 3650 was at a Dillard's department store in Little Rock, Arkansas, in late 1974. They placed arou
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FutureMedia

FutureMedia is a program that analyzes the state and future of digital, social, and mobile media. It functions as a collaborative initiative at Georgia Tech and the Georgia Tech Research Institute. FutureMedia consults approximately 500 faculty members working in those fields. == History == In 2019, Future Media expanded into the Direct-To-Consumer market by acquiring Australian watchmaker Oak & Jackal. == Programs == === FutureMedia Fest === The organization most recently hosted FutureMedia Fest 2010, a four-day conference (Oct 4–7, 2010) with a keynote addresses from Michael Jones, the chief technology advocate at Google. The event featured panels, workshops, and technology demonstrations. === FutureMedia Outlook === Contemporaneous with FutureMedia Fest 2010, the organization released the FutureMedia Outlook, an analysis of the future of media, concentrating on six major trends in those fields, including information overload, personalization, data integrity, an expectation of multimedia, augmented reality, and collaborative software.
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Harmonic

In physics, acoustics, and telecommunications, a harmonic is a sinusoidal wave with a frequency that is a positive integer multiple of the fundamental frequency of a periodic signal. The fundamental frequency is also called the 1st harmonic; the other harmonics are known as higher harmonics. As all harmonics are periodic at the fundamental frequency, the sum of harmonics is also periodic at that frequency. The set of harmonics forms a harmonic series. The term is employed in various disciplines, including music, physics, acoustics, electronic power transmission, radio technology, and other fields. For example, if the fundamental frequency is 50 Hz, a common AC power supply frequency, the frequencies of the first three higher harmonics are 100 Hz (2nd harmonic), 150 Hz (3rd harmonic), 200 Hz (4th harmonic) and any addition of waves with these frequencies is periodic at 50 Hz. An n {\displaystyle \ n} th characteristic mode, for n > 1 , {\displaystyle \ n>1\ ,} will have nodes that are not vibrating. For example, the 3rd characteristic mode will have nodes at 1 3 L {\displaystyle \ {\tfrac {1}{3}}\ L\ } and 2 3 L , {\displaystyle \ {\tfrac {2}{3}}\ L\ ,} where L {\displaystyle \ L\ } is the length of the string. In fact, each n {\displaystyle \ n} th characteristic mode, for n {\displaystyle \ n\ } not a multiple of 3, will not have nodes at these points. These other characteristic modes will be vibrating at the positions 1 3 L {\displaystyle \ {\tfrac {1}{3}}\ L\ } and 2 3 L . {\displaystyle \ {\tfrac {2}{3}}\ L~.} If the player gently touches one of these positions, then these other characteristic modes will be suppressed. The tonal harmonics from these other characteristic modes will then also be suppressed. Consequently, the tonal harmonics from the n {\displaystyle \ n} th characteristic characteristic modes, where n {\displaystyle \ n\ } is a multiple of 3, will be made relatively more prominent. In music, harmonics are used on string instruments and wind instruments as a way of producing sound on the instrument, particularly to play higher notes and, with strings, obtain notes that have a unique sound quality or "tone colour". On strings, bowed harmonics have a "glassy", pure tone. On stringed instruments, harmonics are played by touching (but not fully pressing down the string) at an exact point on the string while sounding the string (plucking, bowing, etc.); this allows the harmonic to sound, a pitch which is always higher than the fundamental frequency of the string. == Terminology == Harmonics may be called "overtones", "partials", or "upper partials", and in some music contexts, the terms "harmonic", "overtone" and "partial" are used fairly interchangeably. But more precisely, the term "harmonic" includes all pitches in a harmonic series (including the fundamental frequency) while the term "overtone" only includes pitches above the fundamental. == Characteristics == A whizzing, whistling tonal character, distinguishes all the harmonics both natural and artificial from the firmly stopped intervals; therefore their application in connection with the latter must always be carefully considered. Most acoustic instruments emit complex tones containing many individual partials (component simple tones or sinusoidal waves), but the untrained human ear typically does not perceive those partials as separate phenomena. Rather, a musical note is perceived as one sound, the quality or timbre of that sound being a result of the relative strengths of the individual partials. Many acoustic oscillators, such as the human voice or a bowed violin string, produce complex tones that are more or less periodic, and thus are composed of partials that are nearly matched to the integer multiples of fundamental frequency and therefore resemble the ideal harmonics and are called "harmonic partials" or simply "harmonics" for convenience (although it's not strictly accurate to call a partial a harmonic, the first being actual and the second being theoretical). Oscillators that produce harmonic partials behave somewhat like one-dimensional resonators, and are often long and thin, such as a guitar string or a column of air open at both ends (as with the metallic modern orchestral transverse flute). Wind instruments whose air column is open at only one end, such as trumpets and clarinets, also produce partials resembling harmonics. However they only produce partials matching the odd harmonics—at least in theory. In practical use, no real acoustic instrument behaves as perfectly as the simplified physical models predict; for example, instruments made of non-linearly elastic wood, instead of metal, or strung with gut instead of brass or steel strings, tend to have not-quite-integer partials. Partials whose frequencies are not integer multiples of the fundamental are referred to as inharmonic partials. Some acoustic instruments emit a mix of harmonic and inharmonic partials but still produce an effect on the ear of having a definite fundamental pitch, such as pianos, strings plucked pizzicato, vibraphones, marimbas, and certain pure-sounding bells or chimes. Antique singing bowls are known for producing multiple harmonic partials or multiphonics. Other oscillators, such as cymbals, drum heads, and most percussion instruments, naturally produce an abundance of inharmonic partials and do not imply any particular pitch, and therefore cannot be used melodically or harmonically in the same way other instruments can. Building on of Sethares (2004), dynamic tonality introduces the notion of pseudo-harmonic partials, in which the frequency of each partial is aligned to match the pitch of a corresponding note in a pseudo-just tuning, thereby maximizing the consonance of that pseudo-harmonic timbre with notes of that pseudo-just tuning. == Partials, overtones, and harmonics == An overtone is any partial higher than the lowest partial in a compound tone. The relative strengths and frequency relationships of the component partials determine the timbre of an instrument. The similarity between the terms overtone and partial sometimes leads to their being loosely used interchangeably in a musical context, but they are counted differently, leading to some possible confusion. In the special case of instrumental timbres whose component partials closely match a harmonic series (such as with most strings and winds) rather than being inharmonic partials (such as with most pitched percussion instruments), it is also convenient to call the component partials "harmonics", but not strictly correct, because harmonics are numbered the same even when missing, while partials and overtones are only counted when present. This chart demonstrates how the three types of names (partial, overtone, and harmonic) are counted (assuming that the harmonics are present): In many musical instruments, it is possible to play the upper harmonics without the fundamental note being present. In a simple case (e.g., recorder) this has the effect of making the note go up in pitch by an octave, but in more complex cases many other pitch variations are obtained. In some cases it also changes the timbre of the note. This is part of the normal method of obtaining higher notes in wind instruments, where it is called overblowing. The extended technique of playing multiphonics also produces harmonics. On string instruments it is possible to produce very pure sounding notes, called harmonics or flageolets by string players, which have an eerie quality, as well as being high in pitch. Harmonics may be used to check at a unison the tuning of strings that are not tuned to the unison. For example, lightly fingering the node found halfway down the highest string of a cello produces the same pitch as lightly fingering the node ⁠ 1 / 3 ⁠ of the way down the second highest string. For the human voice see Overtone singing, which uses harmonics. While it is true that electronically produced periodic tones (e.g. square waves or other non-sinusoidal waves) have "harmonics" that are whole number multiples of the fundamental frequency, practical instruments do not all have this characteristic. For example, higher "harmonics" of piano notes are not true harmonics but are "overtones" and can be very sharp, i.e. a higher frequency than given by a pure harmonic series. This is especially true of instruments other than strings, brass, or woodwinds. Examples of these "other" instruments are xylophones, drums, bells, chimes, etc.; not all of their overtone frequencies make a simple whole number ratio with the fundamental frequency. (The fundamental frequency is the reciprocal of the longest time period of the collection of vibrations in some single periodic phenomenon.) == On stringed instruments == Harmonics may be singly produced [on stringed instruments] (1) by varying the point of contact with the bow, or (2) by slightly pressing the string at the nodes, or divisions of its aliquot parts ( 1 2 {\displaystyle {\tfrac {1}{2}}} , 1
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Competition in artificial intelligence

Competition in artificial intelligence refers to the rivalry among companies, research institutions, and governments to develop and deploy the most capable artificial intelligence (AI) systems. The competition spans multiple domains, including large language models (LLMs), autonomous vehicles, robotics, computer vision systems, natural language processing (NLP), and AI-optimized hardware. == Background == Competition in AI is driven by potential economic, strategic, and scientific advantages. Breakthroughs in AI can enhance productivity, enable new products and services, and provide geopolitical leverage. The field has experienced rapid progress since the mid-2010s, particularly in machine learning and artificial neural networks, leading to intense rivalry among leading actors. == Corporate competition == Major technology companies are among the most visible competitors in AI. In the United States, firms such as OpenAI, Google DeepMind, Meta Platforms, Microsoft, Anthropic, and Nvidia compete in building advanced LLMs, generative AI platforms, and AI-optimized graphics processing units (GPUs). In China, companies such as Baidu, Alibaba Group, Tencent, and startups such DeepSeek have become leaders in AI deployment, often with state backing. The "[war for talent]" in AI research has become a defining feature of corporate competition. Leading firms often recruit top AI researchers from rivals, sometimes offering multi-million-dollar compensation packages. == National competition == Governments see leadership in AI as a strategic priority. The United States has funded AI research for military, economic, and societal applications, while China has set a target to lead the world in AI by 2030 through its "New Generation Artificial Intelligence Development Plan". Other nations, including the UK, India, Israel, Russia, South Korea, and members of the European Union, have launched national AI strategies. In February 2026 Anthropic said Chinese companies - DeepSeek, Moonshot AI, and MiniMax - were conducting "distillation attacks" in an attempt to copy their model's capabilities, and warned that business wars were closely tied to geopolitical ones: "foreign labs that illicitly distill American models can remove safeguards, feeding model capabilities into their own military, intelligence, and surveillance systems." == Sectors of competition == === Large language models and chatbots competition === Competition to produce the most capable generative text models, with benchmarks such as MMLU and ARC used to evaluate performance has been on scale since the emergence of AI. These systems leverage deep learning, especially transformer architectures, to understand and generate human-like language. Companies and research groups globally compete to develop chatbots that are more capable, reliable, and context-aware. Among the most well-known chatbots is ChatGPT, developed by OpenAI. Since its public release in 2022, ChatGPT has rapidly gained widespread attention for its ability to engage in coherent and versatile conversations, assist with creative writing, and solve complex problems. In response, technology firms introduced competing chatbots aiming to challenge or surpass ChatGPT's capabilities. Notably, DeepSeek, a Chinese AI company, launched an advanced chatbot integrated with their R1 language model, emphasizing strong natural language understanding and multilingual support. Similarly, Grok, developed by xAI (company), integrates conversational AI into vehicles and digital assistants, combining natural language processing with real-time data for personalized user interaction. These chatbots not only compete in language tasks but also demonstrate strategic reasoning capabilities by playing complex games such as chess and Go. This form of competition is reminiscent of historic AI milestones set by programs such as Deep Blue and AlphaGo. The OpenAI’s ChatGPT has been tested in playing chess at various levels, while DeepSeek’s chatbot showcased its prowess in online chess tournaments in early 2024, winning several matches against human and AI opponents. Grok, leveraging Tesla's vast data infrastructure, has demonstrated real-time strategic decision-making in simulation environments that include chess-like games. The competition pushes rapid innovation, with firms racing to improve chatbot conversational depth, reduce biases, increase factual accuracy, and integrate multimodal inputs like images and videos. At the same time, the competition raises questions about AI safety, ethical use, and the societal impacts of increasingly human-like chatbots. === Autonomous vehicles === Companies such as Waymo, Tesla, and Baidu are racing to deploy safe and reliable self-driving car technology. === AI chips === Rivalry between Nvidia, AMD, Intel, and Huawei in designing processors optimized for AI workloads. === Military applications === Development of AI-enabled drones, surveillance systems, and decision-support tools, with associated ethical debates. == Events == In 2023, OpenAI released GPT-4, prompting competitors such as Google DeepMind to accelerate the release of their own models, including Gemini. In 2024, Chinese AI company DeepSeek launched the R1 model, leading OpenAI to release an open-source system, GPT-OSS, as a strategic countermeasure. In 2022, Tesla and Waymo both expanded autonomous taxi services in U.S. cities, competing for regulatory approval and public trust. The U.S. Department of Defense's Project Maven and China's AI-enabled surveillance programs have been cited as examples of military AI rivalry. In 2025, Microsoft hired several senior engineers from Google DeepMind, highlighting the ongoing "talent poaching" competition in the AI sector. == Risks and concerns == Critics warn that unrestrained competition in AI can undermine safety, ethics, and governance. Concerns include the proliferation of biased or unsafe models, escalation in autonomous weapons, and reduced cooperation on safety standards.
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Haul video

A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.
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Hydration (web development)

In web development, hydration or rehydration is a technique in which client-side JavaScript converts a web page that is static from the perspective of the web browser, delivered either through static rendering or server-side rendering, into a dynamic web page by attaching event handlers to the HTML elements in the DOM. Because the HTML is pre-rendered on a server, this allows for a fast "first contentful paint" (when useful data is first displayed to the user), but there is a period of time afterward where the page appears to be fully loaded and interactive, but is not until the client-side JavaScript is executed and event handlers have been attached. Frameworks that use hydration include Next.js and Nuxt. React v16.0 introduced a "hydrate" function, which hydrates an element, in its API. == Variations == === Streaming server-side rendering === Streaming server-side rendering allows one to send HTML in chunks that the browser can progressively render as it is received. This can provide a fast first paint and first contentful paint as HTML markup arrives to users faster. === Progressive rehydration === In progressive rehydration, individual pieces of a server-rendered application are “booted up” over time, rather than the current common approach of initializing the entire application at once. This can help reduce the amount of JavaScript required to make pages interactive, since client-side upgrading of low priority parts of the page can be deferred to prevent blocking the main thread. It can also help avoid one of the most common server-side rendering rehydration pitfalls, where a server-rendered DOM tree gets destroyed and then immediately rebuilt – most often because the initial synchronous client-side render required data that wasn't quite ready, perhaps awaiting Promise resolution. === Partial rehydration === Partial rehydration has proven difficult to implement. This approach is an extension of the idea of progressive rehydration, where the individual pieces (components/views/trees) to be progressively rehydrated are analyzed and those with little interactivity or no reactivity are identified. For each of these mostly-static parts, the corresponding JavaScript code is then transformed into inert references and decorative functionality, reducing their client-side footprint to near-zero. The partial hydration approach comes with its own issues and compromises. It poses some interesting challenges for caching, and client-side navigation means it cannot be assumed that server-rendered HTML for inert parts of the application will be available without a full page load. One framework that supports partial rehydration is Elder.js, which is based on Svelte. === Trisomorphic rendering === Trisomorphic rendering is a technique which uses streaming server-side rendering for initial/non-JavaScript navigations, and then uses service workers to take on rendering of HTML for navigations after it has been installed. This can keep cached components and templates up to date and enables SPA-style navigations for rendering new views in the same session. This approach works best when one can share the same templating and routing code between the server, client page, and service worker.
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Acquisition of DirecTV by AT&T

AT&T Inc. announced an agreement with the DirecTV Group on May 18, 2014, to acquire the company for $48.5 billion in a joint cash-stock transaction and assumed debts of $18.6 billion for a total offer of $67.1 billion. Due to stalling growth in the wireless sector, AT&T began diversifying into mass media to expand its consumer offerings. After regulatory agencies approved the purchase on July 24, 2015, AT&T briefly became the largest Pay-TV provider. DirecTV was brought under AT&T's communication segment and DirecTV Now was launched on November 30, 2016, as an alternative to cord-cutting. In the years following the purchase, DirecTV lost millions of subscribers across its satellite and streaming services and by 2019, calls grew for AT&T to divest itself off the business. Initially, AT&T rejected these calls and defended the acquisition, but by February 2021, it reached a deal with TPG Inc. to transfer ownership of DirecTV. Under the terms of the agreement, AT&T would retain a 70% majority stake in DirecTV but would no longer oversee its daily operations. The deal was finalized by August 2, 2021, with AT&T receiving $7.1 billion. By July 3, 2025, AT&T sold its majority stake to TPG, ending any ties of involvement. == Background and Development == === AT&T's history === The company to bear the name "AT&T" was founded on March 3, 1885, as American Telephone and Telegraph Company (or AT&T Corporation) by Theodore Newton Vail as a long-distance subsidiary of the Bell Telephone Company. By December 1899, the Bell Telephone's assets were transferred to AT&T, with the latter gaining control of the Bell System, a regional network of local telecom companies. Theodore Vail became AT&T's President in 1907 and under his leadership, AT&T gained a monopoly over the telephone sector in the United States. This near century dominance earned AT&T the nickname of "Ma Bell." In 1974, the U.S. Department of Justice sued AT&T on accounts of antitrust violations. AT&T challenged the lawsuit, but in 1982, it reached a settlement with the DOJ to break apart its Bell System monopoly into seven regional companies. On January 1, 1984, the Bell System came to an end and led to a reshaped telecom industry. One of these regional companies, Southwestern Bell, emerged as the smallest, but after the passage of the 1996 Telecom Act, deregulated telecom rules allowed SBC to become a major telecom company. AT&T briefly became the largest cable and broadband company by the end of the 20th Century, but later deconsolidated to exit those industries. In 2005, SBC acquired its former parent, AT&T, and took on its branding as AT&T Inc, while retaining its previous business history. The newly reincorporated AT&T acquired BellSouth in 2006 and reconstituted much of its former Bell System. === DirecTV's history === == Acquisition Timeline == == Managing DirecTV == == Divestment and Spinoff ==
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ELMo

ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. It was created by researchers at the Allen Institute for Artificial Intelligence, and University of Washington and first released in February 2018. It is a bidirectional LSTM which takes character-level as inputs and produces word-level embeddings, trained on a corpus of about 30 million sentences and 1 billion words. The architecture of ELMo accomplishes a contextual understanding of tokens. Deep contextualized word representation is useful for many natural language processing tasks, such as coreference resolution and polysemy resolution. ELMo was historically important as a pioneer of self-supervised generative pretraining followed by fine-tuning, where a large model is trained to reproduce a large corpus, then the large model is augmented with additional task-specific weights and fine-tuned on supervised task data. It was an instrumental step in the evolution towards transformer-based language modelling. == Architecture == ELMo is a multilayered bidirectional LSTM on top of a token embedding layer. The output of all LSTMs concatenated together consists of the token embedding. The input text sequence is first mapped by an embedding layer into a sequence of vectors. Then two parts are run in parallel over it. The forward part is a 2-layered LSTM with 4096 units and 512 dimension projections, and a residual connection from the first to second layer. The backward part has the same architecture, but processes the sequence back-to-front. The outputs from all 5 components (embedding layer, two forward LSTM layers, and two backward LSTM layers) are concatenated and multiplied by a linear matrix ("projection matrix") to produce a 512-dimensional representation per input token. ELMo was pretrained on a text corpus of 1 billion words. The forward part is trained by repeatedly predicting the next token, and the backward part is trained by repeatedly predicting the previous token. After the ELMo model is pretrained, its parameters are frozen, except for the projection matrix, which can be fine-tuned to minimize loss on specific language tasks. This is an early example of the pretraining-fine-tune paradigm. The original paper demonstrated this by improving state of the art on six benchmark NLP tasks. === Contextual word representation === The architecture of ELMo accomplishes a contextual understanding of tokens. For example, the first forward LSTM of ELMo would process each input token in the context of all previous tokens, and the first backward LSTM would process each token in the context of all subsequent tokens. The second forward LSTM would then incorporate those to further contextualize each token. Deep contextualized word representation is useful for many natural language processing tasks, such as coreference resolution and polysemy resolution. For example, consider the sentenceShe went to the bank to withdraw money.In order to represent the token "bank", the model must resolve its polysemy in context. The first forward LSTM would process "bank" in the context of "She went to the", which would allow it to represent the word to be a location that the subject is going towards. The first backward LSTM would process "bank" in the context of "to withdraw money", which would allow it to disambiguate the word as referring to a financial institution. The second forward LSTM can then process "bank" using the representation vector provided by the first backward LSTM, thus allowing it to represent it to be a financial institution that the subject is going towards. == Historical context == ELMo is one link in a historical evolution of language modelling. Consider a simple problem of document classification, where we want to assign a label (e.g., "spam", "not spam", "politics", "sports") to a given piece of text. The simplest approach is the "bag of words" approach, where each word in the document is treated independently, and its frequency is used as a feature for classification. This was computationally cheap but ignored the order of words and their context within the sentence. GloVe and Word2Vec built upon this by learning fixed vector representations (embeddings) for words based on their co-occurrence patterns in large text corpora. Like BERT (but unlike "bag of words" such as Word2Vec and GloVe), ELMo word embeddings are context-sensitive, producing different representations for words that share the same spelling. It was trained on a corpus of about 30 million sentences and 1 billion words. Previously, bidirectional LSTM was used for contextualized word representation. ELMo applied the idea to a large scale, achieving state of the art performance. After the 2017 publication of Transformer architecture, the architecture of ELMo was changed from a multilayered bidirectional LSTM to a Transformer encoder, giving rise to BERT. BERT has a similar pretrain-fine-tune workflow, but uses a Transformer with implications for more parallelizable training.
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FactorDaily

FactorDaily is an Indian digital media publication founded in 2016 by Pankaj Mishra and Jayadevan PK. Mishra was formerly an Editor at TechCrunch and the Economic Times. The digital publication was launched with an intent to produce stories on the impact of technology on life in India. == History == FactorDaily began publishing in May 2016, with daily reported stories on technology, culture and life in India. Prior to its launch, the company had raised $1 million in seed funding from Accel India, Blume Ventures, Girish Mathrubootham of Freshdesk, Vijay Shekhar Sharma of PayTm, and Jay Vijayan of Tekion. Josey Puliyenthuruthel John, formerly Managing Editor at Business Today and National Corporate Editor at Mint, later joined the company as a Consulting Editor. In January 2017, FactorDaily launched its first Podcast called The Outliers. The inaugural episode featured a conversation with Manish Sharma of Printo on his journey starting up. == Awards == The FactorDaily team won the Bengaluru Editors Lab 2017, a journalism hackathon organised by the Global Editors Network (GEN). The story titled "India has 3,800 psychiatrists for 1.2bn people. Can tech step in to manage mental health?" won the first prize in the online category of the fifth Schizophrenia Research Foundation’s (SCARF) ‘Media for Mental Health’ awards. The story titled 'The dark hand of tech that stokes sex trafficking in India', won the Stop Slavery media Awards by the Thomson Reuters Foundation for the year 2020.
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Blocking of Twitter in Nigeria

Twitter was blocked in Nigeria from 5 June 2021 to 13 January 2022. The government imposed a ban on the social network after it deleted tweets made by, and temporarily suspended, the Nigerian president Muhammadu Buhari, warning the southeastern people of Nigeria, predominantly Igbo people, of a potential repeat of the 1967 Nigerian Civil War due to the ongoing insurgency in Southeastern Nigeria. The Nigerian government claimed that the deletion of the president's tweets factored into their decision, but it was ultimately based on "a litany of problems with the social media platform in Nigeria, where misinformation and fake news spread through it have had real world violent consequences", citing the persistent use of the platform for activities that are capable of undermining Nigeria's corporate existence. In January 2022, Nigeria lifted its blocking of Twitter after the platform agreed to establish a legal entity within the country sometime in the first quarter of 2022. == Background == On 1 June 2021, Nigerian President Muhammadu Buhari posted a tweet threatening a crackdown on regional separatists "in the language they understand". The next day, Twitter deleted the tweet, claiming it was in violation of Twitter rules, but gave no further details. Nigeria's Information Minister Lai Mohammed said that Twitter's actions were part of an unfair double standard, as Twitter had not banned incitement tweets from other groups. During the Nigerian Civil War a majority of deaths resulted from the blockade of Biafra which caused the deaths of millions of civilians from starvation, a fact that was not alluded to in the tweet. The Nigerian government has long held concerns over the use of Twitter in the country. The ongoing local End SARS protest began on Twitter and got amplified in 2020 when it had 48 million tweets in ten days. Buhari's government floated the idea of social media regulation on different occasions prior to banning Twitter. Attempts to pass an anti-social media bill in the past have failed majorly due to massive outcry on Twitter. Days before the ban, the country's minister of information called Twitter's activities in Nigeria suspicious, citing its influence on the End SARS protests. == Aftermath == Three days after Twitter was suspended, it was reported that the move had cost the country over 6 billion naira and would also contribute to the worsening unemployment in the country. ExpressVPN reported an over 200 percent increase in web traffic and searches for VPN spiked across the country. In response, Nigeria's Minister of Justice and Attorney General of the Federation Abubakar Malami at first openly threatened to prosecute citizens who bypass the ban using a VPN but then denied saying so after a screenshot of a Twitter deactivation notification he shared on Facebook showed a VPN logo. Nigeria's cultural minister Lai Mohammed stated the ban would be lifted once Twitter submitted to locally licensing, registration and conditions. "It will be licensed by the broadcasting commission, and must agree not to allow its platform to be used by those who are promoting activities that are inimical to the corporate existence of Nigeria." In late June 2021, Twitter announced it would enter talks with the Nigerian government over the platform's suspension. The talks began in July 2021. On 15 September 2021, Mohammed said the Nigerian government will lift the ban on Twitter in a "few days." The Minister said Twitter gave a progress report of their talks with them, adding that it has been productive and quite respectful. On 1 October 2021, President Muhammadu Buhari in his Independence Day broadcast said Twitter must meet the Nigerian government's five conditions before the suspension of the social media platform will be lifted. The conditions are: Respect for national security and cohesion; registration, physical presence and representation in Nigeria; fair taxation; dispute resolution; local content. == Reactions == The ban was condemned by Amnesty International, the British, Canadian and Swedish diplomatic missions to Nigeria, as well as the United States and the European Union in a joint statement. Two domestic organizations, the Socio-Economic Rights and Accountability Project (SERAP) and the Nigerian Bar Association, indicated intent to challenge the ban in court. Twitter itself called the ban "deeply concerning". Former U.S. President Donald Trump, who was permanently suspended from Twitter following the United States Capitol attack in January, praised the ban, stating "Congratulations to the country of Nigeria, who just banned Twitter because they banned their President", and also called on other countries to ban Twitter and Facebook due to "not allowing free and open speech." == Lifting of the ban == On 12 January 2022, the Nigerian Government lifted the ban after Twitter agreed to pay an "applicable tax" and establish "a legal entity in Nigeria during the first quarter of 2022".
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Haul video

A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.
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Interactions Corporation

Interactions LLC (also known as Interactions Corporation) is an American software company that develops voice and text-based virtual assistant applications for customer-service contact centers. Since September 2025, it has been a subsidiary of SoundHound AI. == History == Interactions was founded in 2004. In July 2011, the company announced a $12 million venture-capital funding round led by Sigma Partners. In November 2014, AT&T sold its "Watson" speech recognition platform and related patents to Interactions in exchange for equity. In May 2017, Interactions acquired the social media customer-engagement company Digital Roots; financial terms were not disclosed. On September 3, 2025, SoundHound AI completed its acquisition of Interactions Corporation, with the acquired company becoming a wholly owned subsidiary. == Products and services == Interactions' products have been described as automated voice portals and intelligent virtual assistants used for customer-service tasks. In 2011, Humana expanded the use of an Interactions voice portal for Medicare Part D enrollment.
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Web series

A web series, also known as a short-form series or web show, is a collection of short scripted or unscripted online videos released on the Internet (i.e., World Wide Web), generally in episodic form. A single installment of a web series can be called a webisode or an episode. The scale of a web series is small, and a typical episode can be anywhere from 3 to 15 minutes long (though some may run up to 20 minutes). Web series first emerged in the mid-1990s and became more prominent in the early 2000s. Web series are distributed online on video-sharing websites and apps, such as YouTube, Vimeo, and TikTok, and can be watched on devices such as smartphones, tablets, desktops, laptops, and Smart TVs (or television sets connected to the Internet with a media streaming device). They can also be released on social media platforms. Because of the nature of the Internet, a web series may be interactive and immersive. Web series are classified as new media. Web series are different from streaming television series, as the latter are designed to be watched on streaming platforms such as Netflix, Amazon Prime Video, or Hotstar, with the streaming services offering original productions made for and by them, as well as acquiring the rights to distribute licensed content. The length of a streaming television series episode is 30 to 60 minutes (runtimes can also be longer). Although the design of a web series can be similar to that of a television series, its development and production do not entail the same financial investment required for a television series. The popularity of some web series, however, has led to them being optioned for television. Web series differ from short-form content in that the latter are vertical videos specifically designed for smartphone viewing and intended for fast-paced consumption, with runtimes typically ranging from less than one minute to three minutes. There are film festivals for web series, like Webfest Berlin, NYC Web Fest, LA Web Fest, and Vancouver Web Fest. Awards organizations have also been established to celebrate excellence in web series, such as the Streamys, Webbys, IAWTV Awards, and Indie Series Awards. Most major award ceremonies have also created web series and digital media award categories, including the Emmy Awards and the Canadian Screen Awards. == History == === 1990s === In April 1995, "Global Village Idiots", an episode of the reality-based program Rox on public access cable television in Bloomington, Indiana, was uploaded to the Internet, making Rox the first show distributed via the web. The same year, Scott Zakarin created The Spot, an episodic online story that integrated photos, videos, and blogs into the storyline. Likened to Melrose Place-on-the-Web, The Spot featured a rotating cast of characters playing trendy twenty-somethings who rented rooms in a fabled Santa Monica, California beach house called "The Spot". The Spot earned Infoseek's "Cool Site of the Year," an award which later became the Webby. In January 1999, Showtime licensed the animated sci-fi web series WhirlGirl, making it the first independently produced web series licensed by a national television network. In February 1999, the show premiered simultaneously on Showtime and online. The character occasionally appeared on Showtime, for example, hosting a "Lethal Ladies" programming block, but spent most of her time online, appearing in 100 webisodes. === 2000s === As broadband bandwidth increased in speed and availability, delivering high-quality video over the Internet became a reality. In the early 2000s, the Japanese anime industry began broadcasting original net animation (ONA), a type of original video animation (OVA) series, on the Internet. Early examples of the ONA series include Infinite Ryvius: Illusion (2000), Ajimu (2001), and Mahou Yuugi (2001). In 2000, The Brothers Chaps launched the Adobe Flash-created web series Homestar Runner. After being put on hiatus in 2010, it returned in 2014. In 2002, Matt Jolly (better known as "Krinkels") released the first episode of Madness Combat to Newgrounds. The show is still ongoing, with the latest episode "Madness Combat 12: Contravention" released on Twitch in September 2024. In 2003, Microsoft launched MSN Video, offering NBC-related content. Its web series, Weird TV 2000, a spin-off of the syndicated television series Weird TV, featured dozens of shorts, comedy sketches, and mini-documentaries produced exclusively for MSN Video. The video-sharing site YouTube was launched in early 2005, allowing users to share television programs. YouTube co-founder Jawed Karim said the inspiration for YouTube first came from Janet Jackson's role in the 2004 Super Bowl incident, when her breast was exposed during her performance, and later from the 2004 Indian Ocean tsunami. Karim could not easily find video clips of either event online, which led to the idea of a video-sharing site. From 2003 to 2006, many independent web series gained significant popularity, most notably the science fiction series Red vs. Blue by Rooster Teeth. The series was distributed independently via online portals YouTube and Revver, as well as the Rooster Teeth website, acquiring over 100 million social media views during its run. (Rooster Teeth would eventually create the computer-animated web series RWBY in 2013.) In 2004, the adult-animated series Salad Fingers was created, which amassed a cult following. The comedy show The Burg, hailed as the internet's first sitcom and starring Kelli Giddish and Lindsey Broad, rapidly gained an audience and press attention before its creators signed a creation deal with Michael Eisner. The drama Sam Has 7 Friends, which ran in the summer and fall of 2006, was nominated for a Daytime Emmy Award and was temporarily removed from the Internet when it was also acquired by Eisner. In 2004–2005, Spanish producer Pedro Alonso Pablos recorded a series of video interviews featuring actors and directors such as Guillermo del Toro, Santiago Segura, Álex de la Iglesia, and Keanu Reeves, which were distributed through his own website. lonelygirl15, California Heaven, "The Burg", and SamHas7Friends also gained popularity during this time, acquiring audiences in the millions. (Science fiction thriller lonelygirl15 was so successful that it secured a sponsorship deal with Neutrogena in 2007.) In 2004, Stewart St. John, executive producer and head writer of 1990s webisodies The Spot, revived the brand for online audiences as The Spot (2.0), with a new cast, and as a separate soap opera on Sprint PCS Vision-enabled cell phones, creating the first American mobile phone series. St. John and partner Todd Fisher produced over 2,500 daily videos of the mobile soap, driving story lines across platforms to its web counterpart. In 2007, the creators of lonelygirl15 followed up on the show's success with KateModern, a comedy-drama series that debuted on social network Bebo, and took place in the same fictional universe as their previous show. Big Fantastic created and produced the soap opera Prom Queen, financed and distributed by Michael Eisner's production firm Vuguru, and debuted the series on MySpace. Vuguru partnered with Mark Cuban's channel HDNet to release All-for-nots, a mockumentary series by The Burg creators Kathleen Grace and Thom Woodley, which debuted at the SXSW Festival in 2008. These web series highlighted interactivity with the audience in addition to the narrative on relatively low budgets. In contrast, the eight-episode show Sanctuary, starring actor/producer Amanda Tapping, cost $4.3 million to produce. Both Sanctuary and Prom Queen were nominated for a Daytime Emmy Award. Award-winning producer/director Marshall Herskovitz created the drama Quarterlife, which debuted on MySpace and was later distributed on NBC. In 2008, major television studios began releasing web series, such as the ABC comedy show Squeegies, the NBC sci-fi show Gemini Division, and the Bravo reality series The Malan Show. Warner Bros. relaunched The WB as an online network beginning with original mystery web series, Sorority Forever, created and produced by Big Fantastic and executive produced by McG. Meanwhile, MTV announced a new original web series created by Craig Brewer, $5 Cover, that brought together the indie music world and new media expansion. Joss Whedon created, produced, and self-financed musical comedy-drama Dr. Horrible's Sing-Along Blog starring Neil Patrick Harris and Felicia Day. Big Fantastic wrote and produced Foreign Body, a mystery web series that served as a prequel to Robin Cook's novel of the same name. Beckett and Goodfried founded a new Internet studio, EQAL, and produced a spin-off of lonelygirl15 titled LG15: The Resistance. The mainstream press began to provide coverage. In the United Kingdom, KateModern ended its run on Bebo. Bebo also hosted a six-month-long reality travel show, The Gap Year, produced by Endemol UK, and produced an interactive sci-fi drama Kirill for
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Microformat

Microformats (μF) are predefined HTML markup (like HTML classes) created to serve as descriptive and consistent metadata about elements, designating them as representing a certain type of data (such as contact information, geographic coordinates, events, products, recipes, etc.). They allow software to process the information reliably by having set classes refer to a specific type of data rather than being arbitrary. Microformats emerged around 2005 and were predominantly designed for use by search engines, web syndication and aggregators such as RSS. Google confirmed in 2020 that it still parses microformats for use in content indexing. Microformats are referenced in several W3C social web specifications, including IndieAuth and Webmention. Although the content of web pages has been capable of some "automated processing" since the inception of the web, such processing is difficult because the markup elements used to display information on the web do not describe what the information means. Microformats can bridge this gap by attaching semantics, and thereby obviating other, more complicated, methods of automated processing, such as natural language processing or screen scraping. The use, adoption and processing of microformats enables data items to be indexed, searched for, saved or cross-referenced, so that information can be reused or combined. As of 2013, microformats allow the encoding and extraction of event details, contact information, social relationships and similar information. Microformats2, abbreviated as mf2, is the updated version of microformats. Mf2 provides an easier way of interpreting HTML structured syntax and vocabularies than the earlier ways that made use of RDFa and microdata. == Background == Microformats emerged around 2005 as part of a grassroots movement to make recognizable data items (such as events, contact details or geographical locations) capable of automated processing by software, as well as directly readable by end-users. Link-based microformats emerged first. These include vote links that express opinions of the linked page, which search engines can tally into instant polls. CommerceNet, a nonprofit organization that promotes e-commerce on the Internet, has helped sponsor and promote the technology and support the microformats community in various ways. CommerceNet also helped co-found the Microformats.org community site. Neither CommerceNet nor Microformats.org operates as a standards body. The microformats community functions through an open wiki, a mailing list, and an Internet relay chat (IRC) channel. Most of the existing microformats originated at the Microformats.org wiki and the associated mailing list by a process of gathering examples of web-publishing behaviour, then codifying it. Some other microformats (such as rel=nofollow and unAPI) have been proposed, or developed, elsewhere. == Technical overview == XHTML and HTML standards allow for the embedding and encoding of semantics within the attributes of markup elements. Microformats take advantage of these standards by indicating the presence of metadata using the following attributes: class Classname rel relationship, description of the target address in an anchor-element (...) rev reverse relationship, description of the referenced document (in one case, otherwise deprecated in microformats) For example, in the text "The birds roosted at 52.48, -1.89" is a pair of numbers which may be understood, from their context, to be a set of geographic coordinates. With wrapping in spans (or other HTML elements) with specific class names (in this case geo, latitude and longitude, all part of the geo microformat specification): Software agents can recognize exactly what each value represents and can then perform a variety of tasks such as indexing, locating it on a map and exporting it to a GPS device. === Examples === In this example, the contact information is presented as follows: With hCard microformat markup, that becomes: Here, the formatted name (fn), organisation (org), telephone number (tel) and web address (url) have been identified using specific class names and the whole thing is wrapped in class="vcard", which indicates that the other classes form an hCard (short for "HTML vCard") and are not merely coincidentally named. Other, optional, hCard classes also exist. Software, such as browser plug-ins, can now extract the information, and transfer it to other applications, such as an address book. == Specific microformats == Several microformats have been developed to enable semantic markup of particular types of information. However, only hCard and hCalendar have been ratified, the others remaining as drafts: hAtom (superseded by h-entry and h-feed) – for marking up Atom feeds from within standard HTML hCalendar – for events hCard – for contact information; includes: adr – for postal addresses geo – for geographical coordinates (latitude, longitude) hMedia – for audio/video content hAudio – for audio content hNews – for news content hProduct – for products hRecipe – for recipes and foodstuffs. hReview – for reviews rel-directory – for distributed directory creation and inclusion rel-enclosure – for multimedia attachments to web pages rel-license – specification of copyright license rel-nofollow, an attempt to discourage third-party content spam (e.g. spam in blogs) rel-tag – for decentralized tagging (Folksonomy) XHTML Friends Network (XFN) – for social relationships XOXO – for lists and outlines == Uses == Using microformats within HTML code provides additional formatting and semantic data that applications can use. For example, applications such as web crawlers can collect data about online resources, or desktop applications such as e-mail clients or scheduling software can compile details. The use of microformats can also facilitate "mash ups" such as exporting all of the geographical locations on a web page into (for example) Google Maps to visualize them spatially. Several browser extensions, such as Operator for Firefox and Oomph for Internet Explorer, provide the ability to detect microformats within an HTML document. When hCard or hCalendar are involved, such browser extensions allow microformats to be exported into formats compatible with contact management and calendar utilities, such as Microsoft Outlook. When dealing with geographical coordinates, they allow the location to be sent to applications such as Google Maps. Yahoo! Query Language can be used to extract microformats from web pages. On 12 May 2009 Google announced that they would be parsing the hCard, hReview and hProduct microformats, and using them to populate search result pages. They subsequently extended this in 2010 to use hCalendar for events and hRecipe for cookery recipes. Similarly, microformats are also processed by Bing and Yahoo!. As of late 2010, these are the world's top three search engines. Microsoft said in 2006 that they needed to incorporate microformats into upcoming projects, as did other software companies. Alex Faaborg summarizes the arguments for putting the responsibility for microformat user interfaces in the web browser rather than making more complicated HTML: Only the web browser knows what applications are accessible to the user and what the user's preferences are It lowers the barrier to entry for web site developers if they only need to do the markup and not handle "appearance" or "action" issues Retains backwards compatibility with web browsers that do not support microformats The web browser presents a single point of entry from the web to the user's computer, which simplifies security issues == Evaluation == Various commentators have offered review and discussion on the design principles and practical aspects of microformats. Microformats have been compared to other approaches that seek to serve the same or similar purpose. As of 2007, there had been some criticism of one, or all, microformats. The spread and use of microformats was being advocated as of 2007. Opera Software CTO and CSS creator Håkon Wium Lie said in 2005 "We will also see a bunch of microformats being developed, and that’s how the semantic web will be built, I believe." However, in August 2008 Toby Inkster, author of the "Swignition" (formerly "Cognition") microformat parsing service, pointed out that no new microformat specifications had been published since 2005. === Design principles === Computer scientist and entrepreneur, Rohit Khare stated that reduce, reuse, and recycle is "shorthand for several design principles" that motivated the development and practices behind microformats. These aspects can be summarized as follows: Reduce: favor the simplest solutions and focus attention on specific problems; Reuse: work from experience and favor examples of current practice; Recycle: encourage modularity and the ability to embed, valid XHTML can be reused in blog posts, RSS feeds, and anywhere else you can access the web. === Accessibi
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