The Most Dangerous Writing App is a web application for free writing that combats writer's block by deleting all progress if the user stops typing for five seconds. It is targeted at creative writers who want to write first drafts without worrying about editing or formatting. == Features == The app is designed to "shut down your inner editor and get you into a state of flow", referring to the psychological concept of being in a flow state. Users start a writing session by choosing a time or word limit, and can only save or download their work if they complete the set limit without interruption. An optional "hardcore mode" blurs out everything the user has written so far, making it impossible to edit before finishing the writing session. == History == The Most Dangerous Writing App was created by software engineer Manuel Ebert and was released as free, open source software on February 29, 2016. It was reviewed by Wired, Forbes, Vogue, Huffington Post, The Verge, The Next Web, and others. It has been used in free writing contests and is recommended by NaNoWriMo. In April 2019, The Most Dangerous Writing App was acquired by Squibler, but the original version remains freely accessible.
Text Retrieval Conference
The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity (part of the office of the Director of National Intelligence), and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology. TREC's evaluation protocols have improved many search technologies. A 2010 study estimated that "without TREC, U.S. Internet users would have spent up to 3.15 billion additional hours using web search engines between 1999 and 2009." Hal Varian the Chief Economist at Google wrote that "The TREC data revitalized research on information retrieval. Having a standard, widely available, and carefully constructed set of data laid the groundwork for further innovation in this field." Each track has a challenge wherein NIST provides participating groups with data sets and test problems. Depending on track, test problems might be questions, topics, or target extractable features. Uniform scoring is performed so the systems can be fairly evaluated. After evaluation of the results, a workshop provides a place for participants to collect together thoughts and ideas and present current and future research work.Text Retrieval Conference started in 1992, funded by DARPA (US Defense Advanced Research Project) and run by NIST. Its purpose was to support research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies. == Goals == Encourage retrieval search based on large text collections Increase communication among industry, academia, and government by creating an open forum for the exchange of research ideas Speed the transfer of technology from research labs into commercial products by demonstrating substantial improvements retrieval methodologies on real world problems To increase the availability of appropriate evaluation techniques for use by industry and academia including development of new evaluation techniques more applicable to current systems TREC is overseen by a program committee consisting of representatives from government, industry, and academia. For each TREC, NIST provide a set of documents and questions. Participants run their own retrieval system on the data and return to NIST a list of retrieved top-ranked documents. NIST pools the individual result judges the retrieved documents for correctness and evaluates the results. The TREC cycle ends with a workshop that is a forum for participants to share their experiences. == Relevance judgments in TREC == TREC defines relevance as: "If you were writing a report on the subject of the topic and would use the information contained in the document in the report, then the document is relevant." Most TREC retrieval tasks use binary relevance: a document is either relevant or not relevant. Some TREC tasks use graded relevance, capturing multiple degrees of relevance. Most TREC collections are too large to perform complete relevance assessment; for these collections it is impossible to calculate the absolute recall for each query. To decide which documents to assess, TREC usually uses a method call pooling. In this method, the top-ranked n documents from each contributing run are aggregated, and the resulting document set is judged completely. == Various TRECs == In 1992 TREC-1 was held at NIST. The first conference attracted 28 groups of researchers from academia and industry. It demonstrated a wide range of different approaches to the retrieval of text from large document collections .Finally TREC1 revealed the facts that automatic construction of queries from natural language query statements seems to work. Techniques based on natural language processing were no better no worse than those based on vector or probabilistic approach. TREC2 Took place in August 1993. 31 group of researchers participated in this. Two types of retrieval were examined. Retrieval using an ‘ad hoc’ query and retrieval using a ‘routing' query In TREC-3 a small group experiments worked with Spanish language collection and others dealt with interactive query formulation in multiple databases TREC-4 they made even shorter to investigate the problems with very short user statements TREC-5 includes both short and long versions of the topics with the goal of carrying out deeper investigation into which types of techniques work well on various lengths of topics In TREC-6 Three new tracks speech, cross language, high precision information retrieval were introduced. The goal of cross language information retrieval is to facilitate research on system that are able to retrieve relevant document regardless of language of the source document TREC-7 contained seven tracks out of which two were new Query track and very large corpus track. The goal of the query track was to create a large query collection TREC-8 contain seven tracks out of which two –question answering and web tracks were new. The objective of QA query is to explore the possibilities of providing answers to specific natural language queries TREC-9 Includes seven tracks In TREC-10 Video tracks introduced Video tracks design to promote research in content based retrieval from digital video In TREC-11 Novelty tracks introduced. The goal of novelty track is to investigate systems abilities to locate relevant and new information within the ranked set of documents returned by a traditional document retrieval system TREC-12 held in 2003 added three new tracks; Genome track, robust retrieval track, HARD (Highly Accurate Retrieval from Documents) == Tracks == === Current tracks === New tracks are added as new research needs are identified, this list is current for TREC 2018. CENTRE Track – Goal: run in parallel CLEF 2018, NTCIR-14, TREC 2018 to develop and tune an IR reproducibility evaluation protocol (new track for 2018). Common Core Track – Goal: an ad hoc search task over news documents. Complex Answer Retrieval (CAR) – Goal: to develop systems capable of answering complex information needs by collating information from an entire corpus. Incident Streams Track – Goal: to research technologies to automatically process social media streams during emergency situations (new track for TREC 2018). The News Track – Goal: partnership with The Washington Post to develop test collections in news environment (new for 2018). Precision Medicine Track – Goal: a specialization of the Clinical Decision Support track to focus on linking oncology patient data to clinical trials. Real-Time Summarization Track (RTS) – Goal: to explore techniques for real-time update summaries from social media streams. === Past tracks === Chemical Track – Goal: to develop and evaluate technology for large scale search in chemistry-related documents, including academic papers and patents, to better meet the needs of professional searchers, and specifically patent searchers and chemists. Clinical Decision Support Track – Goal: to investigate techniques for linking medical cases to information relevant for patient care Contextual Suggestion Track – Goal: to investigate search techniques for complex information needs that are highly dependent on context and user interests. Crowdsourcing Track – Goal: to provide a collaborative venue for exploring crowdsourcing methods both for evaluating search and for performing search tasks. Genomics Track – Goal: to study the retrieval of genomic data, not just gene sequences but also supporting documentation such as research papers, lab reports, etc. Last ran on TREC 2007. Dynamic Domain Track – Goal: to investigate domain-specific search algorithms that adapt to the dynamic information needs of professional users as they explore in complex domains. Enterprise Track – Goal: to study search over the data of an organization to complete some task. Last ran on TREC 2008. Entity Track – Goal: to perform entity-related search on Web data. These search tasks (such as finding entities and properties of entities) address common information needs that are not that well modeled as ad hoc document search. Cross-Language Track – Goal: to investigate the ability of retrieval systems to find documents topically regardless of source language. After 1999, this track spun off into CLEF. FedWeb Track – Goal: to select best resources to forward a query to, and merge the results so that most relevant are on the top. Federated Web Search Track – Goal: to investigate techniques for the selection and combination of search results from a large number of real on-line web search services. Filtering Track – Goal: to binarily decide retrieval of new
End-to-end encryption
End-to-end encryption (E2EE) is a method of implementing a secure communication system where only the sender and intended recipient can read the messages. No one else, including the system provider, telecom providers, Internet providers or malicious actors, can access the cryptographic keys needed to read or send messages. End-to-end encryption prevents data from being read or secretly modified, except by the sender and intended recipients. In many applications, messages are relayed from a sender to some recipients by a service provider. In an E2EE-enabled service, messages are encrypted on the sender's device such that no third party, including the service provider, has the means to decrypt them. The recipients retrieve encrypted messages and decrypt them independently on their own devices. Since third parties cannot decrypt the data being communicated or stored, services with E2EE are better at protecting user data from data breaches and espionage. Computer security experts, digital freedom organizations, and human rights activists advocate for the use of E2EE due to its security and privacy benefits, including its ability to resist mass surveillance. Popular messaging apps like WhatsApp, iMessage, Facebook Messenger, and Signal use end-to-end encryption for chat messages, with some also supporting E2EE of voice and video calls. As of May 2025, WhatsApp is the most widely used E2EE messaging service, with over 3 billion users. Meanwhile, Signal with an estimated 70 million users, is regarded as the current gold standard in secure messaging by cryptographers, protestors, and journalists. Since end-to-end encrypted services cannot offer decrypted messages in response to government requests, the proliferation of E2EE has been met with controversy. Around the world, governments, law enforcement agencies, and child protection groups have expressed concerns over its impact on criminal investigations. As of 2025, some governments have successfully passed legislation targeting E2EE, such as Australia's Telecommunications and Other Legislation Amendment Act (2018) and the Online Safety Act (2023) in the UK. Other attempts at restricting E2EE include the EARN IT Act in the US and the Child Sexual Abuse Regulation in the EU.[1] Nevertheless, some government bodies such as the UK's Information Commissioner's Office and the US's Cybersecurity and Infrastructure Security Agency (CISA) have argued for the use of E2EE, with Jeff Greene of the CISA advising that "encryption is your friend" following the discovery of the Salt Typhoon espionage campaign in 2024. == Definitions == End-to-end encryption is a means of ensuring the security of communications in applications like secure messaging. Under E2EE, messages are encrypted on the sender's device such that they can be decoded only by the final recipient's device. In many non-E2EE messaging systems, including email and many chat platforms, messages pass through intermediaries and are stored by a third party service provider, from which they are retrieved by the recipient. Even if messages are encrypted, they are only encrypted 'in transit', and are thus accessible by the service provider. Server-side disk encryption is also distinct from E2EE because it does not prevent the service provider from viewing the information, as they have the encryption keys and can simply decrypt it. The term "end-to-end encryption" originally only meant that the communication is never decrypted during its transport from the sender to the receiver. For example, around 2003, E2EE was proposed as an additional layer of encryption for GSM or TETRA, in addition to the existing radio encryption protecting the communication between the mobile device and the network infrastructure. This has been standardized by SFPG for TETRA. Note that in TETRA, the keys are generated by a Key Management Centre (KMC) or a Key Management Facility (KMF), not by the communicating users. Later, around 2014, the meaning of "end-to-end encryption" started to evolve when WhatsApp encrypted a portion of its network, requiring that not only the communication stays encrypted during transport, but also that the provider of the communication service is not able to decrypt the communications—maliciously or when requested by law enforcement agencies. Similarly, messages must be undecryptable in transit by attackers through man-in-the-middle attacks. This new meaning is now the widely accepted one. == Motivations == The lack of end-to-end encryption can allow service providers to easily provide search and other features, or to scan for illegal and unacceptable content. However, it also means that content can be read by anyone who has access to the data stored by the service provider, by design or via a backdoor. This can be a concern in many cases where privacy is important, such as in governmental and military communications, financial transactions, and when sensitive information such as health and biometric data are sent. If this content were shared without E2EE, a malicious actor or adversarial government could obtain it through unauthorized access or subpoenas targeted at the service provider. E2EE alone does not guarantee privacy or security. For example, the data may be held unencrypted on the user's own device or accessed through their own app if their credentials are compromised. == Modern implementations == === Messaging === In May 2026, Meta ended support for end-to-end encryption (E2EE) on Instagram, reversing a previous commitment to expand the technology across its messaging services. The company justified the move as a measure to mitigate fraudulent activity and facilitate the detection of harmful content. The decision highlighted a conflict between digital privacy and online safety; while child protection organizations supported the change to better identify predatory behavior, privacy advocates argued that removing E2EE compromises user security. As of 2025, messaging apps like Signal and WhatsApp are designed to exclusively use end-to-end encryption. Both Signal and WhatsApp use the Signal Protocol. Other messaging apps and protocols that support end-to-end encryption include Facebook Messenger, iMessage, Telegram, Matrix, and Keybase. Although Telegram supports end-to-end encryption, it has been criticized for not enabling it by default, instead supporting E2EE through opt-in "secret chats". As of 2020, Telegram did not support E2EE for group chats and no E2EE on its desktop clients. In 2022, after controversy over the use of Facebook Messenger messages in an abortion lawsuit in Nebraska, Facebook added support for end-to-end encryption in the Messenger app. Writing for Wired, technologist Albert Fox Cahn criticized Messenger's approach to end-to-end encryption, which required the user to opt into E2EE for each conversation and split the message thread into two chats which were easy for users to confuse. In December 2023, Facebook announced plans to enable end-to-end encryption by default despite pressure from British law enforcement agencies. As of 2016, many server-based communications systems did not include end-to-end encryption. These systems can only guarantee the protection of communications between clients and servers, meaning that users have to trust the third parties who are running the servers with the sensitive content. End-to-end encryption is regarded as safer because it reduces the number of parties who might be able to interfere or break the encryption. In the case of instant messaging, users may use a third-party client or plugin to implement an end-to-end encryption scheme over an otherwise non-E2EE protocol. === Audio and video conferencing === Signal and WhatsApp use end-to-end encryption for audio and video calls. Since 2020, Signal has also supported end-to-encrypted video calls. In 2024, Discord added end-to-end encryption for audio and video calls, voice channels, and certain live streams. However, they had no plans to implement E2EE for messages. In 2020, after acquiring Keybase, Zoom announced end-to-end encryption would be limited to paid accounts. Following criticism from human rights advocates, Zoom extended the feature to all users with accounts. In 2021, Zoom settled an $85M class action lawsuit over past misrepresentation about end-to-end encryption. The FTC confirmed Zoom previously retained access to meeting keys. === Other uses === Some encrypted backup and file sharing services provide client-side encryption. Nextcloud and MEGA, offer end-to-end encryption of shared files. The term "end-to-end encryption" is sometimes incorrectly used to describe client-side encryption. Some non-E2EE systems, such as Lavabit and Hushmail, have described themselves as offering "end-to-end" encryption when they did not. == Law enforcement and regulation == In 2022, Facebook Messenger came under scrutiny because the messages between a mother and daughter in Nebraska were used to seek criminal charges in an abortion-rel
Creator economy
The creator economy, also known as influencer economy, is a platform-driven economy in which creators produce content, products, or services and distribute them directly to their audience through social media platforms and emerging technologies. This economic model is based on the ability of creators to build and maintain communities of users, monetizing their creative activity through multiple channels including advertising, sponsorships, product sales, crowdfunding, and subscription-based services. Creators include various professional categories such as social media influencers, YouTubers, bloggers, artists, online educators, podcasters, and independent professionals, who use platforms as infrastructure to reach their audience without necessarily relying on traditional intermediaries in the cultural and media industry. According to Goldman Sachs Research, the ongoing growth of the creator economy will likely benefit companies that possess a combination of factors, including a large global user base, access to substantial capital, robust AI-powered recommendation engines, versatile monetization tools, comprehensive data analytics, and integrated e-commerce options. Examples of creator economy software platforms include YouTube, TikTok, Instagram, Facebook, Twitch, Spotify, Substack, OnlyFans and Patreon. == History == The term "creator" was coined by YouTube in 2011 to be used instead of "YouTube star", an expression that at the time could only apply to famous individuals on the platform. The term has since become omnipresent and is used to describe anyone creating any form of online content. A number of platforms such as TikTok, Snapchat, YouTube, and Facebook have set up funds with which to pay creators. == Criticism == The large majority of content creators derive no monetary gain for their creations, with most of the benefits accruing to the platforms who can make significant revenues from their uploads. As few as 0.1% of creators are able to earn a living through their channels.
Open Sound Control
Open Sound Control (OSC) is a protocol for networking sound synthesizers, computers, and other multimedia devices for purposes such as musical performance or show control. OSC's advantages include interoperability, accuracy, flexibility and enhanced organization and documentation. Its disadvantages include higher bandwidth requirements, increased load on embedded processors, and lack of standardized messages/interoperability. The first specification was released in March 2002. == Motivation == OSC is a content format developed at CNMAT by Adrian Freed and Matt Wright comparable to XML, WDDX, or JSON. It was originally intended for sharing music performance data (gestures, parameters and note sequences) between musical instruments (especially electronic musical instruments such as synthesizers), computers, and other multimedia devices. OSC is sometimes used as an alternative to the 1983 MIDI standard, when higher resolution and a richer parameter space is desired. OSC messages are transported across the internet and within local subnets using UDP/IP and Ethernet. OSC messages between gestural controllers are usually transmitted over serial endpoints of USB wrapped in the SLIP protocol. == Features == OSC's main features, compared to MIDI, include: Open-ended, dynamic, URI-style symbolic naming scheme Symbolic and high-resolution numeric data Pattern matching language to specify multiple recipients of a single message High resolution time tags "Bundles" of messages whose effects must occur simultaneously == Applications == There are dozens of OSC applications, including real-time sound and media processing environments, web interactivity tools, software synthesizers, programming languages and hardware devices. OSC has achieved wide use in fields including musical expression, robotics, video performance interfaces, distributed music systems and inter-process communication. The TUIO community standard for tangible interfaces such as multitouch is built on top of OSC. Similarly the GDIF system for representing gestures integrates OSC. OSC is used extensively in experimental musical controllers, and has been built into several open source and commercial products. The Open Sound World (OSW) music programming language is designed around OSC messaging. OSC is the heart of the DSSI plugin API, an evolution of the LADSPA API, in order to make the eventual GUI interact with the core of the plugin via messaging the plugin host. LADSPA and DSSI are APIs dedicated to audio effects and synthesizers. In 2007, a standardized namespace within OSC called SYN, for communication between controllers, synthesizers and hosts, was proposed. == Design == OSC messages consist of an address pattern (such as /oscillator/4/frequency), a type tag string (such as ,fi for a float32 argument followed by an int32 argument), and the arguments themselves (which may include a time tag). Address patterns form a hierarchical name space, reminiscent of a Unix filesystem path, or a URL, and refer to "Methods" inside the server, which are invoked with the attached arguments. Type tag strings are a compact string representation of the argument types. Arguments are represented in binary form with four-byte alignment. The core types supported are 32-bit two's complement signed integers 32-bit IEEE floating point numbers Null-terminated arrays of eight-bit encoded data (C-style strings) arbitrary sized blob (e.g. audio data, or a video frame) An example message is included in the spec (with null padding bytes represented by ␀): /oscillator/4/frequency␀,f␀␀, Followed by the 4-byte float32 representation of 440.0: 0x43dc0000. Messages may be combined into bundles, which themselves may be combined into bundles, etc. Each bundle contains a timestamp, which determines whether the server should respond immediately or at some point in the future. Applications commonly employ extensions to this core set. More recently some of these extensions such as a compact Boolean type were integrated into the required core types of OSC 1.1. The advantages of OSC over MIDI are primarily internet connectivity; data type resolution; and the comparative ease of specifying a symbolic path, as opposed to specifying all connections as seven-bit numbers with seven-bit or fourteen-bit data types. This human-readability has the disadvantage of being inefficient to transmit and more difficult to parse by embedded firmware, however. The spec does not define any particular OSC Methods or OSC Containers. All messages are implementation-defined and vary from server to server.
Texture filtering
In computer graphics, texture filtering or texture smoothing is the method used to determine the texture color for a texture mapped pixel, using the colors of nearby texels (ie. pixels of the texture). Filtering describes how a texture is applied at many different shapes, size, angles and scales. Depending on the chosen filter algorithm, the result will show varying degrees of blurriness, detail, spatial aliasing, temporal aliasing and blocking. Depending on the circumstances, filtering can be performed in software (such as a software rendering package) or in hardware, eg. with either real time or GPU accelerated rendering circuits, or in a mixture of both. For most common interactive graphical applications, modern texture filtering is performed by dedicated hardware which optimizes memory access through memory cacheing and pre-fetch, and implements a selection of algorithms available to the user and developer. There are two main categories of texture filtering: magnification filtering and minification filtering. Depending on the situation, texture filtering is either a type of reconstruction filter where sparse data is interpolated to fill gaps (magnification), or a type of anti-aliasing (AA) where texture samples exist at a higher frequency than required for the sample frequency needed for texture fill (minification). There are many methods of texture filtering, which make different trade-offs between computational complexity, memory bandwidth and image quality. == The need for filtering == During the texture mapping process for any arbitrary 3D surface, a texture lookup takes place to find out where on the texture each pixel center falls. For texture-mapped polygonal surfaces composed of triangles typical of most surfaces in 3D games and movies, every pixel (or subordinate pixel sample) of that surface will be associated with some triangle(s) and a set of barycentric coordinates, which are used to provide a position within a texture. Such a position may not lie perfectly on the "pixel grid," necessitating some function to account for these cases. In other words, since the textured surface may be at an arbitrary distance and orientation relative to the viewer, one pixel does not usually correspond directly to one texel. Some form of filtering has to be applied to determine the best color for the pixel. Insufficient or incorrect filtering will show up in the image as artifacts (errors in the image), such as 'blockiness', jaggies, or shimmering. There can be different types of correspondence between a pixel and the texel/texels it represents on the screen. These depend on the position of the textured surface relative to the viewer, and different forms of filtering are needed in each case. Given a square texture mapped on to a square surface in the world, at some viewing distance the size of one screen pixel is exactly the same as one texel. Closer than that, the texels are larger than screen pixels, and need to be scaled up appropriately — a process known as texture magnification. Farther away, each texel is smaller than a pixel, and so one pixel covers multiple texels. In this case an appropriate color has to be picked based on the covered texels, via texture minification. Graphics APIs such as OpenGL allow the programmer to set different choices for minification and magnification filters. Note that even in the case where the pixels and texels are exactly the same size, one pixel will not necessarily match up exactly to one texel. It may be misaligned or rotated, and cover parts of up to four neighboring texels. Hence some form of filtering is still required. == Mipmapping == Mipmapping is a standard technique used to save some of the filtering work needed during texture minification. It is also highly beneficial for cache coherency - without it the memory access pattern during sampling from distant textures will exhibit extremely poor locality, adversely affecting performance even if no filtering is performed. During texture magnification, the number of texels that need to be looked up for any pixel is always four or fewer; during minification, however, as the textured polygon moves farther away potentially the entire texture might fall into a single pixel. This would necessitate reading all of its texels and combining their values to correctly determine the pixel color, a prohibitively expensive operation. Mipmapping avoids this by prefiltering the texture and storing it in smaller sizes down to a single pixel. As the textured surface moves farther away, the texture being applied switches to the prefiltered smaller size. Different sizes of the mipmap are referred to as 'levels', with Level 0 being the largest size (used closest to the viewer), and increasing levels used at increasing distances. == Filtering methods == This section lists the most common texture filtering methods, in increasing order of computational cost and image quality. === Nearest-neighbor interpolation === Nearest-neighbor interpolation is the simplest and crudest filtering method — it simply uses the color of the texel closest to the pixel center for the pixel color. While simple, this results in a large number of artifacts - texture 'blockiness' during magnification, and aliasing and shimmering during minification. This method is fast during magnification but during minification the stride through memory becomes arbitrarily large and it can often be less efficient than MIP-mapping due to the lack of spatially coherent texture access and cache-line reuse. === Nearest-neighbor with mipmapping === This method still uses nearest neighbor interpolation, but adds mipmapping — first the nearest mipmap level is chosen according to distance, then the nearest texel center is sampled to get the pixel color. This reduces the aliasing and shimmering significantly during minification but does not eliminate it entirely. In doing so it improves texture memory access and cache-line reuse through avoiding arbitrarily large access strides through texture memory during rasterization. This does not help with blockiness during magnification as each magnified texel will still appear as a large rectangle. === Linear mipmap filtering === Less commonly used, OpenGL and other APIs support nearest-neighbor sampling from individual mipmaps whilst linearly interpolating the two nearest mipmaps relevant to the sample. === Bilinear filtering === In Bilinear filtering, the four nearest texels to the pixel center are sampled (at the closest mipmap level), and their colors are combined by weighted average according to distance. This removes the 'blockiness' seen during magnification, as there is now a smooth gradient of color change from one texel to the next, instead of an abrupt jump as the pixel center crosses the texel boundary. Bilinear filtering for magnification filtering is common. When used for minification it is often used with mipmapping; though it can be used without, it would suffer the same aliasing and shimmering problems as nearest-neighbor filtering when minified too much. For modest minification ratios, however, it can be used as an inexpensive hardware accelerated weighted texture supersample. The Nintendo 64 used an unusual version of bilinear filtering where only three pixels are used known as 3-point texture filtering, instead of four due to hardware optimization concerns. This introduces a noticeable "triangulation bias" in some textures. === Trilinear filtering === Trilinear filtering is a remedy to a common artifact seen in mipmapped bilinearly filtered images: an abrupt and very noticeable change in quality at boundaries where the renderer switches from one mipmap level to the next. Trilinear filtering solves this by doing a texture lookup and bilinear filtering on the two closest mipmap levels (one higher and one lower quality), and then linearly interpolating the results. This results in a smooth degradation of texture quality as distance from the viewer increases, rather than a series of sudden drops. Of course, closer than Level 0 there is only one mipmap level available, and the algorithm reverts to bilinear filtering. === Anisotropic filtering === Anisotropic filtering is the highest quality filtering available in current consumer 3D graphics cards. Simpler, "isotropic" techniques use only square mipmaps which are then interpolated using bi– or trilinear filtering. (Isotropic means same in all directions, and hence is used to describe a system in which all the maps are squares rather than rectangles or other quadrilaterals.) When a surface is at a high angle relative to the camera, the fill area for a texture will not be approximately square. Consider the common case of a floor in a game: the fill area is far wider than it is tall. In this case, none of the square maps are a good fit. The result is blurriness and/or shimmering, depending on how the fit is chosen. Anisotropic filtering corrects this by sampling the texture as a non-square shape. The goal is
Deplatforming
Deplatforming, also known as no-platforming, is a boycott on an individual or group by removing the platforms used to share their information or ideas. The term is commonly associated with social media. == History == === Deplatforming of invited speakers === In the United States, the banning of speakers on university campuses dates back to the 1940s. This was carried out by the policies of the universities themselves. The University of California had a policy known as the Speaker Ban, codified in university regulations under President Robert Gordon Sproul, that mostly, but not exclusively, targeted communists. One rule stated that "the University assumed the right to prevent exploitation of its prestige by unqualified persons or by those who would use it as a platform for propaganda." This rule was used in 1951 to block Max Shachtman, a socialist, from speaking at the University of California at Berkeley. In 1947, former U.S. Vice President Henry A. Wallace was banned from speaking at UCLA because of his views on U.S. Cold War policy, and in 1961, Malcolm X was prohibited from speaking at Berkeley as a religious leader. Controversial speakers invited to appear on college campuses have faced deplatforming attempts to disinvite them or to otherwise prevent them from speaking. The British National Union of Students established its No Platform policy as early as 1973. In the mid-1980s, visits by South African ambassador Glenn Babb to Canadian college campuses faced opposition from students opposed to apartheid. In the United States, recent examples include the March 2017 disruption by protestors of a public speech at Middlebury College by political scientist Charles Murray. In February 2018, students at the University of Central Oklahoma rescinded a speaking invitation to creationist Ken Ham, after pressure from an LGBT student group. In March 2018, a "small group of protesters" at Lewis & Clark Law School attempted to stop a speech by visiting lecturer Christina Hoff Sommers. In the 2019 film No Safe Spaces, Adam Carolla and Dennis Prager documented their own disinvitation along with others. As of February 2020, the Foundation for Individual Rights in Education, a speech advocacy group, documented 469 disinvitation or disruption attempts at American campuses since 2000, including both "unsuccessful disinvitation attempts" and "successful disinvitations"; the group defines the latter category as including three subcategories: formal disinvitation by the sponsor of the speaking engagement; the speaker's withdrawal "in the face of disinvitation demands"; and "heckler's vetoes" (situations when "students or faculty persistently disrupt or entirely prevent the speakers' ability to speak"). === Deplatforming in social media === Beginning in 2015, Reddit banned several communities on the site ("subreddits") for violating the site's anti-harassment policy. A 2017 study published in the journal Proceedings of the ACM on Human-Computer Interaction, examining "the causal effects of the ban on both participating users and affected communities," found that "the ban served a number of useful purposes for Reddit" and that "Users participating in the banned subreddits either left the site or (for those who remained) dramatically reduced their hate speech usage. Communities that inherited the displaced activity of these users did not suffer from an increase in hate speech." In June 2020 and January 2021, Reddit also issued bans to pro-Trump communities over violations of the website's content and harassment policies. On May 2, 2019, Facebook and the Facebook-owned platform Instagram announced a ban of "dangerous individuals and organizations" including Nation of Islam leader Louis Farrakhan, Milo Yiannopoulos, Alex Jones and his organization InfoWars, Paul Joseph Watson, Laura Loomer, and Paul Nehlen. In the wake of the 2021 storming of the US Capitol, Twitter banned then-president Donald Trump, as well as 70,000 other accounts linked to the event and the far-right movement QAnon. Some studies have found that the deplatforming of extremists reduced their audience, although other research has found that some content creators became more toxic following deplatforming and migration to alt-tech platform. ==== Twitter ==== On November 18, 2022, Elon Musk, as newly appointed CEO of Twitter, reopened previously banned Twitter accounts of high-profile users, including Kathy Griffin, Jordan Peterson, and The Babylon Bee as part of the new Twitter policy. As Musk exclaimed, "New Twitter policy is freedom of speech, but not freedom of reach". ==== Alex Jones ==== On August 6, 2018, Facebook, Apple, YouTube and Spotify removed all content by Jones and InfoWars for policy violations. YouTube removed channels associated with InfoWars, including The Alex Jones Channel. On Facebook, four pages associated with InfoWars and Alex Jones were removed over repeated policy violations. Apple removed all podcasts associated with Jones from iTunes. On August 13, 2018, Vimeo removed all of Jones's videos because of "prohibitions on discriminatory and hateful content". Facebook cited instances of dehumanizing immigrants, Muslims and transgender people, as well as glorification of violence, as examples of hate speech. After InfoWars was banned from Facebook, Jones used another of his websites, NewsWars, to circumvent the ban. Jones's accounts were also removed from Pinterest, Mailchimp and LinkedIn. As of early August 2018, Jones retained active accounts on Instagram, Google+ and Twitter. In September, Jones was permanently banned from Twitter and Periscope after berating CNN reporter Oliver Darcy. On September 7, 2018, the InfoWars app was removed from the Apple App Store for "objectionable content". He was banned from using PayPal for business transactions, having violated the company's policies by expressing "hate or discriminatory intolerance against certain communities and religions." After Elon Musk's purchase of Twitter several previously banned accounts were reinstated including Donald Trump, Andrew Tate and Ye resulting in questioning if Alex Jones will be unbanned as well. However Musk denied that Alex Jones will be unbanned criticizing Jones as a person that "would use the deaths of children for gain, politics or fame". InfoWars remained available on Roku devices in January 2019, a year after the channel's removal from multiple streaming services. Roku indicated that they do not "curate or censor based on viewpoint," and that it had policies against content that is "unlawful, incited illegal activities, or violates third-party rights," but that InfoWars was not in violation of these policies. Following a social media backlash, Roku removed InfoWars and stated "After the InfoWars channel became available, we heard from concerned parties and have determined that the channel should be removed from our platform." In March 2019, YouTube terminated the Resistance News channel due to its reuploading of live streams from InfoWars. On May 1, 2019, Jones was barred from using both Facebook and Instagram. Jones briefly moved to Dlive, but was suspended in April 2019 for violating community guidelines. In March 2020, the InfoWars app was removed from the Google Play store due to claims of Jones disseminating COVID-19 misinformation. A Google spokesperson stated that "combating misinformation on the Play Store is a top priority for the team" and apps that violate Play policy by "distributing misleading or harmful information" are removed from the store. ==== Donald Trump ==== On January 6, 2021, in a joint session of the United States Congress, the counting of the votes of the Electoral College was interrupted by a breach of the United States Capitol chambers. The rioters were supporters of President Donald Trump who hoped to delay and overturn the President's loss in the 2020 election. The event resulted in five deaths and at least 400 people being charged with crimes. The certification of the electoral votes was only completed in the early morning hours of January 7, 2021. In the wake of several Tweets by President Trump on January 7, 2021 Facebook, Instagram, YouTube, Reddit, and Twitter all deplatformed Trump to some extent. Twitter deactivated his personal account, which the company said could possibly be used to promote further violence. Trump subsequently tweeted similar messages from the President's official US Government account @POTUS, which resulted in him being permanently banned on January 8. Twitter then announced that Trump's ban from their platform would be permanent. Trump planned to rejoin on social media through the use of a new platform by May or June 2021, according to Jason Miller on a Fox News broadcast. The same week Musk announced Twitter's new freedom of speech policy, he tweeted a poll to ask whether to bring back Trump into the platform. The poll ended with 51.8% in favor of unbanning Trump's account. Twitter has since reinstated Trump's Twitter accou