AI Chatbot That Sends Pictures

AI Chatbot That Sends Pictures — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Microsoft Sway

Microsoft Sway is a presentation program and is part of the Microsoft 365 family of products. Sway was offered for general release by Microsoft in August 2015. It allows users who have a Microsoft account to combine text and media to create a presentable website. Users can pull content locally from the device in use, or from internet sources such as Bing, Facebook, OneDrive, and YouTube. Sway is distinguished from Microsoft FrontPage and Microsoft Expression Web – unrelated web design programs previously developed by Microsoft – in that Sway includes a method for hosting sites. Sway sites are stored on Microsoft's servers and are tied to the user's Microsoft account. They can be viewed and edited from any web browser through Office on the web. There is no offline editing or viewing function, but sites can be accessed using the app for Windows, and formerly iOS. == History == Sway was developed internally by Microsoft. In late 2014, the company announced an invite-only preview version of Sway and announced that Sway would not require an Office 365 subscription. An iOS app was released as a preview on 31 October 2014, but was discontinued on 17 December 2018 due to low usage. As of July 17, 2021, the Sway iOS app's discontinuance in 2018 was the last piece of news posted in the Sway tech blog. The Sway feature blog has not received an update since April 2017. The Microsoft Office Roadmap did not include any items related to Sway ever since. The iOS application is no longer under active development, and is not available for download. Since 2023, Microsoft has been consolidating the domains of its Microsoft 365 apps and services under cloud.microsoft. By 2025, the vast majority of services, including Sway, have already migrated to the cloud.microsoft domain. == Features == Users are able to add content from various sources into their Sway presentations. Some of the integrated services are owned by Microsoft, including OneNote, Bing, and other Sway sites. The program also provides native integration with other services, including YouTube, Facebook, Twitter, Mixcloud, and Infogram.
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Joint constraints

Joint constraints are rotational constraints on the joints of an artificial system. They are used in an inverse kinematics chain, in fields including 3D animation or robotics. Joint constraints can be implemented in a number of ways, but the most common method is to limit rotation about the X, Y and Z axis independently. An elbow, for instance, could be represented by limiting rotation on X and Z axis to 0 degrees, and constraining the Y-axis rotation to 130 degrees. To simulate joint constraints more accurately, dot-products can be used with an independent axis to repulse the child bones orientation from the unreachable axis. Limiting the orientation of the child bone to a border of vectors tangent to the surface of the joint, repulsing the child bone away from the border, can also be useful in the precise restriction of shoulder movement.
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Webull

Webull Corporation, often stylized as simply Webull, is a U.S.-based financial services holding company headquartered in St. Petersburg, Florida. It owns and operates the Webull electronic trading platform for self-directed retail investors. Depending on jurisdiction, the Webull platform offers trading in stocks, exchange-traded funds (ETFs), options, margin, bonds, cryptocurrency and futures, as well as market-data tools. Webull began operations in 2016 under Hunan Fumi Information Technology, a China-based financial technology company founded by Wang Anquan. It launched U.S. brokerage services through Webull Financial LLC in 2018 and expanded during the retail-trading boom of 2020 and 2021. In April 2025, Webull became a publicly traded company on the Nasdaq through a merger with special-purpose acquisition company SK Growth Opportunities Corporation. The company's U.S. brokerage revenue relies substantially on payment for order flow, with options trading accounting for the larger share of its order-flow rebates in 2025. Webull has faced regulatory actions related to options customer approvals, complaint handling, suspicious activity reporting, social-media marketing and customer disclosures. It has also faced scrutiny from U.S. lawmakers and state officials over its historical and operational ties to China and the handling of U.S. customer data. == History == === Founding === Webull was founded in 2016 under Hunan Fumi Information Technology, a China-based financial technology company, by Wang Anquan, a former employee of Alibaba Group and Xiaomi. Hunan Fumi Information Technology received backing from Xiaomi, Shunwei Capital, and other investors in China. Fumi Technology was a Hunan-based fintech start-up incubated by Xiaomi and raised about CNY200 million (approximately US$30 million) in a Series B financing round in 2018. On May 24, 2017, Webull Financial LLC was established as a Delaware limited liability company. It began offering brokerage services in the United States in May 2018. Wang hired Anthony Denier as CEO of the U.S. brokerage that year and the two mapped out their strategy on napkins at a Mexican restaurant in New York City. Webull Corporation was incorporated in the Cayman Islands in September 2019 as the group's holding company. === Retail trading boom === In May 2020, the company received SEC approval to launch a robo-advisor on its platform. By August 2020, the platform had over 11 million registered users, and in October 2020, it had 750,000 daily active users. Webull introduced options trading in 2020 and later added cryptocurrency trading through a separate digital-asset business. In November 2020, Webull began supporting cryptocurrency transactions. In December 2020, Webull launched trading services in Hong Kong. During the GameStop short squeeze in January 2021, Webull gained attention as some retail traders looked for alternatives to Robinhood. On January 27, 2021, Webull recorded its highest-ever number of active daily users, at 952,000, and the Webull app was downloaded across the Apple App and Google Play stores an estimated 100,000 times. That week, approximately 1.2 million people downloaded the Webull mobile app, which the company reported as a 1,548% week-over-week increase. On January 28, 2021, Webull was directed by its clearing house to temporarily halt buy orders for stocks affected by the GameStop short squeeze. In June 2021, Webull was reported to be considering a U.S. initial public offering that could raise up to $400 million. === Restructuring and expansion === Webull restructured its China-related corporate arrangements in 2022 and later stated that Hunan Fumi was no longer affiliated with the group. In 2022 and 2023, Webull expanded in several non-U.S. markets, including Singapore, Australia, South Africa, Japan, the United Kingdom and Indonesia. In June 2023, Webull moved cryptocurrency trading to a separate app called Webull Pay. By the end of 2023, Webull had 4.3 million funded accounts and US$8.2 billion in customer assets. In January 2024, Anthony Denier was promoted to group president of Webull Corporation. In November 2024, Webull launched overnight, or extended-hours, trading, expanding the trading window of U.S. stocks for users inside and outside the United States. === SPAC merger and Nasdaq listing === On February 28, 2024, Webull agreed to go public through a business combination with SK Growth Opportunities Corporation (NASDAQ: SKGR), a special-purpose acquisition company, in a deal that valued the company at approximately US$7.3 billion. The proposed valuation drew scrutiny because of Webull's limited financial disclosure at announcement, reliance on payment for order flow and small expected public float. SK Growth shareholders approved the business combination on March 30, 2025, and the transaction closed on April 10, 2025. Webull's Class A ordinary shares and warrants began trading on the Nasdaq on April 11, 2025 under the ticker symbols BULL and BULLW (incentive warrants traded under BULLZ until their redemption in June 2025). The merger brought Webull to the public market but generated little cash for the company: after shareholder redemptions, Webull disclosed net proceeds of US$430,066 from the transaction. After the listing, Webull's shares experienced extreme volatility, rising as much as 500% to US$79.56 on April 14, 2025, after closing at US$13.25 on the prior trading day. The initial post-listing surge increased the value of Webull holdings owned by earlier investors, including RIT Capital Partners, which had first invested in Webull in 2021. In April 2026, after Webull's shares had fallen about 70% over the previous year, the company authorized a US$100 million share repurchase program. == Business model and financials == Webull provides a self-directed electronic trading platform available through mobile, desktop and web applications. Depending on jurisdiction, the platform offers trading in stocks, exchange-traded funds, options, margin, futures, fixed income products, cryptocurrency, cash management features and market data tools. In the United States, Webull Financial LLC is a registered broker-dealer and member of FINRA and the Securities Investor Protection Corporation, while Webull operates in other markets through locally licensed brokerage subsidiaries. Webull operates a commission-free or low-cost brokerage model for self-directed retail investors. In the United States, a substantial part of its trading-related revenue comes from payment for order flow, while in some non-U.S. markets the company more commonly charges commissions directly to customers. The platform is aimed at more active retail investors, including users seeking options tools, extended-hours trading and real-time market data. For 2025, Webull reported total revenue of US$571.0 million, up from US$390.2 million in 2024. Equity and option order-flow rebates accounted for US$304.1 million, or 53.3% of revenue, making order-flow rebates the company's largest reported revenue category. Interest-related income accounted for US$154.3 million, handling charge income for US$87.3 million and other revenue for US$25.3 million. Options were the larger component of the company's order-flow rebates in 2025, generating US$210.0 million compared with US$94.2 million from equities. Webull also generates revenue from interest-related activities, including margin financing, customer bank deposits, stock lending and corporate bank deposits. The company has stated that its interest-related income is affected by interest rates, customer cash balances, margin balances and demand for stock lending. The company had approximately 20 million registered users worldwide as of February 2024. As of December 31, 2025, it reported 26.8 million registered users, 5.0 million funded accounts and US$24.6 billion in customer assets. As of March 2025, Webull operated in Hong Kong, Singapore, Australia, South Africa, Japan, the United Kingdom, the United States, Indonesia, Canada, Brazil, Thailand, Malaysia and Mexico. == Marketing and sponsorships == Webull has used paid digital advertising, referral incentives, free-stock promotions, affiliate marketing and sports sponsorships to acquire customers and promote its brand. In its 2025 annual filing, the company reported marketing and branding expenses of US$152.3 million in 2023, US$138.7 million in 2024 and US$135.9 million in 2025. Webull said most of its advertising and promotion costs were related to paid search and paid social advertising, and that it had reduced free-stock promotions while shifting toward deposit- and asset-transfer-based incentives. In September 2021, BSE Global, the parent company of the Brooklyn Nets and New York Liberty, entered into a global multi-year agreement with Webull. Under the agreement, Webull became an official sponsor and online brokerage partner of the teams, with branding that included a jersey patch on Brooklyn Nets uniforms. Spo
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Fantavision

Fantavision is an animation program by Scott Anderson for the Apple II and published by Broderbund in 1985. Versions were released for the Apple IIGS (1987), Amiga (1988), and MS-DOS (1988). Fantavision allows the creation of vector graphics animations using the mouse and keyboard. The user creates frames, and the software generates the frames between them. Because this is done in real-time, it allows for creative exploration and quick changes. The program uses a graphical user interface in the style of the Macintosh with pull-down menus and black text on a white background. Advertisements claimed Fantavision a revolutionary breakthrough that brings the animation features of "tweening" and "transforming" to home computers. == Reception == Compute! in 1989 called Fantavision the best animation program for the IBM PC, although it noted the inability to draw curves. == Reviews == Games #70
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Content as a service

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

In computing, a connection string is a string that specifies information about a data source and the means of connecting to it. It is passed in code to an underlying driver or provider in order to initiate the connection. Whilst commonly used for a database connection, the data source could also be a spreadsheet or text file. The connection string may include attributes such as the name of the driver, server and database, as well as security information such as user name and password. == Examples == This example shows a PostgreSQL connection string for connecting to wikipedia.com with SSL and a connection timeout of 180 seconds: DRIVER={PostgreSQL Unicode};SERVER=www.wikipedia.com;SSL=true;SSLMode=require;DATABASE=wiki;UID=wikiuser;Connect Timeout=180;PWD=ashiknoor Users of Oracle databases can specify connection strings: on the command line (as in: sqlplus scott/tiger@connection_string ) via environment variables ($TWO_TASK in Unix-like environments; %TWO_TASK% in Microsoft Windows environments) in local configuration files (such as the default $ORACLE_HOME/network/admin.tnsnames.ora) in LDAP-capable directory services
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MinID

MinID is an electronic login system used to secure a range of internet services in the Norwegian public sector. The communication done with MinID is encrypted to secure information from unauthorized usage. Everyone registered in the Norwegian Population Register over the age of 13 years can create a public ID with MinID. As of April 2010, more than 2 million people living in Norway had created user accounts with MinID. To create a public ID, PIN-codes from the Norwegian Tax Administration are needed. == Purpose == The purpose of MinID is to communicate an electronic identity, so that users are authorized to use electronic services, in a secure way. MinID has a user database where social security numbers and PIN-codes are saved. MinID can be used to access more than 50 online services from various Norwegian public agencies, including the Norwegian Labour and Welfare Administration, the Directorate of Taxes and the State Educational Loan. == Controller == The Norwegian Digitalisation Agency (Digdir) is the controller of the personal data handled by MinID. The Norwegian Digitalisation Agency (Norwegian: Digitaliseringsdirektoratet) or Digdir is a government agency subordinate to the Ministry of Digitalisation and Public Governance. It is responsible for help the public sector achieve quality, efficiency, user friendliness, openness and participation, as well as helping the public sector be organized and led in a good way with good intersectoral cooperation. == User profile == Users of MinID have a user profile that contains their mobile phone number and/or e-mail address. This data is used to administrate MinID use. The e-mail address is needed in order to send the user a temporary password if he or she forgets the password. The phone number is needed in order to send an SMS-code at log in or a temporary password if the user forgets the password. == Transparency, correction and deletion == According to the law users can claim full access of the handling of their own personal data. Users also have the right to information about how this data are handled and saved, and how they can correct or delete inaccurate data. Users can at any time choose to delete themselves as a user of MinID. The user profile will then be deleted from the MinID user database. == Extradition to others == MinID passes on the user's social security number and chosen language to the public services he or she logs on to, so that the user can go to other public services without a new login.
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Patch management

Patch management (or patch management policy or patch policy or patch management process) is concerned with the identification, acquisition, distribution, testing and installation of patches to systems. Proper patch management can be a net productivity boost for an organization. Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them. Problems can arise during patch management, including buggy patches that either fail to fix their problem or introduce new issues. Patch management tools help orchestrate all of the procedures involved in patch management. == Description == Patch management is defined as a sub-practice of various disciplines including vulnerability management (part of security management), lifecycle management (with further possible sub-classification into application lifecycle management and release management), change management, and systems management. The practice is broadly concerned with the identification, acquisition, distribution, and installation of patches to systems. Some definitions of patch management are as a software-level practice, while others are as a systems-level process: software, drivers, and firmware. == Cost–benefit analysis == While reserving time for patching takes up enterprise resources, there are balancing factors which can make proper patch management into a net productivity boost for an organization. Up-to-date systems often perform more efficiently, less costly, with less errors, less security risks, and better user workflow. Additionally, compliance with changing local and federal regulations are more likely to be satisfied. Patching security vulnerabilities has been one among many competing priorities for organizations, leading to longer periods before patching for some organizations. Equifax was too slow to implement its 2015 patch management plan to be able to mitigate or prevent the 2017 Equifax data breach, leading to scrutiny from regulators. == Relation to security management == Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them; therefore, patch management can be considered a sub-discipline of vulnerability management. Every patchable device in a system presents an attack surface that must be secured. === Time plan === Automatic updates are where the patch is applied automatically with little to know actions or planning required. This approach is recommended for many individuals and organizations. Some organizations also have to prioritize which patches to prioritize given limited resources. Patch Tuesday is the most common process when major companies like Microsoft and Adobe release patches on a known date so that companies can plan resources around implementing the patches more quickly. Linux is open-sourced and patches can be released at any time, leading some to rely on mailing lists or other ways to be alerted to updates. === Inventory === Taking an inventory of software and hardware, including versions can make it easier to correlate with bugs or patches as they become known. Taking stock of how much education and support others in an organization need to install their patches can also help for planning how to implement the patch or design systems to begin with. Streamlining the process by using tools that can communicate with each other can also help to reduce the time of exposure to known vulnerabilities. == Challenges == There are a multitude of problems that can arise during patch management. A common issue is buggy patches, which either fail to fix their problem or introduce new issues. Another issue is deployment synchronization, since various subsystems may receive instructions to update at different times. Similarly, the difficulty of patch management across many devices may grow at an uncontrollable rate depending on organizational size. One prominent demonstration of the challenges facing proper patch management was the buggy Falcon Sensor patch by CrowdStrike which caused one of the worst IT outages of all time. == Implementations == A patch management tool (alternatively patch manager, patch management system, patch management software, or centralized patch management) help orchestrate all of the procedures involved in patch management. Tools can be in-house (applied locally by local administrators), or external, as with managed service providers (applied externally by a provider). === Patch management software === Windows Update for Business, System Center Configuration Manager, and Windows Server Update Services offer control over patch deployment, with features enabling testing, scheduling updates, and setting custom configurations on Windows platforms. === Managed service providers === == Regulatory requirements (United States) == Timely patching of software vulnerabilities is a requirement under multiple regulatory frameworks in the United States. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect electronic protected health information by implementing security measures sufficient to reduce risks to a reasonable and appropriate level, which industry guidance has long interpreted to include timely patch management. A proposed new HIPAA Security Rule would make patch management requirements explicit, mandating that covered entities and business associates deploy security patches and updates within a defined risk-based timeline and maintain written procedures for prioritizing, testing, and applying patches to systems that store, process, or transmit ePHI. The 2025 proposal continues to receive industry pushback as of December 2025. HIPAA was last updated in 2013. The Payment Card Industry Data Security Standard (PCI DSS) requires organizations to protect system components from known vulnerabilities by installing applicable security patches within one month of release for critical patches. The Cybersecurity and Infrastructure Security Agency (CISA) maintains a Known Exploited Vulnerabilities (KEV) catalog that compels U.S. federal agencies to remediate listed vulnerabilities within specified timelines. Agencies are typically required to patch within 3 weeks, though some vulnerabilities must be fixed within 24 hours.
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Dental AI

Dental artificial intelligence (Dental AI) refers to the application of artificial intelligence (AI) and machine-learning methods to oral healthcare data. These systems can be used to find patterns or make predictions that can aid in diagnosis, treatment, patient communication, or practice management. == History and development == Research into AI for dentistry dates to the 1990s and 2000s, alongside early CAD/CAM and image-analysis work in dental radiology. Recent developments in deep learning, especially those involving computer vision, such as convolutional neural networks, trained on large image datasets, led to a rapid improvement in performance, as well as a move from prototype technology to productization suitable for use in dental chairs. Dental schools and continuing education programs started incorporating AI content in the 2020s. == Definition and core technologies == The dental AI software accomplishes this task by using various dental images and patient data. Dental images and data used by the dental AI software include bitewing and periapical X-rays, complete mouth X-rays, detailed 3D images, intraoral images, and the patient’s medical history. The dental AI software utilizes several core technologies in accomplishing its task of assisting the dentist. First, the dental AI software utilizes machine learning and deep learning using programs that can learn from examples. Such programs are referred to as convolutional neural network (CNN) and can detect cavities and identify bone changes related to gum disease. The dental AI software utilizes computer vision, which enables the AI software to identify and quantify important features in images and data, whether they are 2D images or 3D images. Natural language processing (NLP) is used for the AI software to understand written text and can automatically generate dental notes and communicate with the patient. Furthermore, the dental AI software utilizes predictive analytics to identify patients that are more prone to dental complications and can suggest the best intervals for checkups or future dental procedures. == Applications in dentistry == Reported clinical and operational applications include diagnostic assistance for caries and periodontal disease, treatment planning assistance, patient education overlays, quality assurance, curriculum assistance for dental education, and claims documentation. Systematic reviews continue to find image-based applications such as caries detection with some variability in study design and a need for prospective validation. == Academic research and clinical validation == Several peer-reviewed studies have measured the effectiveness of AI for applications such as interproximal caries detection and periodontal bone level assessment, showing improvements over unaided readings with a focus on bias within the dataset. The Dental AI Council found variability among clinicians for diagnosis and treatment planning, suggesting the use of a standard tool as an assist. == Industry adoption == Multiple vendors offer FDA-cleared chairside AI for dental imaging: Pearl — Received U.S. FDA 510(k) clearance for its real-time radiologic aid (“Second Opinion”) in 2022 (2D), with subsequent clearances including pediatric and CBCT (“Second Opinion 3D”). TIME gave “Second Opinion” a special mention on its Best Inventions of 2022 list. Overjet — FDA-cleared for bone-level quantification and detection/outline of caries and calculus (e.g., K210187), with additional clearances expanding capabilities. VideaHealth — Received an FDA 510(k) covering 30+ detections across common dental findings (K232384), including indications for patients ages 3 and up; trade coverage has described elements of this as the first pediatric dental-AI clearance. == Regulations == In the U.S., AI-enabled dental imaging software is generally reviewed via the FDA’s 510(k) pathway. The FDA maintains a public AI-Enabled Medical Devices List, which includes numerous medical-imaging AI tools (including dental). Specific dental clearances include Overjet (K210187), VideaHealth (K232384), and Pearl entries such as “Second Opinion 3D” (K243989).
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Per-pixel lighting

In computer graphics, per-pixel lighting refers to any technique for lighting an image or scene that calculates illumination for each pixel on a rendered image. This is in contrast to other popular methods of lighting such as vertex lighting, which calculates illumination at each vertex of a 3D model and then interpolates the resulting values over the model's faces to calculate the final per-pixel color values. Per-pixel lighting is commonly used with techniques, such as blending, alpha blending, alpha to coverage, anti-aliasing, texture filtering, clipping, hidden-surface determination, Z-buffering, stencil buffering, shading, mipmapping, normal mapping, bump mapping, displacement mapping, parallax mapping, shadow mapping, specular mapping, shadow volumes, high-dynamic-range rendering, ambient occlusion (screen space ambient occlusion, screen space directional occlusion, ray-traced ambient occlusion), ray tracing, global illumination, and tessellation. Each of these techniques provides some additional data about the surface being lit or the scene and light sources that contributes to the final look and feel of the surface. Most modern video game engines implement lighting using per-pixel techniques instead of vertex lighting to achieve increased detail and realism. The id Tech 4 engine, used to develop such games as Brink and Doom 3, was one of the first game engines to implement a completely per-pixel shading engine. All versions of the CryENGINE, Frostbite Engine, and Unreal Engine, among others, also implement per-pixel shading techniques. Deferred shading is a recent development in per-pixel lighting notable for its use in the Frostbite Engine and Battlefield 3. Deferred shading techniques are capable of rendering potentially large numbers of small lights inexpensively (other per-pixel lighting approaches require full-screen calculations for each light in a scene, regardless of size). == History == While only recently have personal computers and video hardware become powerful enough to perform full per-pixel shading in real-time applications such as games, many of the core concepts used in per-pixel lighting models have existed for decades. Frank Crow published a paper describing the theory of shadow volumes in 1977. This technique uses the stencil buffer to specify areas of the screen that correspond to surfaces that lie in a "shadow volume", or a shape representing a volume of space eclipsed from a light source by some object. These shadowed areas are typically shaded after the scene is rendered to buffers by storing shadowed areas with the stencil buffer. Jim Blinn first introduced the idea of normal mapping in a 1978 SIGGRAPH paper. Blinn pointed out that the earlier idea of unlit texture mapping proposed by Edwin Catmull was unrealistic for simulating rough surfaces. Instead of mapping a texture onto an object to simulate roughness, Blinn proposed a method of calculating the degree of lighting a point on a surface should receive based on an established "perturbation" of the normals across the surface. == Hardware rendering == Real-time applications, such as video games, usually implement per-pixel lighting through the use of pixel shaders, allowing the GPU hardware to process the effect. The scene to be rendered is first rasterized onto a number of buffers storing different types of data to be used in rendering the scene, such as depth, normal direction, and diffuse color. Then, the data is passed into a shader and used to compute the final appearance of the scene, pixel-by-pixel. Deferred shading is a per-pixel shading technique that has recently become feasible for games. With deferred shading, a "g-buffer" is used to store all terms needed to shade a final scene on the pixel level. The format of this data varies from application to application depending on the desired effect, and can include normal data, positional data, specular data, diffuse data, emissive maps and albedo, among others. Using multiple render targets, all of this data can be rendered to the g-buffer with a single pass, and a shader can calculate the final color of each pixel based on the data from the g-buffer in a final "deferred pass". Because deferred shading assumes only one visible fragment per pixel sample, transparent objects are generally handled in a separate forward pass. == Software rendering == Per-pixel lighting is also performed in software on many high-end commercial rendering applications which typically do not render at interactive framerates. This is called offline rendering or software rendering. NVidia's mental ray rendering software, which is integrated with such suites as Autodesk's Softimage is a well-known example.
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Sample (graphics)

In computer graphics, a sample is an intersection of a channel and a pixel. The diagram below depicts a 24-bit pixel, consisting of 3 samples for Red, Green, and Blue. In this particular diagram, the Red sample occupies 9 bits, the Green sample occupies 7 bits and the Blue sample occupies 8 bits, totaling 24 bits per pixel. Note that the samples do not have to be equal size and not all samples are mandatory in a pixel. Also, a pixel can consist of more than 3 samples (e.g. 4 samples of the RGBA color space). A sample is related to a subpixel on a physical display.
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Kinematic chain

In mechanical engineering, a kinematic chain is an assembly of rigid bodies connected by joints to provide constrained motion that is the mathematical model for a mechanical system. As the word chain suggests, the rigid bodies, or links, are constrained by their connections to other links. An example is the simple open chain formed by links connected in series, like the usual chain, which is the kinematic model for a typical robot manipulator. Mathematical models of the connections, or joints, between two links are termed kinematic pairs. Kinematic pairs model the hinged and sliding joints fundamental to robotics, often called lower pairs and the surface contact joints critical to cams and gearing, called higher pairs. These joints are generally modeled as holonomic constraints. A kinematic diagram is a schematic of the mechanical system that shows the kinematic chain. The modern use of kinematic chains includes analysis of Linkages (mechanical), compliance that arises from flexure joints in precision mechanisms, link compliance in compliant mechanisms and micro-electro-mechanical systems, and cable compliance in cable robotic and tensegrity systems. == Mobility formula == The degrees of freedom, or mobility, of a kinematic chain is the number of parameters that define the configuration of the chain. A system of n rigid bodies moving in space has 6n degrees of freedom measured relative to a fixed frame. This frame is included in the count of bodies, so that mobility does not depend on link that forms the fixed frame. This means the degree-of-freedom of this system is M = 6(N − 1), where N = n + 1 is the number of moving bodies plus the fixed body. Joints that connect bodies impose constraints. Specifically, hinges and sliders each impose five constraints and therefore remove five degrees of freedom. It is convenient to define the number of constraints c that a joint imposes in terms of the joint's freedom f, where c = 6 − f. In the case of a hinge or slider, which are one-degree-of-freedom joints, have f = 1 and therefore c = 6 − 1 = 5. The result in general where d {\displaystyle d} is the degrees of freedom for the mobility of a kinematic chain formed from n moving links and j joints each with freedom fi, i = 1, 2, …, j, is given by M = d n − ∑ i = 1 j ( d − f i ) = d ( N − 1 − j ) + ∑ i = 1 j f i {\displaystyle M=dn-\sum _{i=1}^{j}(d-f_{i})=d(N-1-j)+\sum _{i=1}^{j}f_{i}} Where N is the total number of links and includes the fixed link. Spacial linkages used d = 6 {\displaystyle d=6} and planar linkages use d = 3 {\displaystyle d=3} . This result is known as the Chebychev–Grübler–Kutzbach criterion. == Analysis of kinematic chains == The constraint equations of a kinematic chain couple the range of movement allowed at each joint to the dimensions of the links in the chain, and form algebraic equations that are solved to determine the configuration of the chain associated with specific values of input parameters, called degrees of freedom. The constraint equations for a kinematic chain are obtained using rigid transformations [Z] to characterize the relative movement allowed at each joint and separate rigid transformations [X] to define the dimensions of each link. In the case of a serial open chain, the result is a sequence of rigid transformations alternating joint and link transformations from the base of the chain to its end link, which is equated to the specified position for the end link. A chain of n links connected in series has the kinematic equations, [ T ] = [ Z 1 ] [ X 1 ] [ Z 2 ] [ X 2 ] ⋯ [ X n − 1 ] [ Z n ] , {\displaystyle [T]=[Z_{1}][X_{1}][Z_{2}][X_{2}]\cdots [X_{n-1}][Z_{n}],\!} where [T] is the transformation locating the end-link—notice that the chain includes a "zeroth" link consisting of the ground frame to which it is attached. These equations are called the forward kinematics equations of the serial chain. Kinematic chains of a wide range of complexity are analyzed by equating the kinematics equations of serial chains that form loops within the kinematic chain. These equations are often called loop equations. The complexity (in terms of calculating the forward and inverse kinematics) of the chain is determined by the following factors: Its topology: a serial chain, a parallel manipulator, a tree structure, or a graph. Its geometrical form: how are neighbouring joints spatially connected to each other? Explanation Two or more rigid bodies in space are collectively called a rigid body system. We can hinder the motion of these independent rigid bodies with kinematic constraints. Kinematic constraints are constraints between rigid bodies that result in the decrease of the degrees of freedom of rigid body system. == Synthesis of kinematic chains == The constraint equations of a kinematic chain can be used in reverse to determine the dimensions of the links from a specification of the desired movement of the system. This is termed kinematic synthesis. Perhaps the most developed formulation of kinematic synthesis is for four-bar linkages, which is known as Burmester theory. Ferdinand Freudenstein is often called the father of modern kinematics for his contributions to the kinematic synthesis of linkages beginning in the 1950s. His use of the newly developed computer to solve Freudenstein's equation became the prototype of computer-aided design systems. This work has been generalized to the synthesis of spherical and spatial mechanisms.
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Super app

A super app or super-app (also known as an everything app) is a mobile or web application that can provide multiple services including payment and instant messaging services, effectively becoming an all-encompassing, self-contained, commerce and communication online platform that embraces many aspects of personal and commercial life. Notable examples of super apps include Tencent's WeChat in China, Tata Neu in India, Grab in Southeast Asia and Max in Russia. For end users, a super app is an application that provides a set of core features while also giving access to independently developed miniapps. For app developers, a super app is an application integrated with the capabilities of platforms and ecosystems that allows third-parties to develop and publish miniapps. == History == The super app term was first used to describe WeChat when it combined the instant messaging service with the digital wallet function. Recognition of WeChat as a super app stems from its combination of messaging, payments, e-commerce, and much more within a single application, making it indispensable for many users. WeChat's establishment of the super app model has led companies like Meta to try to build similar applications outside of China. In India, Tata Group has announced that it is currently developing a super app named Tata Neu. Major Indian companies like Paytm, PhonePe, and ITC Maars also have apps in development that might constitute super apps. In Southeast Asia, Grab and Gojek lay claim to the super app classification despite lacking many of the features offered by WeChat. Accordingly, growth-stage companies like Shopee, Traveloka, and AirAsia have also expanded the range of services offered by their respective applications. == Notable examples == === Alipay === Alipay is a third-party mobile and online payment platform established in Hangzhou, China in February 2004 by Alibaba Group and its founder Jack Ma. It operates in association with Ant Group, an affiliate company of the Chinese Alibaba Group. === Gojek === Gojek is an Indonesian on-demand multiservice digital platform and fintech payment super app. Established in Jakarta in 2010, as a call center to connect consumers to courier delivery and two-wheeled ride-hailing services, it launched its mobile app in 2015 with four services: GoRide, GoSend, GoShop, and GoFood, which has since expanded to offer over 20 services. In 2021, it merged with another Indonesian unicorn, Tokopedia, forming the decacorn GoTo Gojek Tokopedia. === Grab === Grab is a Southeast Asian technology company headquartered in Singapore and Indonesia. Founded in 2012 as the MyTeksi app in Kuala Lumpur, Malaysia, it expanded the following year as GrabTaxi, before moving its headquarters to Singapore in 2014 and rebranding officially as Grab. In addition to ride-hailing and transportation services, the company's mobile app also offers food delivery and digital payment services. === Max === Max is a messenger from the Russian company VK, positioned as a super app. The application combines messaging, calls, and channels features with the integration of additional services: payments, miniapps, taxi ordering, deliveries, and other everyday services are available within a single interface. The goal is to unite communication and routine tasks in a unified ecosystem. === Tata Neu === Tata Neu is a multipurpose super app, developed in India by the Tata Group. It is the country's first super app. The app was launched to coincide with the start of a 2022 Indian Premier League cricket match. === WeChat === WeChat is a Chinese multipurpose instant messaging, social media and mobile payment app. First released in 2011, it became the world's largest standalone mobile app in 2018, with over 1 billion monthly active users. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, the sharing of photographs and videos and location sharing. === X === X is an American social network, originally known as Twitter from its launch through 2023. Prior to his acquisition of the service, new owner Elon Musk stated that he planned for Twitter to become an "everything app" known as "X"; in 2023, the service added an AI chatbot known as "Grok" as well as integrated job search tools known as "X Hiring". In January 2025, X announced its intent to offer a digital wallet service in the future. Later in the year, X revamped its direct messaging system as "Chat". == Criticism == Although apps that fit the super app classification can offer users a wider variety of services in comparison to single-purpose alternatives, internet regulators in regions such as the US and Europe have become more concerned about the overall power of the technology industry and have become more critical of companies developing such apps. In China, WeChat and other local firms have been ordered to open up their platforms to rivals by local regulators. There are also reports that suggest it might be difficult to replicate WeChat's super app model. This stems partly from the peaking of smartphone penetration rates in many regions worldwide, which has led to overcrowded app stores and tighter restrictions on targeted advertising as regulators assert more control over the companies. From a technical viewpoint, single-purpose apps are comparatively faster, more responsive and easier to navigate than super apps, which helps improve the overall user experience. Super-apps are also likelier to store larger amounts of personal data to facilitate the delivery of their services, so users run a greater risk of becoming victims of severe data breaches. In 2020, this unfolded with Tokopedia, which had the data of 91 million of its users stolen and shared by crackers. It has also been noted that a user who loses access to their account or is banned from a super app generally loses access to multiple real-life services and digital applications; the Chinese government has used this approach to penalize people who shared the photos of the Sitong Bridge protest.
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Tuber (app)

Tuber (Chinese: Tuber浏览器) was a web browser mobile app developed by Shanghai Fengxuan Information Technology that allowed users within mainland China to view filtered versions of certain websites normally blocked by the Great Firewall. Filtered versions of websites such as Google, Facebook, Instagram, YouTube, Twitter, Netflix, IMDb, and Wikipedia could be viewed. The app was backed by cybersecurity company Qihoo 360 which served as the parent company. The app required phone number registration. Sensitive keywords were blocked by the app. On October 9, 2020, Global Times editor Rita Bai Yunyi tweeted that the move represented "a great step for China's opening up". The app was removed from China domestic app stores and operations ceased as of October 10, 2020. On October 12, when questioned by a Bloomberg News reporter on the topic, Foreign Ministry spokesperson Zhao Lijian replied, "This is not a diplomatic issue, and I do not have the relevant information you mentioned. China has always managed the Internet in accordance with the law. I suggest you ask the competent department for the specific situation."
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Joint constraints

Joint constraints are rotational constraints on the joints of an artificial system. They are used in an inverse kinematics chain, in fields including 3D animation or robotics. Joint constraints can be implemented in a number of ways, but the most common method is to limit rotation about the X, Y and Z axis independently. An elbow, for instance, could be represented by limiting rotation on X and Z axis to 0 degrees, and constraining the Y-axis rotation to 130 degrees. To simulate joint constraints more accurately, dot-products can be used with an independent axis to repulse the child bones orientation from the unreachable axis. Limiting the orientation of the child bone to a border of vectors tangent to the surface of the joint, repulsing the child bone away from the border, can also be useful in the precise restriction of shoulder movement.
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