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.
Statistical shape analysis
Statistical shape analysis is an analysis of the geometrical properties of some given set of shapes by statistical methods. For instance, it could be used to quantify differences between male and female gorilla skull shapes, normal and pathological bone shapes, leaf outlines with and without herbivory by insects, etc. Important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate mean shapes from (possibly random) samples, to estimate shape variability within samples, to perform clustering and to test for differences between shapes. One of the main methods used is principal component analysis (PCA). Statistical shape analysis has applications in various fields, including medical imaging, computer vision, computational anatomy, sensor measurement, and geographical profiling. == Landmark-based techniques == In the point distribution model, a shape is determined by a finite set of coordinate points, known as landmark points. These landmark points often correspond to important identifiable features such as the corners of the eyes. Once the points are collected some form of registration is undertaken. This can be a baseline methods used by Fred Bookstein for geometric morphometrics in anthropology. Or an approach like Procrustes analysis which finds an average shape. David George Kendall investigated the statistical distribution of the shape of triangles, and represented each triangle by a point on a sphere. He used this distribution on the sphere to investigate ley lines and whether three stones were more likely to be co-linear than might be expected. Statistical distribution like the Kent distribution can be used to analyse the distribution of such spaces. Alternatively, shapes can be represented by curves or surfaces representing their contours, by the spatial region they occupy. == Shape deformations == Differences between shapes can be quantified by investigating deformations transforming one shape into another. In particular a diffeomorphism preserves smoothness in the deformation. This was pioneered in D'Arcy Thompson's On Growth and Form before the advent of computers. Deformations can be interpreted as resulting from a force applied to the shape. Mathematically, a deformation is defined as a mapping from a shape x to a shape y by a transformation function Φ {\displaystyle \Phi } , i.e., y = Φ ( x ) {\displaystyle y=\Phi (x)} . Given a notion of size of deformations, the distance between two shapes can be defined as the size of the smallest deformation between these shapes. Diffeomorphometry is the focus on comparison of shapes and forms with a metric structure based on diffeomorphisms, and is central to the field of Computational anatomy. Diffeomorphic registration, introduced in the 90's, is now an important player with existing codes bases organized around ANTS, DARTEL, DEMONS, LDDMM, StationaryLDDMM, and FastLDDMM are examples of actively used computational codes for constructing correspondences between coordinate systems based on sparse features and dense images. Voxel-based morphometry (VBM) is an important technology built on many of these principles. Methods based on diffeomorphic flows are also used. For example, deformations could be diffeomorphisms of the ambient space, resulting in the LDDMM (Large Deformation Diffeomorphic Metric Mapping) framework for shape comparison.
Composite Capability/Preference Profiles
Composite Capability/Preference Profiles (CC/PP) is a specification for defining capabilities and preferences of user agents (also known as "delivery context"). The delivery context can be used to guide the process of tailoring content for a user agent. CC/PP is a vocabulary extension of the Resource Description Framework (RDF). The CC/PP specification is maintained by the W3C's Ubiquitous Web Applications Working Group (UWAWG) Working Group. == History == Composite Capability/Preference Profiles (CC/PP): Structure and Vocabularies 1.0 became a W3C recommendation on 15 January 2004. A "Last-Call Working-Draft" of CC/PP 2.0 was issued in April 2007
ChessMachine
The ChessMachine was a chess computer sold between 1991 and 1995 by TASC (The Advanced Software Company). It was unique at the time for incorporating both an ARM2 coprocessor for the chess engine on an ISA card which plugged into an IBM PC and a software interface running on the PC to display a chess board and control the engine. The ISA card was sold with a CPU running at either 16 MHz or 32 MHz, and 128 KB, 512 KB, or 1 MB of onboard memory for transposition tables. This made economic sense at the time of introduction because mainstream PCs were only running from 10 MHz to 25 MHz. Two engines were sold with the card: The King by Johann de Koning and Gideon by Ed Schröder. Gideon was famed for winning two World Computer Chess Championships on this hardware. The King later became the engine used in the popular Chessmaster series of chess programs. TASC later incorporated the technology into a dedicated unit, sold from 1993 to 1997. There were two models, the R30 and R40, running at 30 MHz and 40 MHz respectively, and having 512 KB and 1 MB of transposition tables, respectively. The SmartBoard, a wooden sensory board, was connected to the units, which were in tiny boxes approximately the size of chess clocks. They were only sold with The King chess engine. This was the end of the era of strong dedicated chess computers, and these two models are acknowledged as the strongest dedicated chess computers that were ever sold. At the height of its strength, the R30 attained a rating over 2350 on computer rating lists, higher than any other dedicated unit. According to the SSDF rating list, the R30 held its own against its contemporary programs running a Pentium-90 MHz and won against other dedicated units.
Global call for AI red lines
The global call for AI red lines is a declaration made on 22 September 2025 calling on governments to define and internationally prohibit unacceptable AI uses and behaviors. The online declaration was announced by Nobel Peace Prize laureate Maria Ressa at the 80th United Nations General Assembly high-level week. The declaration was initially signed by 200 prominent politicians and scientists, including 10 Nobel Prize winners. The call does not specify which red lines to set, but suggests several, such as banning bioweapon design, mass surveillance or AI impersonation. == The declaration == The declaration was published online as an open letter on 22 September 2025. Nobel Peace Prize laureate Maria Ressa announced it in her opening speech at the 80th United Nations General Assembly high-level week in New York, urging governments to "define what AI should never be allowed to do" and "establish clear international boundaries to prevent universally unacceptable risks for A.I." The initiative was organized by three nonprofit organisations: the French Center for AI Safety (CeSIA), The Future Society, and the Center for Human-Compatible Artificial Intelligence (CHAI). The letter argues that humanity faces risks such as engineered pandemics, widespread disinformation, large-scale manipulation, unemployment and loss of control. Proponents argue that national laws are insufficient to address these risks and that "an international agreement on clear and verifiable red lines is necessary". They urge governments to reach an agreement by the end of 2026, and called for robust enforcement mechanisms and the creation of an independent organisation to implement it. The letter does not call for specific red lines, but suggests the possibility of banning lethal autonomous weapons, autonomous replication of AI systems and the use of AI in nuclear warfare. Other examples of possible red lines include social scoring, mass surveillance, bioweapon design, AI-generated child sexual abuse material and AI impersonation. A red line could prohibit either AI behaviors (what AI systems should be guaranteed to never do even if asked to) or AI uses. == Signatories == When published, the online declaration was signed by more than 200 prominent politicians and scientists, including 10 Nobel Prize winners. Signers include former president of Colombia Juan Manuel Santos and researchers Geoffrey Hinton and Yoshua Bengio. It also includes popular authors like Stephen Fry and Yuval Noah Harari. The letter received support from European lawmakers, including former Italian prime minister Enrico Letta, and former president of Ireland Mary Robinson. == Development of red lines == As of 2025, there is no global red line on AI. Some regional red lines exist, such as with the uses deemed "unacceptable" by the AI Act in Europe, and with the US-China agreement not to leave to AI the decision of whether to launch nuclear weapons. At the United Nations Security Council, days after the declaration, Michael Kratsios, Donald Trump's director of the White House Office of Science and Technology Policy, said "We totally reject all efforts by international bodies to assert centralized control and global governance of AI." The topic of AI red lines gained prominence in 2026 with the dispute between Anthropic and the Department of Defense (DoD), which resulted from the DoD requesting Anthropic to remove contractual red lines on fully autonomous weapons and mass domestic surveillance. The event led employees from Google and OpenAI as well as Senate Democrats to further call for red lines on military use of AI. Senator Adam Schiff proposed a bill to "codify" Anthropic's red lines.
Reflection lines
Engineers use reflection lines to judge a surface's quality. Reflection lines reveal surface flaws, particularly discontinuities in normals indicating that the surface is not C 2 {\displaystyle C^{2}} . Reflection lines may be created and examined on physical surfaces or virtual surfaces with the help of computer graphics. For example, the shiny surface of an automobile body is illuminated with reflection lines by surrounding the car with parallel light sources. Virtually, a surface can be rendered with reflection lines by modulating the surfaces point-wise color according to a simple calculation involving the surface normal, viewing direction and a square wave environment map. == Mathematical definition == Consider a point p {\displaystyle p} on a surface M {\displaystyle M} with (normalized) normal n {\displaystyle n} . If an observer views this point from infinity at view direction v {\displaystyle v} then the reflected view direction r {\displaystyle r} is: r = v − 2 ( n ⋅ v ) n . {\displaystyle r=v-2(n\cdot v)n.} (The vector v {\displaystyle v} is decomposed into its normal part v n = ( n ⋅ v ) v {\displaystyle v_{n}=(n\cdot v)v} and tangential part v t = v − v n {\displaystyle v_{t}=v-v_{n}} . Upon reflection, the tangential part is kept and the normal part is negated.) For reflection lines we consider the surface M {\displaystyle M} surrounded by parallel lines with direction a {\displaystyle a} , representing infinite, non-dispersive light sources. For each point p {\displaystyle p} on M {\displaystyle M} we determine which line is seen from direction v {\displaystyle v} . The position on each line is of no interest. Define the vector r p {\displaystyle r_{p}} to be the reflection direction r {\displaystyle r} projected onto a plane P {\displaystyle P} that is orthogonal to a {\displaystyle a} : r p = r − ( r ⋅ a ) a {\displaystyle r_{p}=r-(r\cdot a)a} and similarly let v p {\displaystyle v_{p}} be the viewing direction projected onto P {\displaystyle P} : v p = v − ( v ⋅ a ) a {\displaystyle v_{p}=v-(v\cdot a)a} Finally, define v o {\displaystyle v_{o}} to be the direction lying in P {\displaystyle P} perpendicular to a {\displaystyle a} and v p {\displaystyle v_{p}} : v o = a × v p {\displaystyle v_{o}=a\times v_{p}} Using these vectors, the reflection line function θ ( p ) : M → ( − π , π ] {\displaystyle \theta (p):M\rightarrow (-\pi ,\pi ]} is a scalar function mapping points p {\displaystyle p} on the surface to angles between v p {\displaystyle v_{p}} and r p {\displaystyle r_{p}} : θ = arctan ( r p ⋅ v o , r p ⋅ v p ) {\displaystyle \theta =\arctan {(r_{p}\cdot v_{o},r_{p}\cdot v_{p})}} where a r c t a n ( y , x ) {\displaystyle arctan(y,x)} is the atan2 function producing a number in the range ( − π , π ] {\displaystyle (-\pi ,\pi ]} . ( v p {\displaystyle v_{p}} and v o {\displaystyle v_{o}} can be viewed as a local coordinate system in P {\displaystyle P} with x {\displaystyle x} -axis in direction v p {\displaystyle v_{p}} and y {\displaystyle y} -axis in direction v o {\displaystyle v_{o}} .) Finally, to render the reflection lines positive values θ > 0 {\displaystyle \theta >0} are mapped to a light color and non-positive values to a dark color. == Highlight lines == Highlight lines are a view-independent alternative to reflection lines. Here the projected normal is directly compared against some arbitrary vector x {\displaystyle x} perpendicular to the light source: θ = arctan ( n a ⋅ a ⊥ , n a ⋅ x ) {\displaystyle \theta =\arctan {(n_{a}\cdot a^{\perp },n_{a}\cdot x)}} where n a {\displaystyle n_{a}} is the surface normal projected on the light source plane P {\displaystyle P} : n a ^ / | n a ^ | , n a ^ = n − ( n ⋅ a ) a {\displaystyle {\hat {n_{a}}}/|{\hat {n_{a}}}|,{\hat {n_{a}}}=n-(n\cdot a)a} The relationship between reflection lines and highlight lines is likened to that between specular and diffuse shading.
Fooocus
Fooocus is an open source generative artificial intelligence program that allows users to generate images from a text prompt. It uses Stable Diffusion XL as the base model for its image capabilities as well as a collection of default settings and prompts to make the image generation process more streamlined. == History == Fooocus was created by Lvmin Zhang, a doctoral student at Stanford University who previously studied at the Chinese University of Hong Kong and Soochow University. He is also the main author of ControlNet, which has been adopted by many other Stable Diffusion interfaces, such as AUTOMATIC1111 and ComfyUI. As of 9 July 2024, the project had 38.1k stars on GitHub. == Features == Fooocus' main feature is that it is easy to set up and does not require users to manually configure model parameters to achieve desirable results. According to the project, it uses GPT-2 to automatically add more detail to the user's prompts. It includes common extensions such LCM low-rank adaptation by default which allows for faster generation speed. Fooocus prefers a photographic style by default, with a list of predefined styles to choose from. While Fooocus aims to provide good results out of the box, it also includes an "advanced" tab that allows for user customization. The user interface is based on Gradio. It appears this project has not been updated in over 1 year. The latest git update for Fooocus was in Aug 12, 2024.