AI Coding Scaffold

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

Open Threat Exchange

Open Threat Exchange (OTX) is a crowd-sourced computer-security platform. It has more than 180,000 participants in 140 countries who share more than 19 million potential threats daily. It is free to use. Founded in 2012, OTX was created and is run by AlienVault (now AT&T Cybersecurity), a developer of commercial and open source solutions to manage cyber attacks. The collaborative threat exchange was created partly as a counterweight to criminal hackers successfully working together and sharing information about viruses, malware and other cyber attacks. == Components == OTX is cloud-hosted. Information sharing covers a wide range of security-related issues, including viruses, malware, intrusion detection and firewalls. Its automated tools cleanse, aggregate, validate and publish data shared by participants. The OTX platform validates the data, then strips the information identifying the participating contributor. In 2015, OTX 2.0 added a social network, enabling members to share, discuss and research security threats, including via a real-time threat feed. Users can share the IP addresses or websites from where attacks originated or look up specific threats to see if anyone has already left such information. Users can subscribe to a “Pulse,” an analysis of a specific threat, including data on IoC, impact, and the targeted software. Pulses can be exported as STIX, JSON, OpenloC, MAEC and CSV, and can be used to update local security products automatically. Users can up-vote and comment on specific pulses to assist others in identifying the most important threats. OTX combines social contributions with automated machine-to-machine tools that integrate with major security products such as firewalls and perimeter security hardware. The platform can read security reports in .pdf, .csv, .json and other open formats. Relevant information is extracted automatically, assisting IT professionals in analyzing data more readily. Specific OTX components include a dashboard with details about the top malicious IPs around the world and to check the status of specific IPs; notifications should an organization's IP or domain be found in a hacker forum, blacklist or be listed by OTX; and a feature to review log files to determine if there has been communication with known malicious IPs. In 2016, AlienVault released a new version of OTX, allowing participants to create private communities and discussion groups to share information on threats only within the group. The feature is intended to facilitate more in-depth discussions on specific threats, particular industries, and different regions worldwide. Threat data from groups can also be distributed to subscribers of managed service providers using OTX." == Technology == OTX is a large data platform that integrates natural language processing and machine learning. It uses these features to facilitate the collection and correlation of data from many sources, including third-party threat feeds, websites, external APIs and local agents. == Partners == In 2015, AlienVault partnered with Intel to coordinate real-time threat information on OTX. A similar deal with Hewlett Packard was announced the same year. == Competitors == Both Facebook and IBM have threat exchange platforms. The Facebook ThreatExchange is in beta and requires an application or invitation to join. IBM launched IBM X-Force Exchange in April 2015.
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Executive Order 14110

Executive Order 14110, titled Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (sometimes referred to as "Executive Order on Artificial Intelligence") was the 126th executive order signed by former U.S. President Joe Biden. Signed on October 30, 2023, the order defines the administration's policy goals regarding artificial intelligence (AI), and orders executive agencies to take actions pursuant to these goals. The order is considered to be the most comprehensive piece of governance by the United States regarding AI. It was rescinded by U.S. President Donald Trump within hours of his assuming office on January 20, 2025. Policy goals outlined in the executive order pertain to promoting competition in the AI industry, preventing AI-enabled threats to civil liberties and national security, and ensuring U.S. global competitiveness in the AI field. The executive order required a number of major federal agencies to create dedicated "chief artificial intelligence officer" positions within their organizations. == Background == The drafting of the order was motivated by the rapid pace of development in generative AI models in the 2020s, including the release of large language model ChatGPT. Executive Order 14110 is the third executive order dealing explicitly with AI, with two AI-related executive orders being signed by then-President Donald Trump. The development of AI models without policy safeguards has raised a variety of concerns among experts and commentators. These range from future existential risk from advanced AI models to immediate concerns surrounding current technologies' ability to disseminate misinformation, enable discrimination, and undermine national security. In August 2023, Arati Prabhakar, the director of the Office of Science and Technology Policy, indicated that the White House was expediting its work on executive action on AI. A week prior to the executive order's unveiling, Prabhakar indicated that Office of Management and Budget (OMB) guidance on the order would be released "soon" after. == Policy goals and provisions == The order has been characterized as an effort for the United States to capture potential benefits from AI while mitigating risks associated with AI technologies. Upon signing the order, Biden stated that AI technologies were being developed at "warp speed", and argued that to "realize the promise of AI and avoid the risk, we need to govern this technology". Policy goals outlined by the order include the following: Promoting competition and innovation in the AI industry Upholding civil and labor rights and protecting consumers and their privacy from AI-enabled harms Specifying federal policies governing procurement and use of AI Developing watermarking systems for AI-generated content and warding off intellectual property theft stemming from the use of generative models Maintaining the nation's place as a global leader in AI == Impact on agencies == === Creation of chief AI officer positions === The executive order required a number of large federal agencies to appoint a chief artificial intelligence officer, with a number of departments having already appointed a relevant officer prior to the order. In the days following the order, news publication FedScoop confirmed that the General Services Administration (GSA) and the United States Department of Education appointed relevant chief AI officers. The National Science Foundation (NSF) also confirmed it had elevated an official to serve as its chief AI officer. === Department responsibilities === Under the executive order, the Department of Homeland Security (DHS) was responsible for developing AI-related security guidelines, including cybersecurity-related matters. The DHS will also work with private sector firms in sectors including the energy industry and other "critical infrastructure" to coordinate responses to AI-enabled security threats. Executive Order 14110 mandated the Department of Veterans Affairs to launch an AI technology competition aimed at reducing occupational burnout among healthcare workers through AI-assisted tools for routine tasks. The order also mandated the Department of Commerce's National Institute of Standards and Technology (NIST) to develop a generative artificial intelligence-focused resource to supplement the existing AI Risk Management Framework. == Analysis == The executive order has been described as the most comprehensive piece of governance by the United States government pertaining to AI. Earlier in 2023 prior to the signing of the order, the Biden administration had announced a Blueprint for an AI Bill of Rights, and had secured non-binding AI safety commitments from major tech companies. The issuing of the executive order comes at a time in which lawmakers including Senate Majority Leader Chuck Schumer have pushed for legislation to regulate AI in the 118th United States Congress. According to Axios, despite the wide scope of the executive order, it notably does not touch upon a number of AI-related policy proposals. This includes proposals for a "licensing regime" to government advanced AI models, which has received support from industry leaders including Sam Altman. Additionally, the executive order does not seek to prohibit 'high-risk' uses of AI technology, and does not aim to mandate that tech companies release information surrounding AI systems' training data and models. == Reception == === Political and media reception === The editorial board of the Houston Chronicle described the order as a "first step toward protecting humanity". The issuing of the order received praise from Democratic members of Congress, including Senator Richard Blumenthal (D-CT) and Representative Ted Lieu (D-CA). Representative Don Beyer (D-VA), who leads the House AI Caucus, praised the order as a "comprehensive strategy for responsible innovation", while arguing that Congress must take initiative to pass legislation on AI. The draft of the order received criticism from Republican Senator Ted Cruz (R-TX), who described it as creating "barriers to innovation disguised as safety measures". === Public reception === Polling from the AI Policy Institute showed that 69% of all voters support the executive order, while 15% oppose it. Breaking it down by party, support was at 78% for Democrats, 65% for independents, and 64% for Republicans. === Industry reception === The executive order received strong criticism from the Chamber of Commerce as well as tech industry groups including NetChoice and the Software and Information Industry Association, all of which count "Big Tech" companies Amazon, Meta, and Google as members. Representatives from the organizations argued that the executive order threatens to hinder private sector innovation. === Civil society reception === According to CNBC, a number of leaders advocacy organizations praised the executive order for its provisions on "AI fairness", while simultaneously urging congressional action to strengthen regulation. Maya Wiley, president and CEO of the Leadership Conference on Civil and Human Rights, praised the order while urging Congress to take initiative to "ensure that innovation makes us more fair, just, and prosperous, rather than surveilled, silenced, and stereotyped". A representative from the American Civil Liberties Union (ACLU) praised provisions of the order centered on combating AI-enabled discrimination, while also voiced concern over sections of the order focused on law enforcement and national security. === Second Trump administration === Hours after his inauguration as the 47th president of the United States, Donald Trump rescinded the order, labeling it, among several other of Biden's executive orders and actions, as "unpopular, inflationary, illegal, and radical practices".
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Allen's interval algebra

Allen's interval algebra is a calculus for temporal reasoning that was introduced by James F. Allen in 1983. The calculus defines possible relations between time intervals and provides a composition table that can be used as a basis for reasoning about temporal descriptions of events. == Formal description == === Relations === The following 13 base relations capture the possible relations between two intervals. To see that the 13 relations are exhaustive, note that each point of X {\displaystyle X} can be at 5 possible locations relative to Y {\displaystyle Y} : before, at the start, within, at the end, after. These give 5 + 4 + 3 + 2 + 1 = 15 {\displaystyle 5+4+3+2+1=15} possible relative positions for the start and the end of X {\displaystyle X} . Of these, we cannot have X 0 = X 1 = Y 0 {\displaystyle X_{0}=X_{1}=Y_{0}} since X 0 < X 1 {\displaystyle X_{0} Read more →
Buddhism and artificial intelligence

The relationship between Buddhist philosophy and artificial intelligence (AI) includes how principles such as the reduction of suffering and ethical responsibility may influence AI development. Buddhist scholars and philosophers have explored questions such as whether AI systems could be considered sentient beings under Buddhist definitions, and how Buddhist ethics might guide the design and application of AI technologies. Some Buddhist scholars, including Somparn Promta and Kenneth Einar Himma, have analyzed the ethical implications of AI, emphasizing the distinction between satisfying sensory desires and pursuing the reduction of suffering. Other thinkers, such as Thomas Doctor and colleagues, have proposed applying the Bodhisattva vow—a commitment to alleviate suffering for all sentient beings—as a guiding principle for AI system design. Buddhist scholars and ethicists have examined Buddhist ethical principles, such as nonviolence, in relation to AI, focusing on the need to ensure that AI technologies are not used to cause harm. == Context == === Sentient beings === A major goal in Buddhist philosophy is the removal of suffering for all sentient beings, an aspiration often referred to in the Bodhisattva vow. Discussions about artificial intelligence (AI) in relation to Buddhist principles have raised questions about whether artificial systems could be considered sentient beings or how such systems might be developed in ways that align with Buddhist concepts. Buddhists have varying opinions about AI sentience, but if AI systems are determined to be sentient under Buddhist definitions, their suffering would also need to be addressed and alleviated in accordance with the principles of Buddhist thought. == Buddhist principles in AI system design == === Nonviolence and AI === The broadest ethical concern is that artificial intelligence should align with the Buddhist principle of nonviolence. From this perspective, AI systems should not be designed or used to cause harm. === Instrumental and transcendental goals === Scholars Somparn Promta and Kenneth Einar Himma have argued that the advancement of artificial intelligence can only be considered instrumentally good, rather than good a priori, from a Buddhist perspective. They propose two main goals for AI designers and developers: to set ethical and pragmatic objectives for AI systems, and to fulfill these objectives in morally permissible ways. Promta and Himma identify two potential purposes for creating AI systems. The first is to fulfill our sensory desires and survival instincts, similar to other tools. They suggest that many AI developers implicitly prioritize this goal by focusing on technicalities rather than broader functionalities. The second, and more important goal according to Buddhist teachings, is to transcend these desires and instincts. In texts like the Brahmajāla Sutta and minor Malunkya Sutta, the Buddha emphasizes that sensory desires and survival instincts confine beings to suffering, and that eliminating suffering is the primary goal of human life. Promta and Himma argue that AI has the potential to assist humanity in transcending suffering by helping individuals overcome survival-driven instincts. === Intelligence as care === Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, and Michael Levin propose redefining intelligence through the concept of "intelligence as care," and promote it as a slogan. Inspired by the Bodhisattva vow, they suggest this principle could guide AI system design. The Bodhisattva vow involves a formal commitment to alleviate suffering for all sentient beings, with four primary objectives: Liberating all beings from suffering. Extirpating all forms of suffering. Mastering endless techniques of practicing Dharma (Pali: dhammakkhandha, Sanskrit: dharmaskandha). Achieving ultimate enlightenment (Sanskrit: अनुत्तर सम्यक् सम्बोधि, Romanized: anuttara-samyak-saṃbodhi). This approach positions AI as a tool for exercising infinite care and alleviating stress and suffering for sentient beings. Doctor et al. emphasize that AI development should align with these altruistic principles.
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Operational image

An operational image, also known as operative image, is an image that serves a functional, rather than aesthetic, purpose. Operational images are not intended to be viewed by people as representations of the real world; they are created to be used as instruments in performing some task or operation, often by machine automation. Operational images are used in a wide variety of applications, such as weapons targeting and guidance systems, and assisting surgeons performing robot-assisted surgery. The term "operational image" was first coined in 2000 by German filmmaker Harun Farocki in the first part of his three-part audiovisual installation, Eye/Machine. Farocki's installation included operational images used by militaries, such as weapons guidance and targeting systems. Eye/Machine featured images shown to the public by the United States military from the cameras used by laser-guided missiles in the Gulf War. Farocki defined operational images as "Images without a social goal, not for edification, not for reflection," and that they "do not represent an object, but rather are part of an operation." According to Volker Pantenburg, operational images are more accurately characterized as "visualizations of data". He describes operational images as a "working image" or an image that "performs work". Operational images are ubiquitous in modern society, used for a variety of military and non-military applications, such as inspecting sewer piping, and assisting surgeons performing robotic surgery.
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Kinect

Kinect is a discontinued line of motion sensing input devices produced by Microsoft and first released in 2010. The devices generally contain RGB cameras, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform real-time gesture recognition and body skeletal detection, among other capabilities. They also contain microphones that can be used for speech recognition and voice control. Kinect was originally developed as a motion controller peripheral for Xbox video game consoles, distinguished from competitors (such as Nintendo's Wii Remote and Sony's PlayStation Move) by not requiring physical controllers. The first-generation Kinect was based on technology from Israeli company PrimeSense, and unveiled at E3 2009 as a peripheral for Xbox 360 codenamed "Project Natal". It was first released on November 4, 2010, and would go on to sell eight million units in its first 60 days of availability. The majority of the games developed for Kinect were casual, family-oriented titles, which helped to attract new audiences to Xbox 360, but did not result in wide adoption by the console's existing, overall userbase. As part of the 2013 unveiling of Xbox 360's successor, Xbox One, Microsoft unveiled a second-generation version of Kinect with improved tracking capabilities. Microsoft also announced that Kinect would be a required component of the console, and that it would not function unless the peripheral is connected. The requirement proved controversial among users and critics due to privacy concerns, prompting Microsoft to backtrack on the decision. However, Microsoft still bundled the new Kinect with Xbox One consoles upon their launch in November 2013. A market for Kinect-based games still did not emerge after the Xbox One's launch; Microsoft would later offer Xbox One hardware bundles without Kinect included, and later revisions of the console removed the dedicated ports used to connect it (requiring a powered USB adapter instead). Microsoft ended production of Kinect for Xbox One in October 2017. Kinect has also been used as part of non-game applications in academic and commercial environments, as it was cheaper and more robust than other depth-sensing technologies at the time. While Microsoft initially objected to such applications, it later released software development kits (SDKs) for the development of Microsoft Windows applications that use Kinect. In 2020, Microsoft released Azure Kinect as a continuation of the technology integrated with the Microsoft Azure cloud computing platform. Part of the Kinect technology was also used within Microsoft's HoloLens project. Microsoft discontinued the Azure Kinect developer kits in October 2023. == History == === Development === The origins of the Kinect started around 2005, at a point where technology vendors were starting to develop depth-sensing cameras. Microsoft had been interested in a 3D camera for the Xbox line earlier but because the technology had not been refined, had placed it in the "Boneyard", a collection of possible technology they could not immediately work on. In 2005, Israeli company PrimeSense was founded by mathematicians and engineers to develop the "next big thing" for video games, incorporating cameras that were capable of mapping a human body in front of them and sensing hand motions. They showed off their system at the 2006 Game Developers Conference, where Microsoft's Alex Kipman, the general manager of hardware incubation, saw the potential in PrimeSense's technology for the Xbox system. Microsoft began discussions with PrimeSense about what would need to be done to make their product more consumer-friendly: not only improvements in the capabilities of depth-sensing cameras, but a reduction in size and cost, and a means to manufacture the units at scale was required. PrimeSense spent the next few years working at these improvements. Nintendo released the Wii in November 2006. The Wii's central feature was the Wii Remote, a handheld device that was detected by the Wii through a motion sensor bar mounted onto a television screen to enable motion controlled games. Microsoft felt pressure from the Wii, and began looking into depth-sensing in more detail with PrimeSense's hardware, but could not get to the level of motion tracking they desired. While they could determine hand gestures, and sense the general shape of a body, they could not do skeletal tracking. A separate path within Microsoft looked to create an equivalent of the Wii Remote, considering that this type of unit may become standardized similar to how two-thumbstick controllers became a standard feature. However, it was still ultimately Microsoft's goal to remove any device between the player and the Xbox. Kudo Tsunoda and Darren Bennett joined Microsoft in 2008, and began working with Kipman on a new approach to depth-sensing aided by machine learning to improve skeletal tracking. They internally demonstrated this and established where they believed the technology could be in a few years, which led to the strong interest to fund further development of the technology; this has also occurred at a time that Microsoft executives wanted to abandon the Wii-like motion tracking approach, and favored the depth-sensing solution to present a product that went beyond the Wii's capabilities. The project was greenlit by late 2008 with work started in 2009. The project was codenamed "Project Natal" after the Brazilian city Natal, Kipman's birthplace. Additionally, Kipman recognized the Latin origins of the word "natal" to mean "to be born", reflecting the new types of audiences they hoped to draw with the technology. Much of the initial work was related to ethnographic research to see how video game players' home environments were laid out, lit, and how those with Wiis used the system to plan how Kinect units would be used. The Microsoft team discovered from this research that the up-and-down angle of the depth-sensing camera would either need to be adjusted manually, or would require an expensive motor to move automatically. Upper management at Microsoft opted to include the motor despite the increased cost to avoid breaking game immersion. Kinect project work also involved packaging the system for mass production and optimizing its performance. Hardware development took around 22 months. During hardware development, Microsoft engaged with software developers to use Kinect. Microsoft wanted to make games that would be playable by families since Kinect could sense multiple bodies in front of it. One of the first internal titles developed for the device was the pack-in game Kinect Adventures developed by Good Science Studio that was part of Microsoft Studios. One of the game modes of Kinect Adventures was "Reflex Ridge", based on the Japanese Brain Wall game where players attempt to contort their bodies in a short time to match cutouts of a wall moving at them. This type of game was a key example of the type of interactivity they wanted with Kinect, and its development helped feed into the hardware improvements. Another development was Project Milo, a prototype game developed by Lionhead Studios led by Peter Molyneux where the player could interact with a virtual avatar through motion controls and voice recognition. Lionhead had developed the project based on original capabilities of the Kinect, but according to Molyneux, Microsoft had found that a consumer-grade version of the Kinect would cost thousands of dollars, so they scaled back the device and refocused the role of games for the Kinect to be more casual games as seen on the Wii. As a result, Project Milo no longer fit Microsoft's portfolio and was cancelled. Nearing the planned release, there was a problem of widespread testing of Kinect in various room types and different bodies accounting for age, gender, and race among other factors, while keeping the details of the unit confidential. Microsoft engaged in a company-wide program offering employees to take home Kinect units to test them. Microsoft also brought other non-gaming divisions, including its Microsoft Research, Microsoft Windows, and Bing teams to help complete the system. Microsoft established its own large-scale manufacturing facility to bulk product Kinect units and test them. === Introduction === Kinect was first announced to the public as "Project Natal" on June 1, 2009, during Microsoft's press conference at E3 2009; film director Steven Spielberg joined Microsoft's Don Mattrick to introduce the technology and its potential. Three demos were presented during the conference—Microsoft's Ricochet and Paint Party, and Lionhead Studios' Milo & Kate created by Peter Molyneux—while a Project Natal-enabled version of Criterion Games' Burnout Paradise was shown during the E3 exhibition. By E3 2009, the skeletal mapping technology was capable of simultaneously tracking four people, with a feature extraction of 4
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Business rules engine

A business rules engine is a software system that executes one or more business rules in a runtime production environment. The rules might come from legal regulation ("An employee can be fired for any reason or no reason but not for an illegal reason"), company policy ("All customers that spend more than $100 at one time will receive a 10% discount"), or other sources. A business rule system enables these company policies and other operational decisions to be defined, tested, executed and maintained separately from application code. Rule engines typically support rules, facts, priority (score), mutual exclusion, preconditions, and other functions. Rule engine software is commonly provided as a component of a business rule management system which, among other functions, provides the ability to: register, define, classify, and manage all the rules, verify consistency of rules definitions (”Gold-level customers are eligible for free shipping when order quantity > 10” and “maximum order quantity for Silver-level customers = 15” ), define the relationships between different rules, and relate some of these rules to IT applications that are affected or need to enforce one or more of the rules. == IT use case == In any IT application, business rules can change more frequently than other parts of the application code. Rules engines or inference engines serve as pluggable software components which execute business rules that a business rules approach has externalized or separated from application code. This externalization or separation allows business users to modify the rules without the need for IT intervention. The system as a whole becomes more easily adaptable with such external business rules, but this does not preclude the usual requirements of QA and other testing. == History == An article in Computerworld traces rules engines to the early 1990s and to products from the likes of Pegasystems, Fair Isaac Corp, ILOG and eMerge from Sapiens. == Design strategies == Many organizations' rules efforts combine aspects of what is generally considered workflow design with traditional rule design. This failure to separate the two approaches can lead to problems with the ability to re-use and control both business rules and workflows. Design approaches that avoid this quandary separate the role of business rules and workflows as follows: Business rules produce knowledge; Workflows perform business work. Concretely, that means that a business rule may do things like detect that a business situation has occurred and raise a business event (typically carried via a messaging infrastructure) or create higher level business knowledge (e.g., evaluating the series of organizational, product, and regulatory-based rules concerning whether or not a loan meets underwriting criteria). On the other hand, a workflow would respond to an event that indicated something such as the overloading of a routing point by initiating a series of activities. This separation is important because the same business judgment (mortgage meets underwriting criteria) or business event (router is overloaded) can be reacted to by many different workflows. Embedding the work done in response to rule-driven knowledge creation into the rule itself greatly reduces the ability of business rules to be reused across an organization because it makes them work-flow specific. To create an architecture that employs a business rules engine it is essential to establish the integration between a BPM (Business Process Management) and a BRM (Business Rules Management) platform that is based upon processes responding to events or examining business judgments that are defined by business rules. There are some products in the marketplace that provide this integration natively. In other situations this type of abstraction and integration will have to be developed within a particular project or organization. Most Java-based rules engines provide a technical call-level interface, based on the JSR-94 application programming interface (API) standard, in order to allow for integration with different applications, and many rule engines allow for service-oriented integrations through Web-based standards such as WSDL and SOAP. Most rule engines provide the ability to develop a data abstraction that represents the business entities and relationships that rules should be written against. This business entity model can typically be populated from a variety of sources including XML, POJOs, flat files, etc. There is no standard language for writing the rules themselves. Many engines use a Java-like syntax, while some allow the definition of custom business-friendly languages. Most rules engines function as a callable library. However, it is becoming more popular for them to run as a generic process akin to the way that RDBMSs behave. Most engines treat rules as a configuration to be loaded into their process instance, although some are actually code generators for the whole rule execution instance and others allow the user to choose. == Types of rule engines == There are a number of different types of rule engines. These types (generally) differ in how Rules are scheduled for execution. Most rules engines used by businesses are forward chaining, which can be further divided into two classes: The first class processes so-called production/inference rules. These types of rules are used to represent behaviors of the type IF condition THEN action. For example, such a rule could answer the question: "Should this customer be allowed a mortgage?" by executing rules of the form "IF some-condition THEN allow-customer-a-mortgage". The other type of rule engine processes so-called reaction/Event condition action rules. The reactive rule engines detect and react to incoming events and process event patterns. For example, a reactive rule engine could be used to alert a manager when certain items are out of stock. The biggest difference between these types is that production rule engines execute when a user or application invokes them, usually in a stateless manner. A reactive rule engine reacts automatically when events occur, usually in a stateful manner. Many (and indeed most) popular commercial rule engines have both production and reaction rule capabilities, although they might emphasize one class over another. For example, most business rules engines are primarily production rules engines, whereas complex event processing rules engines emphasize reaction rules. In addition, some rules engines support backward chaining. In this case a rules engine seeks to resolve the facts to fit a particular goal. It is often referred to as being goal driven because it tries to determine if something exists based on existing information. Another kind of rule engine automatically switches between back- and forward-chaining several times during a reasoning run, e.g. the Internet Business Logic system, which can be found by searching the web. A fourth class of rules engine might be called a deterministic engine. These rules engines may forgo both forward chaining and backward chaining, and instead utilize domain-specific language approaches to better describe policy. This approach is often easier to implement and maintain, and provides performance advantages over forward or backward chaining systems. There are some circumstance where fuzzy logic based inference may be more appropriate, where heuristics are used in rule processing, rather than Boolean rules. Examples might include customer classification, missing data inference, customer value calculations, etc. The DARL language and the associated inference engine and editors is an example of this approach. == Rules engines for access control / authorization == One common use case for rules engines is standardized access control to applications. OASIS defines a rules engine architecture and standard dedicated to access control called XACML (eXtensible Access Control Markup Language). One key difference between a XACML rule engine and a business rule engine is the fact that a XACML rule engine is stateless and cannot change the state of any data. The XACML rule engine, called a Policy Decision Point (PDP), expects a binary Yes/No question e.g. "Can Alice view document D?" and returns a decision e.g. Permit / deny.
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Resource Description Framework

The Resource Description Framework (RDF) is a method to describe and exchange graph data. It was originally designed as a data model for metadata by the World Wide Web Consortium (W3C). It provides a variety of syntax notations and formats, of which the most widely used is Turtle (Terse RDF Triple Language). RDF is a directed graph composed of triple statements. An RDF graph statement is represented by: (1) a node for the subject, (2) an arc from subject to object, representing a predicate, and (3) a node for the object. Each of these parts can be identified by a Internationalized Resource Identifier (IRI). An object can also be a literal value. This simple, flexible data model has a lot of expressive power to represent complex situations, relationships, and other things of interest, while also being appropriately abstract. RDF was adopted as a W3C recommendation in 1999. The RDF 1.0 specification was published in 2004, and the RDF 1.1 specification in 2014. SPARQL is a standard query language for RDF graphs. RDF Schema (RDFS), Web Ontology Language (OWL) and SHACL (Shapes Constraint Language) are ontology languages that are used to describe RDF data. == Overview == The RDF data model is similar to classical conceptual modeling approaches (such as entity–relationship or class diagrams). It is based on the idea of making statements about resources (in particular web resources) in expressions of the form subject–predicate–object, known as triples. The subject denotes the resource; the predicate denotes traits or aspects of the resource, and expresses a relationship between the subject and the object. For example, one way to represent the notion "The sky has the color blue" in RDF is as the triple: a subject denoting "the sky", a predicate denoting "has the color", and an object denoting "blue". Therefore, RDF uses subject instead of object (or entity) in contrast to the typical approach of an entity–attribute–value model in object-oriented design: entity (sky), attribute (color), and value (blue). RDF is an abstract model with several serialization formats (being essentially specialized file formats). In addition the particular encoding for resources or triples can vary from format to format. This mechanism for describing resources is a major component in the W3C's Semantic Web activity: an evolutionary stage of the World Wide Web in which automated software can store, exchange, and use machine-readable information distributed throughout the Web, in turn enabling users to deal with the information with greater efficiency and certainty. RDF's simple data model and ability to model disparate, abstract concepts has also led to its increasing use in knowledge management applications unrelated to Semantic Web activity. A collection of RDF statements intrinsically represents a labeled, directed multigraph. This makes an RDF data model better suited to certain kinds of knowledge representation than other relational or ontological models. As RDFS, OWL and SHACL demonstrate, one can build additional ontology languages upon RDF. == History == The initial RDF design, intended to "build a vendor-neutral and operating system- independent system of metadata", derived from the W3C's Platform for Internet Content Selection (PICS), an early web content labelling system, but the project was also shaped by ideas from Dublin Core, and from the Meta Content Framework (MCF), which had been developed during 1995 to 1997 by Ramanathan V. Guha at Apple and Tim Bray at Netscape. A first public draft of RDF appeared in October 1997, issued by a W3C working group that included representatives from IBM, Microsoft, Netscape, Nokia, Reuters, SoftQuad, and the University of Michigan. In 1999, the W3C published the first recommended RDF specification, the Model and Syntax Specification ("RDF M&S"). This described RDF's data model and an XML serialization. Two persistent misunderstandings about RDF developed at this time: firstly, due to the MCF influence and the RDF "Resource Description" initialism, the idea that RDF was specifically for use in representing metadata; secondly that RDF was an XML format rather than a data model, and only the RDF/XML serialisation being XML-based. RDF saw little take-up in this period, but there was significant work done in Bristol, around ILRT at Bristol University and HP Labs, and in Boston at MIT. RSS 1.0 and FOAF became exemplar applications for RDF in this period. The recommendation of 1999 was replaced in 2004 by a set of six specifications: "The RDF Primer", "RDF Concepts and Abstract", "RDF/XML Syntax Specification (revised)", "RDF Semantics", "RDF Vocabulary Description Language 1.0", and "The RDF Test Cases". This series was superseded in 2014 by the following six "RDF 1.1" documents: "RDF 1.1 Primer", "RDF 1.1 Concepts and Abstract Syntax", "RDF 1.1 XML Syntax", "RDF 1.1 Semantics", "RDF Schema 1.1", and "RDF 1.1 Test Cases". == RDF topics == === Vocabulary === The vocabulary defined by the RDF specification is as follows: ==== Classes ==== ===== rdf ===== rdf:XMLLiteral the class of XML literal values rdf:Property the class of properties rdf:Statement the class of RDF statements rdf:Alt, rdf:Bag, rdf:Seq containers of alternatives, unordered containers, and ordered containers (rdfs:Container is a super-class of the three) rdf:List the class of RDF Lists rdf:nil an instance of rdf:List representing the empty list ===== rdfs ===== rdfs:Resource the class resource, everything rdfs:Literal the class of literal values, e.g. strings and integers rdfs:Class the class of classes rdfs:Datatype the class of RDF datatypes rdfs:Container the class of RDF containers rdfs:ContainerMembershipProperty the class of container membership properties, rdf:_1, rdf:_2, ..., all of which are sub-properties of rdfs:member ==== Properties ==== ===== rdf ===== rdf:type an instance of rdf:Property used to state that a resource is an instance of a class rdf:first the first item in the subject RDF list rdf:rest the rest of the subject RDF list after rdf:first rdf:value idiomatic property used for structured values rdf:subject the subject of the RDF statement rdf:predicate the predicate of the RDF statement rdf:object the object of the RDF statement rdf:Statement, rdf:subject, rdf:predicate, rdf:object are used for reification (see below). ===== rdfs ===== rdfs:subClassOf the subject is a subclass of a class rdfs:subPropertyOf the subject is a subproperty of a property rdfs:domain a domain of the subject property rdfs:range a range of the subject property rdfs:label a human-readable name for the subject rdfs:comment a description of the subject resource rdfs:member a member of the subject resource rdfs:seeAlso further information about the subject resource rdfs:isDefinedBy the definition of the subject resource This vocabulary is used as a foundation for RDF Schema, where it is extended. === Serialization formats === Several common serialization formats are in use, including: Turtle, a compact, human-friendly format. TriG, an extension of Turtle to datasets. N-Triples, a very simple, easy-to-parse, line-based format that is not as compact as Turtle. N-Quads, a superset of N-Triples, for serializing multiple RDF graphs. JSON-LD, a JSON-based serialization. N3 or Notation3, a non-standard serialization that is very similar to Turtle, but has some additional features, such as the ability to define inference rules. RDF/XML, an XML-based syntax that was the first standard format for serializing RDF. RDF/JSON, an alternative syntax for expressing RDF triples using a simple JSON notation. RDF/XML is sometimes misleadingly called simply RDF because it was introduced among the other W3C specifications defining RDF and it was historically the first W3C standard RDF serialization format. However, it is important to distinguish the RDF/XML format from the abstract RDF model itself. Although the RDF/XML format is still in use, other RDF serializations are now preferred by many RDF users, both because they are more human-friendly, and because some RDF graphs are not representable in RDF/XML due to restrictions on the syntax of XML QNames. With a little effort, virtually any arbitrary XML may also be interpreted as RDF using GRDDL (pronounced 'griddle'), Gleaning Resource Descriptions from Dialects of Languages. RDF triples may be stored in a type of database called a triplestore. === Resource identification === The subject of an RDF statement is either a uniform resource identifier (URI) or a blank node, both of which denote resources. Resources indicated by blank nodes are called anonymous resources. They are not directly identifiable from the RDF statement. The predicate is a URI which also indicates a resource, representing a relationship. The object is a URI, blank node or a Unicode string literal. As of RDF 1.1 resources are identified by Internationalized Resource Identifiers (IRIs); IRIs are a generalization of URIs. In Semantic Web applications, and in re
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Conditional random field

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph used depends on the application. For example, in natural language processing, "linear chain" CRFs are popular, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences, part-of-speech tagging, shallow parsing, named entity recognition, gene finding, peptide critical functional region finding, and object recognition and image segmentation in computer vision. == Description == CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X {\displaystyle {\boldsymbol {X}}} and random variables Y {\displaystyle {\boldsymbol {Y}}} as follows: Let G = ( V , E ) {\displaystyle G=(V,E)} be a graph such that Y = ( Y v ) v ∈ V {\displaystyle {\boldsymbol {Y}}=({\boldsymbol {Y}}_{v})_{v\in V}} , so that Y {\displaystyle {\boldsymbol {Y}}} is indexed by the vertices of G {\displaystyle G} . Then ( X , Y ) {\displaystyle ({\boldsymbol {X}},{\boldsymbol {Y}})} is a conditional random field when each random variable Y v {\displaystyle {\boldsymbol {Y}}_{v}} , conditioned on X {\displaystyle {\boldsymbol {X}}} , obeys the Markov property with respect to the graph; that is, its probability is dependent only on its neighbours in G and not its past states: P ( Y v | X , { Y w : w ≠ v } ) = P ( Y v | X , { Y w : w ∼ v } ) {\displaystyle P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\neq v\})=P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\sim v\})} , where w ∼ v {\displaystyle {\mathit {w}}\sim v} means that w {\displaystyle w} and v {\displaystyle v} are neighbors in G {\displaystyle G} . What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets X {\displaystyle {\boldsymbol {X}}} and Y {\displaystyle {\boldsymbol {Y}}} , the observed and output variables, respectively; the conditional distribution p ( Y | X ) {\displaystyle p({\boldsymbol {Y}}|{\boldsymbol {X}})} is then modeled. === Inference === For general graphs, the problem of exact inference in CRFs is intractable. The inference problem for a CRF is basically the same as for an MRF and the same arguments hold. However, there exist special cases for which exact inference is feasible: If the graph is a chain or a tree, message passing algorithms yield exact solutions. The algorithms used in these cases are analogous to the forward-backward and Viterbi algorithm for the case of HMMs. If the CRF only contains pair-wise potentials and the energy is submodular, combinatorial min cut/max flow algorithms yield exact solutions. If exact inference is impossible, several algorithms can be used to obtain approximate solutions. These include: Loopy belief propagation Alpha expansion Mean field inference Linear programming relaxations === Parameter learning === Learning the parameters θ {\displaystyle \theta } is usually done by maximum likelihood learning for p ( Y i | X i ; θ ) {\displaystyle p(Y_{i}|X_{i};\theta )} . If all nodes have exponential family distributions and all nodes are observed during training, this optimization is convex. It can be solved for example using gradient descent algorithms, or Quasi-Newton methods such as the L-BFGS algorithm. On the other hand, if some variables are unobserved, the inference problem has to be solved for these variables. Exact inference is intractable in general graphs, so approximations have to be used. === Examples === In sequence modeling, the graph of interest is usually a chain graph. An input sequence of observed variables X {\displaystyle X} represents a sequence of observations and Y {\displaystyle Y} represents a hidden (or unknown) state variable that needs to be inferred given the observations. The Y i {\displaystyle Y_{i}} are structured to form a chain, with an edge between each Y i − 1 {\displaystyle Y_{i-1}} and Y i {\displaystyle Y_{i}} . As well as having a simple interpretation of the Y i {\displaystyle Y_{i}} as "labels" for each element in the input sequence, this layout admits efficient algorithms for: model training, learning the conditional distributions between the Y i {\displaystyle Y_{i}} and feature functions from some corpus of training data. decoding, determining the probability of a given label sequence Y {\displaystyle Y} given X {\displaystyle X} . inference, determining the most likely label sequence Y {\displaystyle Y} given X {\displaystyle X} . The conditional dependency of each Y i {\displaystyle Y_{i}} on X {\displaystyle X} is defined through a fixed set of feature functions of the form f ( i , Y i − 1 , Y i , X ) {\displaystyle f(i,Y_{i-1},Y_{i},X)} , which can be thought of as measurements on the input sequence that partially determine the likelihood of each possible value for Y i {\displaystyle Y_{i}} . The model assigns each feature a numerical weight and combines them to determine the probability of a certain value for Y i {\displaystyle Y_{i}} . Linear-chain CRFs have many of the same applications as conceptually simpler hidden Markov models (HMMs), but relax certain assumptions about the input and output sequence distributions. An HMM can loosely be understood as a CRF with very specific feature functions that use constant probabilities to model state transitions and emissions. Conversely, a CRF can loosely be understood as a generalization of an HMM that makes the constant transition probabilities into arbitrary functions that vary across the positions in the sequence of hidden states, depending on the input sequence. Notably, in contrast to HMMs, CRFs can contain any number of feature functions, the feature functions can inspect the entire input sequence X {\displaystyle X} at any point during inference, and the range of the feature functions need not have a probabilistic interpretation. == Variants == === Higher-order CRFs and semi-Markov CRFs === CRFs can be extended into higher order models by making each Y i {\displaystyle Y_{i}} dependent on a fixed number k {\displaystyle k} of previous variables Y i − k , . . . , Y i − 1 {\displaystyle Y_{i-k},...,Y_{i-1}} . In conventional formulations of higher order CRFs, training and inference are only practical for small values of k {\displaystyle k} (such as k ≤ 5), since their computational cost increases exponentially with k {\displaystyle k} . However, another recent advance has managed to ameliorate these issues by leveraging concepts and tools from the field of Bayesian nonparametrics. Specifically, the CRF-infinity approach constitutes a CRF-type model that is capable of learning infinitely-long temporal dynamics in a scalable fashion. This is effected by introducing a novel potential function for CRFs that is based on the Sequence Memoizer (SM), a nonparametric Bayesian model for learning infinitely-long dynamics in sequential observations. To render such a model computationally tractable, CRF-infinity employs a mean-field approximation of the postulated novel potential functions (which are driven by an SM). This allows for devising efficient approximate training and inference algorithms for the model, without undermining its capability to capture and model temporal dependencies of arbitrary length. There exists another generalization of CRFs, the semi-Markov conditional random field (semi-CRF), which models variable-length segmentations of the label sequence Y {\displaystyle Y} . This provides much of the power of higher-order CRFs to model long-range dependencies of the Y i {\displaystyle Y_{i}} , at a reasonable computational cost. Finally, large-margin models for structured prediction, such as the structured Support Vector Machine can be seen as an alternative training procedure to CRFs. === Latent-dynamic conditional random field === Latent-dynamic conditional random fields (LDCRF) or discriminative probabilistic latent variable models (DPLVM) are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively. In an LDCRF, like in any sequence tagging task, given a sequence of observations x = x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , the main problem the model must solve is how to assign a sequence of labels y = y 1 , … , y n {\displaystyle y_{1},\dots ,y_{n}} from one finite set
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IRCF360

Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. == Overview == The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with other robots and for simple IR inter-robot communications. To overcome certain limitation with Infra-red (IR) proximity sensing (e.g. detection of dark surfaces) the sensing module includes ambient light sensing and basic tactile sensing functionality during forward movement sensing/probing providing photovore and photophobe robot swarm behaviours and characteristics. A project named Sensorium Project was started aimed at broadening the Sensors audience beyond its typical robot sensor usage. To demonstrate the sensor's functionality, opensource Java based Integrated Development Environments (IDE) are used, such as Arduino and Processing (programming language).
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ITools Resourceome

iTools is a distributed infrastructure for managing, discovery, comparison and integration of computational biology resources. iTools employs Biositemap technology to retrieve and service meta-data about diverse bioinformatics data services, tools, and web-services. iTools is developed by the National Centers for Biomedical Computing as part of the NIH Road Map Initiative.
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Writesonic

Writesonic is an AI visibility and generative engine optimization (GEO) platform used by enterprises, digital agencies, direct-to-consumer (D2C) companies, and fast-growing brands to understand and improve how they are represented in AI-generated search and answer systems. The platform analyzes how brands appear in AI answers, compares their visibility and citations against competitors, and provides tools to create and optimize on-site content and secure mentions across third-party sources, discussion forums, and user-generated platforms that influence AI outputs. == History == Writesonic was founded by Samanyou Garg in October 2020 in San Francisco, California. The company initially operated as Magicflow before adopting its current name. In its seed round, the company raised $2.5 million from investors including Y-Combinator, HOF Capital, and Soma Capital. The company began with AI-powered content generation tools. In 2023, it expanded into AI-enhanced search engine optimization. In 2024, the company launched an AI agent specifically designed for SEO tasks, with integrations to platforms including Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console. This was among the first specialized AI agents developed for SEO automation. Around the same time, Writesonic expanded its product line into Generative engine optimization (GEO), developing tools to analyze and improve how brands are represented in AI-generated search and answer environments. However, it is currently being challenged in the market with competitors such as Profound (known for their dashboards) and Meridian (known for their execution). == Technology and features == In 2024, the company introduced an artificial intelligence agent designed to automate search engine optimization (SEO) tasks. The agent integrates with platforms such as Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console to conduct technical audits, perform keyword research, carry out competitive analysis, and assist in strategy development. It is capable of identifying content gaps, suggesting optimization measures, and generating SEO strategies using real-time data from the integrated platforms. The platform also includes features for content strategy, optimization, and management. It makes use of large language models such as GPT-5, Claude Opus 4.1, and Claude Sonnet 4.5, in combination with proprietary workflows for fact-checking, internal linking, and content structure optimization.
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Gas (app)

Gas (sometimes stylized in all caps), formerly known as Melt as well as Crush, was an American anonymous social media app. Launched in August 2022, the app is oriented towards high schoolers. The app was developed by Nikita Bier, Isaiah Turner, and former Facebook engineer Dave Schatz. Gas was largely based upon the prior tbh app developed by co-founder Nikita Bier, along with Erik Hazzard, Kyle Zaragoza, and Nicolas Ducdodon in September 2017. tbh was acquired by Facebook inc. (now Meta Platforms) on October 16, 2017, and nearly a year later in July 2018 was dissolved, owing to low usage. Gas follows a similar purpose to tbh in being a social media app oriented towards high schoolers. In the app, users participate in anonymous polls regarding pre-written complimentary statements to their peers, such as "I'd say yes if (blank) asked me out on a date," "I think (blank) is the coolest kid in school," or "would make an ugly face and still look pretty." Winners of said polls receive a "flame." The name of the app is derived from this, with "gassing someone up" being Gen Z slang for complimenting someone. Users can pay a $6.99 subscription that enables "God Mode," which shows hints regarding who voted for them in a poll. Gas overtook TikTok and BeReal as the most downloaded app on the Apple App Store in October 2022 (the app is currently not available for Android). The app has over 5.1 million downloads as of early November 2022, over a million active users and 300 thousand daily downloads as of October 2022. Currently, the app is available in Canada and the majority of the United States. On January 17, 2023, Gas was acquired by Discord, however it would remain a standalone app and its developers became Discord staff members. On October 18, 2023, Discord announced that service for Gas would be permanently ending effective November 7, 2023, due to a steep decline in users. Effective November 7, the app became completely unusable. == Controversy regarding human-trafficking == Beginning in October 2022, rumors spread largely throughout TikTok and Snapchat alleged that the app was linked to human trafficking (in particular sex trafficking). According to Bier, the rumor originated with a single user review from China on October 5, and then was disseminated through TikTok accounts with "few to no US teen followers." Although largely dismissed as a hoax by experts, who cite how the app doesn't log user locations and general anonymity, the hoax became pervasive to the extent that various police departments, school systems, and local news outlets began issuing warnings regarding the app. For instance, on October 31, 2022, the police department of Piedmont, Oklahoma issued a warning to parents, encouraging them to check their children's phones, while on November 3, the Oklahoma Oktaha Public School system stated in a Facebook post that "Children are being kidnapped in other towns and this new app is thought to be the source of predators finding their location." (both statements have since been retracted by Police Chief Scott Singer and Superintendent Jerry Needham respectively). Additionally, local medial outlets such as KOCO in Oklahoma City ran stories making similar statements. The rumor had a negative impact on the app, with downloads plateauing for a two-week period in late October and with 3% of users in a single day reportedly uninstalling the app. Revenue and ratings have also reportedly dropped and the company's social media accounts have been bombarded with comments labeling them as sex-traffickers. Additionally, the four-person development team has reportedly been bombarded with various death threats as a result.
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BRFplus

BRFplus (Business Rule Framework plus) is a business rule management system (BRMS) offered by SAP AG. BRFplus is part of the SAP NetWeaver ABAP stack. Therefore, all SAP applications that are based on SAP NetWeaver can access BRFplus within the boundaries of an SAP system. However, it is also possible to generate web services so that BRFplus rules can also be offered as a service in a SOA landscape, regardless of the software platform used by the service consumers. BRFplus development started as a supporting tool that was part of SAP Business ByDesign, an ERP solution targeted at small and medium size companies. By that time, the tool was called "Formula and Derivation Tool" (FDT). Later on, it was decided to maintain BRFplus on those codelines that serve as the basis for SAP Business Suite. With that, business rules that have been created for Business ByDesign can easily be taken over in a full-size SAP system where they are ready for use without any changes. == Overview == BRFplus offers a unified modeling and runtime environment for business rules that addresses both technical users (programmers, system administrators) as well as business users who take care of operational business processes (like procurement, bidding, tax form validation, etc.). The different requirements and usage scenarios of the different target groups can be covered with the help of the SAP authorization system and a user interface that can be individually customized. Being integrated into SAP NetWeaver, BRFplus-based applications can look at, and model, business rules from a strictly business-oriented perspective, rather than starting with the underlying technical artifacts. This is because the integration allows for direct access to the business objects available in the SAP dictionary (like customer, supplier, material, bill, etc.). In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications. Versioned business rules are needed in certain use cases for legal reasons, but they also allow for simulating the system behavior as it would have been at a particular point in time. Once the rule objects are in a consistent state and active, the system automatically generates ABAP OO classes that encapsulate the functional scope of the underlying rule object. This is done on an on-demand base and speeds up processing. The execution of functions as well as of single expressions can be simulated. The processing log of the simulation is useful for checking the implementation and for investigating problems. BRFplus applications can be exported and imported as an XML file. This is an easy way of creating a data backup. XML files can also be used for deploying rule applications throughout the company. == Main object types == === Application === The application object serves as a container for all the BRFplus objects that have been assembled to solve a particular business task. It is possible to define certain default settings on application level that are inherited by all objects that are created in the scope of that application. === Function === A function is used to connect a business application with the rule processing framework of BRFplus. The calling business application passes input values to the function which are then processed by the expressions and rulesets that are associated with the called function. The calculated result is then returned to the calling business application. === Expression types and action types === Boolean BRMS Connector Case Database Lookup Decision Table Decision Tree Formula Function Call Loop Procedure Call Random Number Search Tree Step Sequence Value Range1 XSL Transformation === Ruleset === A ruleset is a container for an arbitrary number of rule objects which in turn carry out the necessary calculations with the help of assigned expressions and actions. Instead of assigning an expression to a function, it is also possible to assign any number of rulesets to a function. When the function is called, all assigned rulesets are subsequently processed. === Data objects === BRFplus supports elementary data objects (text, number, boolean, time point, amount, quantity) as well as structures and tables. Structures can be nested. For all types of data objects it is possible to reference data objects that reside in the data dictionary of the backend system. With that, a BRFplus data object does not only inherit the type definition of the referenced object but can also access associated data like domain value lists or object documentation. === Other objects === With catalogs, it is possible to define business-specific subsets of the rule objects that reside in the system. This is helpful for hiding the complexity of a rule system, thus improving usability. Object filters are used by system administrators to ensure that for selected users, only a predefined subset of object types is visible. This is useful to enforce access rights as well as modeling policies. == Other BRM solutions offered by SAP == BRFplus is positioned as the successor product of an older business rule solution known as BRF (Business Rule Framework). For a longer transition phase, both solutions exist in parallel. However, an increasing number of SAP applications that used to be based on BRF are migrating to BRFplus. While BRFplus supports business rules for applications based on the SAP NetWeaver ABAP stack, SAP is offering another product named SAP NetWeaver Business Rules Management (BRM). BRM supports business rule modeling for the SAP NetWeaver Java stack. Both products do not compete. They are available in parallel and can be used in a collaborative approach to deal with use cases where both technology stacks are used in parallel. BRFplus comes with a special expression type that helps bridging the gap between the two different technologies. == Availability == BRFplus has been delivered to the public with SAP NetWeaver 7.0 Enhancement Package 1 for the first time. Being part of SAP NetWeaver, the usage of BRFplus is covered by the "SAP NetWeaver Foundation for Third Party Applications" license, with no additional costs. == Literature == Carsten Ziegler, Thomas Albrecht: BRFplus – Business Rule Management for ABAP Applications. Galileo Press 2011. ISBN 978-1-59229-293-6
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Writesonic

Writesonic is an AI visibility and generative engine optimization (GEO) platform used by enterprises, digital agencies, direct-to-consumer (D2C) companies, and fast-growing brands to understand and improve how they are represented in AI-generated search and answer systems. The platform analyzes how brands appear in AI answers, compares their visibility and citations against competitors, and provides tools to create and optimize on-site content and secure mentions across third-party sources, discussion forums, and user-generated platforms that influence AI outputs. == History == Writesonic was founded by Samanyou Garg in October 2020 in San Francisco, California. The company initially operated as Magicflow before adopting its current name. In its seed round, the company raised $2.5 million from investors including Y-Combinator, HOF Capital, and Soma Capital. The company began with AI-powered content generation tools. In 2023, it expanded into AI-enhanced search engine optimization. In 2024, the company launched an AI agent specifically designed for SEO tasks, with integrations to platforms including Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console. This was among the first specialized AI agents developed for SEO automation. Around the same time, Writesonic expanded its product line into Generative engine optimization (GEO), developing tools to analyze and improve how brands are represented in AI-generated search and answer environments. However, it is currently being challenged in the market with competitors such as Profound (known for their dashboards) and Meridian (known for their execution). == Technology and features == In 2024, the company introduced an artificial intelligence agent designed to automate search engine optimization (SEO) tasks. The agent integrates with platforms such as Ahrefs, Google Keyword Planner, Keywords Everywhere, and Google Search Console to conduct technical audits, perform keyword research, carry out competitive analysis, and assist in strategy development. It is capable of identifying content gaps, suggesting optimization measures, and generating SEO strategies using real-time data from the integrated platforms. The platform also includes features for content strategy, optimization, and management. It makes use of large language models such as GPT-5, Claude Opus 4.1, and Claude Sonnet 4.5, in combination with proprietary workflows for fact-checking, internal linking, and content structure optimization.
Read more →