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ActivityPub

ActivityPub is a protocol and open standard for decentralized social networking. It provides a client-to-server (C2S) API for creating and modifying content, as well as a federated server-to-server (S2S) protocol for delivering notifications and content to other servers. ActivityPub is the defining standard of the Fediverse, a decentralised social network of various social interaction models, and content types, which consists of independently managed instances of software such as Mastodon, Pixelfed and PeerTube, among others. ActivityPub is considered to be an update to the ActivityPump protocol used in pump.io, and the official W3C repository for ActivityPub is identified as a fork of ActivityPump. The creation of a new standard for decentralized social networking was prompted by the complexity of OStatus, the most commonly used protocol at the time. OStatus was built using a multitude of technologies (such as Atom, Salmon, WebSub and WebFinger), a product of the infrastructure used in GNU social (the originator and largest user of the OStatus protocol), which made it difficult to implement the protocol into new software. OStatus was also only designed to work with microblogging services, with little flexibility to the types of data that it could hold. The standard was first published by the World Wide Web Consortium (W3C) as a W3C Recommendation in January 2018 by the Social Web Working Group (SocialWG), a working group chartered to build the protocols and vocabularies needed to create a standard for social functionality. Shortly after, further development was moved to the Social Web Community Group (SocialCG), the successor to the SocialWG. == Design == ActivityPub uses the ActivityStreams 2.0 format for building its content, which itself uses JSON-LD. The three main data types used in ActivityPub are Objects, Activities and Actors. Objects are the most common data type, and can be images, videos, or more abstract items such as locations or events. Activities are actions that create and modify objects, for example a Create activity creates an object. Actors are representative of an individual, a group, an application or a service, and are the owners of objects. Every actor type contains an inbox and outbox stream, which sends and receives activities for a user. In order to publish data (for example liking an article), a user creates an activity that declares that they liked an Article object and publishes it to their outbox, where it is then delivered by the ActivityPub server via a POST request to the inboxes listed in the activity's to, bto, cc and bcc fields. The receiving servers then account for the newly received activity and update the article by adding the like action to it. === Example data === An example actor object that represents a user account: An example activity that likes an article object: An example article object: == Project status == The SocialCG previously organized a yearly free conference called ActivityPub Conf about the future of ActivityPub. Triages are held regularly to review issues pertaining to the ActivityPub and ActivityStreams 2.0 specifications as part of the SocialCG. In 2023, Germany's Sovereign Tech Fund donated €152,000 to socialweb.coop with the goal of building a new suite for testing various ActivityPub implementations and their compliance with the specification. === Adoption === The initial wave of adoption for ActivityPub (circa 2016–2018) came from software that was already using OStatus as their federation protocol, such as Mastodon, GNU social and Pleroma. Following the acquisition of Twitter by Elon Musk in 2022, many groups of users that were critical of the acquisition migrated to Mastodon, bringing new attention to the ActivityPub protocol with it. Various major social media platforms and corporations have since pledged to implement ActivityPub support, including Tumblr, Flipboard and Meta Platforms' Threads. Threads introduced crossposting to ActivityPub in 2024 for users outside of the European Economic Area, however full 2-way compatibility remains incomplete as of 2025. == Criticism == === Accidental denial-of-service attacks === Poorly optimized ActivityPub implementations can cause unintentional distributed denial-of-service (DDOS) attacks on other websites and servers, due to the decentralized nature of the network. An example would be Mastodon's implementation of OpenGraph link previews, wherein every instance that receives a post that contains a link with OpenGraph metadata will download the associated data, such as a thumbnail, in a very short timeframe, which can slow down or crash servers as a result of the sudden burst of requests. === Account migration === ActivityPub has been criticized for not natively supporting moving accounts from one server to another, forcing implementations to build their own solutions. While there has been work on building a standardized system for migrating accounts using the Move activity via the Fediverse Enhancement Proposal organization, the current proposal only allows for basic follower migration, with all other data remaining linked to the original account. === Missing content and data === ActivityPub implementations have been criticized for missing replies and parts of reply threads from remote posts, and presenting outdated statistics (e.g. likes and reposts) about remote posts. However, this isn't a problem with the ActivityPub protocol itself, but with implementations not refreshing their content for updated data when needed. == Software using ActivityPub == === Future implementations === Flarum, an internet forum software Forgejo, a Git forge and development platform === Uncertain future implementations === GitLab, a Git forge and development platform which had previously had an open issue discussing the topic, but was later closed due to the development team moving focus to other areas. Tumblr, a microblogging platform. Despite previous statements from Automattic CEO Matt Mullenweg, ActivityPub integration has been delayed indefinitely. The integration would have been implemented with its WordPress migration, as the first-party plugin for interoperability would have been used for federation. Flickr, an image and video hosting site.
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Extended affix grammar

In computer science, extended affix grammars (EAGs) are a formal grammar formalism for describing the context free and context sensitive syntax of language, both natural language and programming languages. EAGs are a member of the family of two-level grammars; more specifically, a restriction of Van Wijngaarden grammars with the specific purpose of making parsing feasible. Like Van Wijngaarden grammars, EAGs have hyperrules that form a context-free grammar except in that their nonterminals may have arguments, known as affixes, the possible values of which are supplied by another context-free grammar, the metarules. EAGs were introduced and studied by D.A. Watt in 1974; recognizers were developed at the University of Nijmegen between 1985 and 1995. The EAG compiler developed there will generate either a recogniser, a transducer, a translator, or a syntax directed editor for a language described in the EAG formalism. The formalism is quite similar to Prolog, to the extent that it borrowed its cut operator. EAGs have been used to write grammars of natural languages such as English, Spanish, and Hungarian. The aim was to verify the grammars by making them parse corpora of text (corpus linguistics); hence, parsing had to be sufficiently practical. However, the parse tree explosion problem that ambiguities in natural language tend to produce in this type of approach is worsened for EAGs because each choice of affix value may produce a separate parse, even when several different values are equivalent. The remedy proposed was to switch to the much simpler Affix Grammar over a Finite Lattice (AGFL) instead, in which metagrammars can only produce simple finite languages.
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How to Choose an AI Video Generator

Looking for the best AI video generator? An AI video generator is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI video generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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AI Sales Assistants: Free vs Paid (2026)

Trying to pick the best AI sales assistant? An AI sales assistant is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI sales assistant slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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BLOOM (language model)

The BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) is an open-access large language model (LLM) released in 2022. It was created by a volunteer-driven research effort to provide a transparently-created alternative to proprietary AI models. With 176 billion parameters, BLOOM is a transformer-based autoregressive model designed to generate text in 46 natural languages and 13 programming languages. The model is distributed under the project's "Responsible AI License". == Development == BLOOM is the main outcome of the BigScience initiative, a one-year-long research workshop. The project was coordinated by Hugging Face using funding from the French government and involved several hundred volunteer researchers and engineers from academia and the private sector. The model was trained between March and July 2022 on the Jean Zay public supercomputer in France, managed by GENCI and IDRIS (CNRS). Unlike GPT-3, BLOOM was trained to be multilingual. The source code is released under the Apache 2.0 license. The model's parameters are released under BigScience's "Responsible AI License" (RAIL), which grants open access and reuse rights but with some usage restrictions. BLOOM was used in the chatbots BLOOMChat and HuggingChat due to its multilingual abilities. BLOOM's training corpus, named ROOTS, combines data extracted from the then-latest version of the web-based OSCAR corpus (38% of ROOTS) and newly collected data extracted from a manually selected and documented list of language data sources. In total, the model was trained on approximately 366 billion (1.6TB) tokens. It was developed using the open-source libraries DeepSpeed Megatron. BigScience then released xP3, a multilingual dataset for LLM supervised learning. It also released BLOOMZ, a variant of BLOOM fine-tuned on xP3 to follow instructions.
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Diane Litman

Diane Litman is an American professor of computer science at the University of Pittsburgh. She also jointly holds the positions of senior scientist with the Learning Research and Development Center and faculty with the Intelligent Systems department. Litman is noted for her work in the areas of artificial intelligence, computational linguistics, knowledge representation and reasoning, natural language processing, and user modeling. == Education == Litman did her undergraduate studies at the College of William and Mary and her master's and PhD degrees at the University of Rochester. == Career == Before joining the University of Pittsburgh, she was an assistant professor at Columbia University. She additionally held the position of a research scientist in the Artificial Intelligence Principles Research Department Laboratory at AT&T Labs. Litman has held the position of Chair of the North American Chapter of the Association for Computational Linguistics two times, elected twice for the position, whose tenure lasts four years. She is also a distinguished member of the executive committee of the Association for Computational Linguistics, and a member of the editorial boards of Computational Linguistics and User Modeling and User-Adapted Interaction. She has also held the position of Leverhulme Professor at the University of Edinburgh. Litman was the keynote speaker at the Speech and Language Technology in Education 2013 symposium, the 2006 SIGdial Meeting on Discourse and Dialogue, and at the 2008 Symposium of the Annual Meeting of the Society for the Study of Artificial Intelligence and Simulation of Behaviour. She also sits on the board of the several interest groups, including the International Speech Communication Association's Special Interest Group on Speech and Language Technology in Education. Litman has served as chair, organizer, and a senior member of numerous committees of peer-reviewed scientific journals. == Awards and recognition == She has also co-authored numerous award-winning papers and was awarded senior member status by the Association for the Advancement of Artificial Intelligence in 2011, an award designed to honor those who have "achieved significant accomplishments within the field of artificial intelligence."
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Top 10 AI Clip Makers Compared (2026)

Comparing the best AI clip maker? An AI clip maker is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI clip maker slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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How to Choose an AI Customer-support Bot

Comparing the best AI customer-support bot? An AI customer-support bot is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI customer-support bot slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Application permissions

Permissions are a means of controlling and regulating access to specific system- and device-level functions by software. Typically, types of permissions cover functions that may have privacy implications, such as the ability to access a device's hardware features (including the camera and microphone), and personal data (such as storage devices, contacts lists, and the user's present geographical location). Permissions are typically declared in an application's manifest, and certain permissions must be specifically granted at runtime by the user—who may revoke the permission at any time. Permission systems are common on mobile operating systems, where permissions needed by specific apps must be disclosed via the platform's app store. == Mobile devices == On mobile operating systems for smartphones and tablets, typical types of permissions regulate: Access to storage and personal information, such as contacts, calendar appointments, etc. Location tracking. Access to the device's internal camera and/or microphone. Access to biometric sensors, including fingerprint readers and other health sensors.. Internet access. Access to communications interfaces (including their hardware identifiers and signal strength where applicable, and requests to enable them), such as Bluetooth, Wi-Fi, NFC, and others. Making and receiving phone calls. Sending and reading text messages The ability to perform in-app purchases. The ability to "overlay" themselves within other apps. Installing, deleting and otherwise managing applications. Authentication tokens (e.g., OAuth tokens) from web services stored in system storage for sharing between apps. Prior to Android 6.0 "Marshmallow", permissions were automatically granted to apps at runtime, and they were presented upon installation in Google Play Store. Since Marshmallow, certain permissions now require the app to request permission at runtime by the user. These permissions may also be revoked at any time via Android's settings menu. Usage of permissions on Android are sometimes abused by app developers to gather personal information and deliver advertising; in particular, apps for using a phone's camera flash as a flashlight (which have grown largely redundant due to the integration of such functionality at the system level on later versions of Android) have been known to require a large array of unnecessary permissions beyond what is actually needed for the stated functionality. iOS imposes a similar requirement for permissions to be granted at runtime, with particular controls offered for enabling of Bluetooth, Wi-Fi, and location tracking. == WebPermissions == WebPermissions is a permission system for web browsers. When a web application needs some data behind permission, it must request it first. When it does it, a user sees a window asking him to make a choice. The choice is remembered, but can be cleared lately. Currently the following resources are controlled: geolocation desktop notifications service workers sensors audio capturing devices, like sound cards, and their model names and characteristics video capturing devices, like cameras, and their identifiers and characteristics == Analysis == The permission-based access control model assigns access privileges for certain data objects to application. This is a derivative of the discretionary access control model. The access permissions are usually granted in the context of a specific user on a specific device. Permissions are granted permanently with few automatic restrictions. In some cases permissions are implemented in 'all-or-nothing' approach: a user either has to grant all the required permissions to access the application or the user can not access the application. There is still a lack of transparency when the permission is used by a program or application to access the data protected by the permission access control mechanism. Even if a user can revoke a permission, the app can blackmail a user by refusing to operate, for example by just crashing or asking user to grant the permission again in order to access the application. The permission mechanism has been widely criticized by researchers for several reasons, including; Intransparency of personal data extraction and surveillance, including the creation of a false sense of security; End-user fatigue of micro-managing access permissions leading to a fatalistic acceptance of surveillance and intransparency; Massive data extraction and personal surveillance carried out once the permissions are granted. Some apps, such as XPrivacy and Mockdroid spoof data in order to act as a measure for privacy. Further transparency methods include longitudinal behavioural profiling and multiple-source privacy analysis of app data access.
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Datacap

Datacap (an IBM Company), a privately owned company, manufactures and sells computer software, and services. Datacap's first product, Paper Keyboard, was a "forms processing" product and shipped in 1989. In August 2010, IBM announced that it had acquired Datacap for an undisclosed amount. == Overview == Datacap sells products through a value-added distribution network worldwide. The software is classified as "enterprise software", meaning that it requires trained professionals to install and configure. Although the Company has focused on providing solutions for scanning paper documents, most recently Company materials have emphasized customer requirements to handle electronic documents ("eDocs"), documents being received into an organization electronically (usually email). Datacap claims that its software is unique because of the rules engine ("Rulerunner") used for processing inbound documents, including performing the image processing (deskew, noise removal, etc.), optical character recognition (OCR), intelligent character recognition (ICR), validations, and export-release formatting of extracted data to target ERP and line of business application.
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METAL MT

A machine translation system developed at the University of Texas and at Siemens which ran on Lisp Machines. == Background == Originally titled the Linguistics Research System (LRS), it was later renamed METAL (Mechanical Translation and Analysis of Languages). It started life as a German-English system funded by the USAF. == 1980 == A copy of the Weidner Multi-Lingual Word Processing software was requested by the German Government for the Siemens Corporation of Germany in September 1980 and was nicknamed the Siemens-Weidner Engine (originally English-German). This revolutionary multilingual word processing engine became foundational in the development of the Metal MT project, according to John White of the Siemens Corporation. After the Metal MT, development Rights to the Siemens-Weidner Engine were sold to a Belgium company, Lernout & Hauspie. The Siemens copy of the Weidner Multilingual Word Processing software has since been acquired through the purchase of assets of Lernout & Hauspie by Bowne Global Solutions, Inc., which was later acquired by Lionbridge Technologies, Inc. and is demonstrated in their itranslator software.
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Is an AI Copywriting Tool Worth It in 2026?

Looking for the best AI copywriting tool? An AI copywriting tool is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI copywriting tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Comparison of raster graphics editors

Raster graphics editors can be compared by many variables, including availability. == List == == General information == Basic general information about the editor: creator, company, license, etc. == Operating system support == The operating systems on which the editors can run natively, that is, without emulation, virtual machines or compatibility layers. In other words, the software must be specifically coded for the operation system; for example, Adobe Photoshop for Windows running on Linux with Wine does not fit. == Features == == Color spaces == == File support ==
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Barbara Di Eugenio

Barbara Di Eugenio is an Italian-American computer scientist, the Collegiate Warren S. McCulloch Professor of Computer Science at the University of Illinois Chicago. Her research focuses on natural language processing and its applications to human–computer interaction, educational technology, and artificial intelligence in healthcare. == Education and career == Di Eugenio is originally from Turin. After an undergraduate education in Italy, she completed her Ph.D. in computer and information science in 1993 at the University of Pennsylvania. Her dissertation, Understanding Natural Language Instructions: A Computational Approach to Purpose Clauses, was supervised by Bonnie Webber. She became a faculty member at the University of Illinois Chicago in 1999, and at that time was the only woman faculty member in the Department of Electrical Engineering and Computer Science. == Recognition == In 2022, Di Eugenio received the Zenith Award of the Association for Women in Science. She was named as a Fellow of the Association for Computational Linguistics in 2023, "for outstanding contributions to natural language generation; intelligent tutoring systems; discourse; intercoder agreement; and applying multimodal interactive systems to health".
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Co-occurrence

In linguistics, co-occurrence or cooccurrence (in older texts often shown with diacritic as coöccurrence) is an above-chance frequency of ordered occurrence of two adjacent terms in a text corpus. Co-occurrence in this linguistic sense can be interpreted as an indicator of semantic proximity or an idiomatic expression. Corpus linguistics and its statistical analyses can reveal (regularity of) patterns of co-occurrences within a language and enable the working out of typical collocations for its lexical items. A co-occurrence restriction is identified when linguistic elements never occur together. Analysis of these restrictions can lead to discoveries about the structure and development of a language. Co-occurrence can be seen an extension of word counting in higher dimensions. Co-occurrence can be quantitatively described using measures like a massive correlation or mutual information. Co-occurrence information and knowledge of co-occurring words may be relevant in analysis of language for the purposes of large language models, part of the emerging field of artificial intelligence, and helpful in word games such as scrabble.
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