AI Headshot Enhancer

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Gooch shading

Gooch shading is a non-photorealistic rendering technique for shading objects. It is also known as "cool to warm" shading, and is widely used in technical illustration. == History == Gooch shading was developed by Amy Gooch et al. at the University of Utah School of Computing and first presented at the 1998 SIGGRAPH conference. It has since been implemented in shader libraries, software, and games released by Autodesk, Nvidia, and Valve. == Process == Gooch shading defines an additional two colors in conjunction with the original model color: a warm color (such as yellow) and a cool color (such as blue). The warm color indicates surfaces that are facing toward the light source while the cool color indicates surfaces facing away. This allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. The Gooch shader is typically implemented in two passes: all objects in the scene are first drawn with the "cool to warm" shading, and in the second pass the object's edges are rendered in black.
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Digital cinematography

Digital cinematography is the process of capturing (recording) a motion picture using digital image sensors rather than through film stock. As digital technology has improved in recent years, this practice has become dominant. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Many vendors have brought products to market, including traditional film camera vendors like Arri and Panavision, as well as new vendors like Red, Blackmagic, Silicon Imaging, Vision Research and companies which have traditionally focused on consumer and broadcast video equipment, like Sony, GoPro, and Panasonic. As of 2023, professional 4K digital cameras were approximately equal to 35mm film in their resolution and dynamic range capacity. Some filmmakers still prefer to use film picture formats to achieve the desired results. == History == The basis for digital cameras are metal–oxide–semiconductor (MOS) image sensors. The first practical semiconductor image sensor was the charge-coupled device (CCD), based on MOS capacitor technology. Following the commercialization of CCD sensors during the late 1970s to early 1980s, the entertainment industry slowly began transitioning to digital imaging and digital video over the next two decades. The CCD was followed by the CMOS active-pixel sensor (CMOS sensor), developed in the 1990s. Beginning in the late 1980s, Sony began marketing the concept of "electronic cinematography," utilizing its analog Sony HDVS professional video cameras. The effort met with very little success. However, this led to one of the earliest high definition video shot feature movies, Julia and Julia (1987). Rainbow (1996) was the world's first film to utilize extensive digital post production techniques. Shot entirely with Sony's first Solid State Electronic Cinematography cameras and featuring over 35 minutes of digital image processing and visual effects, all post production, sound effects, editing and scoring were completed digitally. The Digital High Definition image was transferred to a 35mm negative via an electron beam recorder for theatrical release. The first digitally videoed and post produced feature was Windhorse, shot in Tibet and Nepal in 1996 on the Sony DVW-700WS Digital Betacam and the prosumer Sony DCR-VX1000. The offline editing (Avid) and the online post and color work (Roland House / da Vinci) were also all digital. The film, transferred to 35mm negative for theatrical release, won Best U.S. Feature at the Santa Barbara Film Festival in 1998. In 1997, with the introduction of HDCAM recorders and 1920 × 1080 pixel digital professional video cameras based on CCD technology, the idea, now re-branded as "digital cinematography," began to gain traction in the market. Shot and released in 1998, The Last Broadcast is believed by some to be the first feature-length video shot and edited entirely on consumer-level digital equipment. In May 1999, George Lucas challenged the supremacy of the movie-making medium of film for the first time by including footage filmed with high-definition digital cameras in Star Wars: Episode I – The Phantom Menace. The digital footage blended seamlessly with the footage shot on film and he announced later that year he would film its sequels entirely on hi-def digital video. Also in 1999, digital projectors were installed in four theaters for the showing of The Phantom Menace. In May 2000, Vidocq, which was directed by Pitof, began principal photography shot entirely using a Sony HDW-F900 camera, with the video being released in September the next year. According to the Guinness World Records, Vidocq is the first full length feature filmed in digital high resolution. In June 2000, Star Wars: Episode II – Attack of the Clones began principal photography shot entirely using a Sony HDW-F900 camera as Lucas had previously stated. The film was released in May 2002. In May 2001 Once Upon a Time in Mexico was also shot in 24 frame-per-second high-definition digital video, partially developed by George Lucas using a Sony HDW-F900 camera, following Robert Rodriguez's introduction to the camera at Lucas' Skywalker Ranch facility whilst editing the sound for Spy Kids. A lesser-known movie, Russian Ark (2002), was also shot with the same camera and was the first tapeless digital movie, recorded on HDD instead of tape. In 2009, Slumdog Millionaire became the first movie shot mainly in digital to be awarded the Academy Award for Best Cinematography. The highest-grossing movie in the history of cinema, Avatar (2009), not only was shot on digital cameras as well, but also made the main revenues at the box office no longer by film, but digital projection. Major movies shot on digital video overtook those shot on film in 2013. Since 2016 over 90% of major films were shot on digital video. As of 2017, 92% of films are shot on digital. Only 24 major films released in 2018 were shot on 35mm. Since the 2000s, most movies across the world have been captured as well as distributed digitally. Today, cameras from companies like Sony, Panasonic, JVC and Canon offer a variety of choices for shooting high-definition video. At the high-end of the market, there has been an emergence of cameras aimed specifically at the digital cinema market. These cameras from Sony, Vision Research, Arri, Blackmagic Design, Panavision, Grass Valley and Red offer resolution and dynamic range that exceeds that of traditional video cameras, which are designed for the limited needs of broadcast television. == Technology == Digital cinematography captures motion pictures digitally in a process analogous to digital photography. While there is a clear technical distinction that separates the images captured in digital cinematography from video, the term "digital cinematography" is usually applied only in cases where digital acquisition is substituted for film acquisition, such as when shooting a feature film. The term is seldom applied when digital acquisition is substituted for video acquisition, as with live broadcast television programs. === Recording === ==== Cameras ==== Professional cameras include the Sony CineAlta (F) Series, Blackmagic Cinema Camera, Red One, Arri D-20, D-21 and Alexa, Panavision Genesis, Silicon Imaging SI-2K, Thomson Viper, Vision Research Phantom, IMAX 3D camera based on two Vision Research Phantom cores, Weisscam HS-1 and HS-2, GS Vitec noX, and the Fusion Camera System. Independent micro-budget filmmakers have also pressed low-cost consumer and prosumer cameras into service for digital filmmaking. Flagship smartphones like the Apple iPhone have been used to shoot movies like Unsane (shot on the iPhone 7 Plus) and Tangerine (shot on three iPhone 5S phones) and in January 2018, Unsane's director and Oscar winner Steven Soderbergh expressed an interest in filming other productions solely with iPhones going forward. ==== Sensors ==== Digital cinematography cameras capture digital images using image sensors, either charge-coupled device (CCD) sensors or CMOS active-pixel sensors, usually in one of two arrangements. Single chip cameras designed specifically for the digital cinematography market often use a single sensor (much like digital photo cameras), with dimensions similar in size to a 16 or 35 mm film frame or even (as with the Vision 65) a 65 mm film frame. An image can be projected onto a single large sensor exactly the same way it can be projected onto a film frame, so cameras with this design can be made with PL, PV and similar mounts, in order to use the wide range of existing high-end cinematography lenses available. Their large sensors also let these cameras achieve the same shallow depth of field as 35 or 65 mm motion picture film cameras, which many cinematographers consider an essential visual tool. Codecs Professional raw video recording codecs include Blackmagic Raw, Red Raw, Arri Raw and Canon Raw. ==== Video formats ==== Unlike other video formats, which are specified in terms of vertical resolution (for example, 1080p, which is 1920×1080 pixels), digital cinema formats are usually specified in terms of horizontal resolution. As a shorthand, these resolutions are often given in "nK" notation, where n is the multiplier of 1024 such that the horizontal resolution of a corresponding full-aperture, digitized film frame is exactly 1024 n {\displaystyle 1024n} pixels. Here the "K" has a customary meaning corresponding to the binary prefix "kibi" (ki). For instance, a 2K image is 2048 pixels wide, and a 4K image is 4096 pixels wide. Vertical resolutions vary with aspect ratios though; so a 2K image with an HDTV (16:9) aspect ratio is 2048×1152 pixels, while a 2K image with a SDTV or Academy ratio (4:3) is 2048×1536 pixels, and one with a Panavision ratio (2.39:1) would be 2048×856 pixels, and so on. Due to the "nK" notation not corresponding to specific horizontal resolutions per format a 2K image lacking, for example, the typical 35mm film soundtrack space, is only 182
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OpenWebRTC

OpenWebRTC (OWR) is a free software stack that implements the WebRTC standard, a set of protocols and application programming interfaces defined by the World Wide Web Consortium (W3C) and the Internet Engineering Task Force (IETF). It is an alternative to the reference implementation that is based on software from Global IP Solutions (GIPS). It is published under the terms of the Simplified (2-clause) BSD license and officially supports iOS, Linux, OS X, and Android operating systems. It is meant to also work outside web browsers, e.g. to power native mobile apps. It is mostly written in C and based largely on the multimedia framework GStreamer and a number of other, smaller external libraries. It officially supports both VP8 and H.264 as video formats. For H.264 it uses OpenH264 to which Cisco pays the patent licensing bills. Development of OpenWebRTC started at Ericsson Research under the lead of Stefan Ålund. They released it as free software in September 2014, together with the proof-of-concept web browser "Bowser" that is based on the stack. Among other things, this initial version didn't support data channels yet and was said to still be less mature than Google's reference implementation.
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Affordable affluence

Affordable affluence refers to a cultural phenomenon where consumers use accessible luxury goods and lifestyles to project status and align themselves with a higher social class, without requiring substantial wealth. This concept is embodied by brands such as Aritzia and Erewhon, which position themselves as offering high-end, trendy, or health-conscious products that are relatively accessible to the average consumer. A related concept is quiet luxury, where the ultra-wealthy signal wealth through subtle means. Quiet luxury emphasizes the widening gap between the ultra-wealthy and the general public, whereas accessible affluence provides a way for the general public to indulge in the lifestyle of the ultra-wealthy. == Origin of the term == An early use of the phrase in this context in a 2023 article in The Cut called "Meet the People Working 3 Jobs to Afford Erewhon." One of the interviewees used Erewhon as an archetype of affordable affluence. It was described as “a way for regular people to position themselves adjacent to the upper class.” == Background and description == The phenomenon arises due to an individual's desire to showcase status. For years, companies have strategized how to target the average consumers by providing a product that signals an elevated social status. For instance, Aritzia partnered with celebrities and micro-influencers to make it an aspirational brand at an affordable cost. Erewhon similarly has allowed middle class consumers to subtly signal a higher degree of perceived wealth by purchasing higher priced, but still attainable items. It has allowed middle-class individuals to feel as though they are part of an exclusive culture. This phenomenon has been seen particularly with Gen Z and Millennials in the setting of financial hardships in the 2020s. Affordable affluence is an example of the lipstick effect. Because traditional status symbols such as expensive cars became relatively more unattainable, posting clips on social media that showcase affordable affluence become an alternative status symbol. Particularly with food, the perception has evolved from a necessity to a luxury. A McKinsey & Company report demonstrated that these generations place a higher importance on groceries than restaurants, travel, and beauty/fashion.
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List of publications in data science

This is a list of publications in data science, generally organized by order of use in a data analysis workflow. See the list of publications in statistics for more research-based and fundamental publications; while this list is more applied, business oriented, and cross-disciplinary. General article inclusion criteria are: Papers from notable practitioners or notable professors, either with a Wikipedia page or reference to their notability Common knowledge all data professionals should know, with references validating this claim Highly cited applied statistics and machine learning publications Discussion-facilitating papers on the field of data science as a whole (for example, the Attention Is All You Need paper is arguably a landmark paper that can be added here, but it is specific to generative artificial intelligence, not for all practitioners of data) Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic Breakthrough – A publication that changed scientific knowledge significantly Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of data science. When possible, a reference is used to validate the inclusion of the publication in this list. == History == Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) Author: Leo Breiman Publication data: Online version: https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.pdf Description: Describes two cultures of statistics, one using a parsimonious and generative stochastic model, while the other is an algorithmic model with no known mechanism for how the data is generated. Breiman argues that while statistics has traditionally favored using the stochastic model, there is value in expanding the methods that statisticians can use to study phenomenon. Importance: Influence on the philosophies of statisticians right before the increased use of machine learning and deep learning methods. In a 20-year retrospective on this article, "Breiman's words are perhaps more relevant than ever". Notable statisticians at the time wrote opinion pieces about the publication. Although overall critical of the publication, David Cox writes that the publication "contains enough truth and exposes enough weaknesses to be thought-provoking." Bradley Efron commented that this publication is a "stimulating paper". Emanuel Parzen also comments about this publication that "Breiman alerts us to systematic blunders (leading to wrong conclusions) that have been committed applying current statistical practice of data modeling". Data Scientist: The Sexiest Job of the 21st Century Author: Thomas H. Davenport and DJ Patil Publication data: Online version: hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century Description: Describes the new role at companies that is coined "Data scientist", what they do, how an organization might recruit one to their organization, and how to work with one effectively. Importance: This publication has been an influence on the data community as mentioned near the time it was published in 2012 by institutions like IEEE Spectrum, but also mentioned nearly a decade later asking the same question the title poses. In a retrospective response to their own publication 10 years earlier, authors Davenport and Patil have reflected that the role of a data scientist has "become better institutionalized, the scope of the job has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown". 50 Years of Data Science Author: David Donoho Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734 Description: Retrospective discussion paper on the history and origins of data science, with a number of commentary from notable statisticians. Importance: This has been described as "the first in the field to present such a comprehensive and in-depth survey and overview", and helps to define the field that has many definitions. The Composable Data Management System Manifesto Author: Pedro Pedreira, Orri Erling, Konstantinos Karanasos, Scott Schneider, Wes McKinney, Satya R Valluri, Mohamed Zait, Jacques Nadeau Publication data: Online version: https://www.vldb.org/pvldb/vol16/p2679-pedreira.pdf Description: The vision paper advocating for a paradigm shift in how data management systems are designed using standard, composable, interoperable tools rather than siloed software tools. Importance: A paradigm shifting view on how future data science software tools should be designed for more efficient workflows, the principles of which "will be especially crucial for addressing fragmentation, improving interoperability, and promoting user-centricity as data ecosystems grow increasingly complex". == Data collection and organization == Tidy Data Author: Hadley Wickham Publication data: Online version: https://www.jstatsoft.org/article/view/v059i10/ https://vita.had.co.nz/papers/tidy-data.pdf Description: Describes a framework for data cleaning that is summarized in the quote, "each variable is a column, each observation is a row, and each type of observational unit is a table". This allows a standard data structure for which data analysis tools can be consistently built around. Importance: Cited over 1,500 times, this effort for tidy data has been described by David Donoho as having "more impact on today's practice of data analysis than many highly regarded theoretical statistics articles". In the context of data visualization, this publication is said to support "efficient exploration and prototyping because variables can be assigned different roles in the plot without modifying anything about the original dataset". Data Organization in Spreadsheets Author: Karl W. Broman and Kara H. Woo Publication data: Online version: https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989 Description: This article offers practical recommendations for organizing data in spreadsheets, like Microsoft Excel and Google Sheets, to reduce errors and lower the barrier for later analyses due to limitations in spreadsheets or quirks in the software. Importance: Influences teaching both data and non-data practitioners to create more analysis-friendly spreadsheets, and has been described to outline "spreadsheet best practices". == Data visualizations == Quantitative Graphics in Statistics: A Brief History Author: James R. Beniger and Dorothy L. Robyn Publication data: Online version: https://www.jstor.org/stable/2683467 Description: Outlines history and evolution of quantitative graphics in statistics, going through spatial organization (17th and 18th centuries), discrete comparison (18th and 19th centuries), continuous distribution (19th century), and multivariate distribution and correlation (late 19th and 20th centuries). Importance: Helps put into perspective for learning data practitioners the recency of graphics that are used. A later publication "Graphical Methods in Statistics" by Stephen Fienberg in 1979 writes that his publication "owes much to the work of Beniger and Robyn". == Practice == Data Science for Business Author: Foster Provost and Tom Fawcett Publication data: Online version: N/A Description: Broadly outlines principles of data science and data-analytic thinking for businesses. Importance: Cited over 3,000 times, it is "highly recommended for students" but also it is also recommended due to its "relevance to senior management leaders who want to build and lead a team of data scientists and implement data science in solving complex business problems". == Tooling == Hidden Technical Debt in Machine Learning Systems Author: D. Sculley, Gary Holy, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison Publication data: Online version: https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Description: This paper argues that it is "dangerous to think of [complex machine learning] quick wins as coming for free" and overviews risk factors to account for when implementing a machine learning system. Importance: All authors worked for Google, article is cited over 2,000 times, and helped practitioners thinking about quickly implementing a machine learning tool without understanding the long-term maintenance of the tool. A few useful things to know about machine learning Author: Pedro Domingos Publication data: Online version: https://dl.acm.org/doi/10.1145/2347736.2347755 https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Description: The purpose of this paper is to distill inaccessible "folk knowledge" to effectively implement machine learning projects because "machin
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Death and the Internet

A recent extension to the cultural relationship with death is the increasing number of people who die having created a large amount of digital content, such as social media profiles, that will remain after death. This may result in concern and confusion, because of automated features of dormant accounts (e.g. birthday reminders), uncertainty of the deceased's preferences that profiles be deleted or left as a memorial, and whether information that may violate the deceased's privacy (such as email or browser history) should be made accessible to family. Issues with how this information is sensitively dealt with are further complicated as it may belong to the service provider (not the deceased) and many do not have clear policies on what happens to the accounts of deceased users. While some sites, including Facebook and X (formerly Twitter), have policies related to death, others remain dormant until if applicable, deleted due to inactivity or transferred to family or friends. The FADA (Fiduciary Access to Digital Assets Act) was set in place to make it possible to transfer digital possessions legally. More broadly, the heavy increase in social media use is affecting cultural practices surrounding death. "Virtual funerals" and other forms of previously physical memorabilia are being introduced into the digital world, complete with public details of a person's life and death. == E-mail == Gmail and Hotmail allow the email accounts of the deceased to be accessed provided certain requirements are met. Yahoo! Mail will not provide access, citing the No Right of Survivorship and Non-Transferability clause in the Yahoo! terms of service. In 2005, Yahoo! was ordered by the Probate Court of Oakland County, Michigan, to release emails of deceased US Marine Justin Ellsworth to his father, John Ellsworth. == By website == === Facebook === ==== Policies ==== In its early days, Facebook used to delete profiles of dead people, but does not anymore. In October 2009, the company introduced "memorial pages" in response to multiple user requests related to the 2007 Virginia Tech shooting. After receiving a proof of death via a special form, the profile would be converted into a tribute page with minimal personal details, where friends and family members could share their grief. In February 2015, Facebook allowed users to appoint a friend or family member as a "legacy contact" with the rights to manage their page after death. It also gave Facebook users an option to have their account permanently deleted when they die. As of January 2019, all 3 options were active. ==== Controversies ==== In 2013, BuzzFeed criticized Facebook for the lack of control over memorialization that resulted in a "Facebook death" prank aimed at locking users out of their own accounts. In 2017, Reuters reported that a German court rejected a mother's demand to access her deceased daughter's memorialized account stating that the right to private telecommunications outweighed the right to inheritance. In July 2018, Dubai's DIFC Courts ruling clarified that Facebook, Twitter and other social media accounts should be bequeathed in legally binding will. Social media networks have also been criticized for not responding to relatives' requests to alter information on memorialized accounts. Another criticism is that Facebook users often are unaware that their content is ultimately owned not by them, but by Facebook. === Dropbox === ==== Policies ==== Dropbox determines inactive accounts by looking at sign-ins, file shares, and file activity over the previous 12 months. Once an account is determined inactive, Dropbox deletes the files on the account. To request access to the account of a deceased person, heirs are required to send appropriate documents by physical mail. === Google === ==== Policies ==== In April 2013, Google announced the creation of the 'Inactive Account Manager', which allows users of Google services to set up a process in which ownership and control of inactive accounts is transferred to a delegated user. Google also allows users to submit a range of requests regarding accounts belonging to deceased users. Google works with immediate family members and representatives to close online accounts in some cases once a user is known to be deceased, and in certain circumstances may also provide content from a deceased user's account. === X (formerly Twitter) === ==== Policies ==== Until 2010, Twitter (launched in July 2006) did not have a policy on handling deceased user accounts, and simply deleted timelines of deceased users. In August 2010, Twitter allowed memorialization of accounts upon request from family members, and also provided them with an option of either deleting the account or obtaining a permanent backup of the deceased user's public tweets. In 2014, Twitter updated its policy to include an option to delete deceased user photographs. This policy was implemented after multiple Twitter trolls sent Zelda Williams, daughter of Robin Williams, photoshopped images of her father. As of January 2019, the only option that Twitter offered for the accounts of dead people was account deactivation. Previously published content is not removed. To deactivate an account Twitter requires an immediate family member to present a copy of their ID and a death certificate of the deceased. Twitter specified that it does not provide account access to anyone, but does allow people having account login information to continue posting. A prominent example is Roger Ebert's account maintained by his wife Chaz. ==== Controversies ==== In 2012, The Next Web columnist Martin Bryant noticed that since Twitter, unlike Facebook, did not have a "one account per real person" emphasis, memorializing accounts presented a difficulty to the service. He also criticized the service for the lack of control over hacking of such accounts and disapproved the practice of passing dead people's usernames to new owners after a certain period of inactivity. In 2013, Variety ran a feature about Cory Monteith's Twitter account that had 1.5 million followers at the moment on his death and gained almost 1 million new followers afterwards. Monteith's fans also launched #DontDeleteCorysTwitter campaign. As of February 2019, the celebrity's account had 1.63 million followers. Various media reported awkward incidents related to automatic posting and account hacking. === iTunes === ==== Policies ==== iCloud and iTunes accounts are "non transferable" since the content is not owned — users only have a licence to access it. === Wikipedia === Users who have made at least several hundred edits or are otherwise known for substantial contributions to Wikipedia can be noted at a central memorial page. Wikipedia user pages are ordinarily fully edit-protected after the user has died, to prevent vandalism. === YouTube === YouTube grants access to accounts of deceased persons under certain conditions. It is one of the data options that one can select to give access to a trusted contact with Google's Inactive Account Manager. === Instagram === ==== Policies ==== As of the COVID-19 pandemic, Instagram has notified its users of a delay in time of reviewing reports of deceased users due to the limited staff the pandemic has caused. Users that submit a report on a deceased user on Instagram can either memorialize the account or remove it from Instagram's platform. Through memorializing the account, Instagram secures and protects a platform of a deceased user, but per their policy, they do not supply any of the login credentials to the account. For both memorializing or removing a deceased users account, a verified user needs to submit a tangible document that shows proof of death of the user. However, to fully remove an account, the user must be a close or direct family member to the deceased person, and show proof of credibility as well. === Microsoft === ==== Policies ==== Per Microsoft's policies, they do not supply any of the login credentials to a deceased user's Microsoft account. A user does not have to contact or notify Microsoft of the deceased user, as the related user is able to close the account themselves. At default, Microsoft removes accounts after 2 years of inactivity. If the user does not have access to the deceased user's account, Microsoft recommends that the user deletes all bank accounts linked to that of the deceased to ensure no subscriptions are still going through. If the user wants to request to gain access to the deceased user's account, a court order or a subpoena has to be provided to Microsoft, but does not guarantee access to the deceased user's account. For users that live in Germany, more documentation is needed to gain access of a deceased user's account, including the deceased user's death certificate, a form of ID, and a documentation of consent from the deceased. The requesting user needs to provide a form of ID as well. == Digital inheritance == Digital inheritance is the process of handing over
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Cringe culture

Cringe culture () is an Internet phenomenon and neologism characterized by the mockery and ridicule of content, behaviors, or expressions deemed embarrassing or awkward. The term cringe evolved semantically from describing personal secondhand embarrassment to becoming a dismissive label applied to various forms of online expression and fan behavior. The phenomenon emerged in the early 2000s as a response to awkward online content but gradually transformed into a cultural force that impacted fan communities, creative expression, and social media behavior. Cringe culture gained particular prominence through online platforms like Reddit and 4chan, and has been observed to cause the decline of various fandoms when they become labeled as cringe. Cringe culture has extended beyond Internet communities into academic and professional settings. Educators have noticed increased self-consciousness among students about displaying effort in their work (known as tryharding). By the early 2020s, a cultural pushback against cringe culture began to emerge, with public figures and celebrities advocating for authentic self-expression and rejecting the fear of being perceived as "trying too hard". == Origin == The term cringe underwent semantic change from its original usage describing an involuntary physical response, then to embarrassment. The term gained popularity in online forums during the early 2000s, when public self-humiliation online was a relatively novel phenomenon. Early cringe culture drew much of its content from YouTube. According to Kaitlyn Tiffany of The Atlantic, the majority of cringe stemmed from people who did not seem to understand that anyone in the world could see their videos. The phenomenon initially focused on empathy and secondhand embarrassment, with viewers relating to the awkward situations they witnessed. Popular early examples of cringe include the 2002 viral video Star Wars Kid and "My Video for Briona for Our 7 Month", in which a man winks, licks his lips, and makes romantic declarations to his partner. Early cringe culture encompassed multiple styles, including self-deprecating, playful, and hostile forms. On /b/ (4chan's "random" board), early cringe discussions targeted groups like Tumblr users, social justice warriors, fangirls, and furries, while also being used to describe "normies" who lacked sufficient knowledge of Internet culture to understand its ironic humor. In July 2012, Reddit user Michael Dombkowski took over the dormant r/cringe subreddit after watching a KENS5 segment about teen werewolves. Dombkowski created RSS feeds to alert him whenever someone mentioned cringe anywhere on Reddit, then encouraged users to visit his subreddit. The subreddit collected 10,000 monthly pageviews in its first month, which grew to 941,000 by September 2012 and 5 million the following month. According to The Daily Dot, Dombkowski had intended the subreddit to elicit empathy from viewers rather than to mock its subjects. On November 9, 2012, Dombkowski banned all images from r/cringe and created r/cringepics as a spinoff subreddit for image-based content. The community initially opposed this decision, as users worried that it would fragment the community. In a few months, r/cringepics overtook r/cringe in traffic and subscribers. By 2014, the combined subreddits amassed over 500,000 subscribers and more than 30 million monthly pageviews. In a March 2013 company AMA ("Ask Me Anything"), Reddit's general manager Erik Martin stated that he hates "r/cringepics and anything cringe related and the whole idea." == Impact == Cringe culture has impacted various fandoms. Screen Rant dubbed the phenomenon in which a fandom abruptly dissipates when suddenly deemed cringe (due to the actions of individuals within the fandom or the fandom being re-evaluated as a whole) as the "My Hero Academia Effect". My Hero Academia initially enjoyed popularity in 2020 during the COVID-19 pandemic, but the resurfacing of embarrassing TikTok videos of convention-goers in 2020 caused the My Hero Academia fandom to be deemed cringy, and thus was abandoned by many anime fans. Similarly, the fandom of the Homestuck webcomic, which ran from 2009 to 2016, faced scrutiny when cosplayers filled bathtubs with Sharpies to achieve gray skin coloring (emulating the design of the Homestuck characters), which led to property damage at hotels and convention bans. Many fans subsequently abandoned the fandom, and as a result, according to Screen Rant, the Homestuck fandom was almost non-existent by 2024. It is worth noting that as of September 27, 2025 animation studio SpindleHorse, also responsible for the popular animated show Hazbin Hotel (another common recipient of Cringe Culture discussion) has released a Homestuck animated pilot episode on YouTube. Other fandoms that were deemed cringy include the Stranger Things and Hazbin Hotel fandoms. Isobel Heal of Varsity described being "far too insecure as a teen to even consider listening to songs inspired by My Little Pony or Five Nights at Freddy's regardless of how catchy they were," but found that attending a Living Tombstone concert allowed her to overcome these inhibitions. She wrote that everyone in the crowd was "completely unafraid to engage in the silliness of the entire night," which allowed her to "let my guard down and enjoy the evening without fear of feeling 'cringe.'" Heal described her experience of singing along to tracks like "Discord", a My Little Pony–themed song, provided what she described as healing "the wounds of the younger me" and represented a form of reclaiming interests that had been suppressed due to social pressure and bullying. == Reactions == New York University professor Ocean Vuong observed that students increasingly hesitate to reveal effort behind their creative work. Vuong stated that students often say "I want to be a good writer, but it's a bit cringe" and perform cynicism because it can be misread as intelligence. In May 2022, Taylor Swift addressed cringe culture in her commencement speech at New York University: she advised graduates to "learn to live alongside cringe" and that "cringe is unavoidable over a lifetime." Other celebrities have made public speeches fighting against the perceived notion that "tryharding" is cringe. In his 31st Screen Actors Guild Awards acceptance speech, Timothée Chalamet emphasized his pursuit of greatness and the effort he invested in his roles, which diverged from typical humble acceptance speeches. In her 67th Annual Grammy Awards acceptance speech, rapper Doechii also stressed her dedication and hard work. According to The Daily Dot, X users called Chalamet and Doechii's speeches "refreshing" and decried those who embrace cringe culture as "miserable losers". In 2023, Critical Role dungeon master Matthew Mercer spoke against cringe culture at New York Comic Con: "We live in an odd time of 'cringe culture' where anything that's honest can be called cringe. And I don't agree with that." Mercer argued that much of what is dismissed as cringe consists of "people being their authentic self." In October 2025, actress and singer Ariana Grande discussed her experience with cringe culture in an interview on the podcast Shut Up Evan. She described the phenomenon as "unfair", stating that people should be allowed to express passion and happiness without judgement. She further explained that in the wake of her leading role in the 2024 film Wicked there were those who perceived the behavior of her and costar Cynthia Erivo during the film's press tour as "inauthentic" and therefore cringe. == Analysis == In 2021, Steven Dashiell wrote in the journal Studies in Popular Culture that cringe culture functions as a mechanism for social boundaries within the My Little Pony: Friendship Is Magic fandom, and that cringe culture operates not only between different communities but also within fandoms themselves. In his analysis, Dashiell examined a Reddit thread where a brony (an adult fan of My Little Pony: Friendship Is Magic) expressed embarrassment about other bronies. The thread received over 400 comments in which participants engaged in what Dashiell termed other-izing: distancing themselves from behaviors they deemed cringeworthy. Rather than defending the criticized bronies, commenters consistently used the term cringe to describe their reactions to certain fan behaviors while distinguishing themselves from the so-called "deviant brony" to normalize their own participation in the fandom. A February 2024 Hinge report revealed that more than half of Generation Z worries about cringe while dating and are 50 percent more likely than millennials to delay responding to avoid seeming overeager.
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Packard Bell Statesman

The Packard Bell Statesman was an economy line of notebook-sized laptops introduced in 1993 by Packard Bell. They were slower in performance and lacked features compared to most competitor products, but they were lower in price. It was created in a collaboration between Packard Bell and Zenith Data Systems. The Statesman series was essentially a rebrand of Zenith Data Systems Z-Star 433 series, with the only notable difference of the logo in the middle and text on the front bezel. == History == In June 1993 Zenith Data Systems announced an alliance with Packard Bell. Zenith acquired about 20% of Packard Bell and they would both now work together to design and build PC's. Zenith would also provide Packard Bell with private-label versions of their portable PC's. The Packard Bell Statesman was a rebrand of the Zenith Z-Star notebook computer series. While the Statesman was being advertised by Packard Bell, the Z-Star series was also still being sold by Zenith. The Statesman was first introduced on October 4, 1993. Prices started at $1,500 for a monochrome or color DSTN model with a 33 MHz Cyrix Cx486SLC, 4 MB of RAM, 200 MB hard disk drive, internal 1.44 MB floppy disk drive, and MS-DOS 6.0 with Windows 3.1 for the included software. A "J mouse" pointing device was included, similar to the TrackPoint. The Statesman was expected to begin shipping within the next few weeks. == Specifications == === Hardware === CPU The first two models, the 200M and 200C, used the Cyrix Cx486SLC. This was Cyrix's first processor, which was a 386SX pin-compatible chip with on-board L1 cache and 486 instructions, being known as a "hybrid chip". The processor was clocked at 33 MHz and had 1 KB of L1 cache. It was a 16-bit processor and was pin compatible with the Intel 80386SX. On the bottom of the unit, the motherboard had an empty socket for a Cyrix FasMath co-processor, which could improve floating-point math performance. The 200M and 200C plus models had a Cyrix Cx486SLC2 clocked at 50 MHz, which was 50% faster than the original 486SLC. The SLC2 similarly had 1 KB of on-board cache and was pin compatible with the previous model. Graphics & Display For video all models used three versions of the Chips & Technologies 655xx, the CT65520, 65525, and 65530. The 65520 was first introduced in early 1992 as the first controller with Super VGA resolution. It supported resolutions up to 1024x768 in 16 colors or shades of gray. If in 800x600 resolution, it can display up to 256 colors. All 3 chips were the same, with the CT65525 identifying as a CT65530. The CT65530 had an ability of 5V and 3.3V mixed operation and linear video memory addressing. All models used a 9.5in 800x600 resolution DSTN LCD. The 200M and 200M Plus had a monochrome display, while the 200C and 200C Plus had a color display. Audio All models had only basic audio available, with just a piezo speaker soldered onto the motherboard and no sound controller. Memory Standard RAM included was 4-8 MB of EDO RAM. The RAM was on a proprietary SIPP package that could only be upgraded to 12 MB maximum if the user had compatible modules. Storage For storage all models used a hard drive with a size of 100 or 200 MB, and all models had an internal 1.44 MB floppy disk drive located on the side of the unit. The maximum capacity hard drive compatible if the user wanted to upgrade was 500 MB.Ports & Expansion For ports all models had 1x serial, 1x parallel, 1x VGA output, and 1x PS/2 keyboard/mouse input. For expansion all models only had one PCMCIA type II slot. Keyboard & Mouse All models used a small-scale keyboard with control keys. One interesting feature of the keyboard is that the J key also acted as a mouse, working similar to IBM's ThinkPad TrackPoint. On some models additional keys such as S, D, F, G and space let you do other mouse actions such as right click, left click, double click, and middle mouse click. === Software === The series shipped with MS-DOS and Windows 3.1 as the included operating system. == Model Comparison == Statesman 200M — The first Statesman model, it came with a DSTN monochrome screen, and a Nickel-cadmium battery pack which could last up to 4 hours. It weighed 7.4 lb and was $1500. Statesman 200C — The second Statesman model, it was the same as the 200M with the only notable differences of a DSTN color display rather than monochrome and a slightly decreased battery life of about 3 hours. It cost $700 more than the 200M at $2200. Statesman 200M/200C Plus — The 200M/200C Plus were both identical to their previous base models, with the only difference of them having a Cx486SLC2 running at 50 MHz. In 1994 it cost around $2,295 for the 200C plus with 4 MB of ram, with 8 MB costing an extra $400. == Reception == The Statesman received fair reception, with most reviewers giving positivity for the low price and high battery life, but mainly criticizing the performance and screen quality of the model line. A review by PC World writer Rex Farrance and Owen Linderholm said the 200M had a good price, being only $1500, and a good battery life which lasted about 4 hours. In benchmarks however, the 200M performed "noticeably below the average". It was noted that the 200M's worst feature was its monochrome display, being "cloudy and a bit dim for our tastes". The J mouse was considered a decent choice, and was said to be "highly usable" after some practice. The 200M was listed as number 3 on PC World's top 20 budget PC list. PC World also reviewed the 200C, saying the color display is only a "marginal, although an improvement on the monochrome version". The 200C placed 9 on the PC World top 20 budget PC list. Compute! Magazine reviewed the 200C Plus in September 1994 stating it "lagged far behind the others, especially the DXs, but then speed isn't everything". It was given pros for low cost and good display, but criticized for its low performance, not having a trackball, and poor external monitor support.
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Niki.ai

Niki was an artificial intelligence company headquartered in Bangalore, Karnataka. It was founded in May 2015 by IIT Kharagpur graduates Sachin Jaiswal, Keshav Prawasi, Shishir Modi, and Nitin Babel. The Niki android app was launched for a limited beta in June 2015, then released for public during YourStory's TechSparks 2015, and is a Tech30 company. The company raised an undisclosed amount in seed funding from Unilazer Ventures, a Mumbai-based VC firm founded by Ronnie Screwvala, in October 2015. This was followed by another seed funding round by Ratan Tata in May 2016. The company then raised US$2 million in Series A round of funding from SAP.iO, existing investors and some US and German-based investors, among others. Niki.ai shut down in October 2021 as per media reports. Website not working. == Product == The product is an artificial intelligence-powered chatbot which works as an intelligent personal assistant, named Niki. Leveraging natural language processing and machine learning, Niki presents a chat-based natural language user interface to the users where they can interact with Niki in their natural language. Niki understands how users chat in India, deciphers the words, in the context of product/services that they would like to purchase, and comes up with apt recommendations. Initially, it was only available on the Android platform as a mobile app. The company has expanded its operations to the Facebook Messenger and Apple iOS platforms. The company aims to soon be present on more messaging platforms like Slack and WhatsApp. The company currently provides 20+ services to over 2 million consumers, covering a wide spectrum ranging from utility services like mobile recharge, bill payments, travel services like cabs, buses, hotels and entertainment services like movies and events. Services such as flights and healthcare are also planned. == Partnerships == In September 2017, Infosys Finacle joined with Niki.ai to provide chat-based service to banking customers. In August 2017, Niki partnered with LazyPay to enable a 'buy now, pay later' feature for its users.
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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.
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Quality of experience

Quality of experience (QoE) is a measure of the delight or annoyance of a customer's experiences with a service (e.g., web browsing, phone call, TV broadcast). QoE focuses on the entire service experience; it is a holistic concept, similar to the field of user experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements. == Definition and concepts == In 2013, within the context of the COST Action QUALINET, QoE has been defined as:The degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state.This definition has been adopted in 2016 by the International Telecommunication Union in Recommendation ITU-T P.10/G.100. Before, various definitions of QoE had existed in the domain, with the above-mentioned definition now finding wide acceptance in the community. QoE has historically emerged from Quality of Service (QoS), which attempts to objectively measure service parameters (such as packet loss rates or average throughput). QoS measurement is most of the time not related to a customer, but to the media or network itself. QoE however is a purely subjective measure from the user's perspective of the overall quality of the service provided, by capturing people's aesthetic and hedonic needs. QoE looks at a vendor's or purveyor's offering from the standpoint of the customer or end user, and asks, "What mix of goods, services, and support, do you think will provide you with the perception that the total product is providing you with the experience you desired and/or expected?" It then asks, "Is this what the vendor/purveyor has actually provided?" If not, "What changes need to be made to enhance your total experience?" In short, QoE provides an assessment of human expectations, feelings, perceptions, cognition and satisfaction with respect to a particular product, service or application. QoE is a blueprint of all human subjective and objective quality needs and experiences arising from the interaction of a person with technology and with business entities in a particular context. Although QoE is perceived as subjective, it is an important measure that counts for customers of a service. Being able to measure it in a controlled manner helps operators understand what may be wrong with their services and how to improve them. == QoE factors == QoE aims at taking into consideration every factor that contributes to a user's perceived quality of a system or service. This includes system, human and contextual factors. The following so-called "influence factors" have been identified and classified by Reiter et al.: Human Influence Factors Low-level processing (visual and auditory acuity, gender, age, mood, …) Higher-level processing (cognitive processes, socio-cultural and economic background, expectations, needs and goals, other personality traits…) System Influence Factors Content-related Media-related (encoding, resolution, sample rate, …) Network-related (bandwidth, delay, jitter, …) Device-related (screen resolution, display size, …) Context Influence Factors Physical context (location and space) Temporal context (time of day, frequency of use, …) Social context (inter-personal relations during experience) Economic context Task context (multitasking, interruptions, task type) Technical and information context (relationship between systems) Studies in the field of QoE have typically focused on system factors, primarily due to its origin in the QoS and network engineering domains. Through the use of dedicated test laboratories, the context is often sought to be kept constant. == QoE versus User Experience == QoE is strongly related to but different from the field of User Experience (UX), which also focuses on users' experiences with services. Historically, QoE has emerged from telecommunication research, while UX has its roots in Human–Computer Interaction. Both fields can be considered multi-disciplinary. In contrast to UX, the goal of improving QoE for users was more strongly motivated by economic needs. Wechsung and De Moor identify the following key differences between the fields: == QoE measurement == As a measure of the end-to-end performance at the service level from the user's perspective, QoE is an important metric for the design of systems and engineering processes. This is particularly relevant for video services because – due to their high traffic demands –, bad network performance may highly affect the user's experience. So, when designing systems, the expected output, i.e. the expected QoE, is often taken into account – also as a system output metric and optimization goal. To measure this level of QoE, human ratings can be used. The mean opinion score (MOS) is a widely used measure for assessing the quality of media signals. It is a limited form of QoE measurement, relating to a specific media type, in a controlled environment and without explicitly taking into account user expectations. The MOS as an indicator of experienced quality has been used for audio and speech communication, as well as for the assessment of quality of Internet video, television and other multimedia signals, and web browsing. Due to inherent limitations in measuring QoE in a single scalar value, the usefulness of the MOS is often debated. Subjective quality evaluation requires a lot of human resources, establishing it as a time-consuming process. Objective evaluation methods can provide quality results faster, but require dedicated computing resources. Since such instrumental video quality algorithms are often developed based on a limited set of subjective data, their QoE prediction accuracy may be low when compared to human ratings. QoE metrics are often measured at the end devices and can conceptually be seen as the remaining quality after the distortion introduced during the preparation of the content and the delivery through the network, until it reaches the decoder at the end device. There are several elements in the media preparation and delivery chain, and some of them may introduce distortion. This causes degradation of the content, and several elements in this chain can be considered as "QoE-relevant" for the offered services. The causes of degradation are applicable for any multimedia service, that is, not exclusive to video or speech. Typical degradations occur at the encoding system (compression degradation), transport network, access network (e.g., packet loss or packet delay), home network (e.g. WiFi performance) and end device (e.g. decoding performance). == QoE management == Several QoE-centric network management and bandwidth management solutions have been proposed, which aim to improve the QoE delivered to the end-users. When managing a network, QoE fairness may be taken into account in order to keep the users sufficiently satisfied (i.e., high QoE) in a fair manner. From a QoE perspective, network resources and multimedia services should be managed in order to guarantee specific QoE levels instead of classical QoS parameters, which are unable to reflect the actual delivered QoE. A pure QoE-centric management is challenged by the nature of the Internet itself, as the Internet protocols and architecture were not originally designed to support today's complex and high demanding multimedia services. As an example for an implementation of QoE management, network nodes can become QoE-aware by estimating the status of the multimedia service as perceived by the end-users. This information can then be used to improve the delivery of the multimedia service over the network and proactively improve the users' QoE. This can be achieved, for example, via traffic shaping. QoE management gives the service provider and network operator the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction. As it may involve limiting resources for some users or services in order to increase the overall network performance and QoE, the practice of QoE management requires that net neutrality regulations are considered.
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Media Auxiliary Memory

Media Auxiliary Memory or Medium Auxiliary Memory (MAM) refers to a chip embedded into a digital media device (usually a tape cartridge) that stores a small amount of data or metadata that a computer can read without having to read the actual tape. MAMs can be used by the tape driver to increase efficiency, or by custom software to store & retrieve custom data. Some examples of MAM's are Cartridge Memory (HP/Seagate/IBM LTO) and MIC (Sony AIT).
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AI data center

An AI data center is a specialized data center facility designed for the computationally intensive tasks of training and running inference for artificial intelligence (AI) and machine learning models. Unlike general-purpose data centers, they are optimized for the parallel processing demands of AI workloads, typically using hardware such as AI accelerators (e.g., GPUs, TPUs) and high-speed interconnects. The global push to construct these specialized facilities accelerated dramatically during the AI boom of the 2020s. Memory manufacturers prioritized production of High Bandwidth Memory (HBM) essential for AI servers, which led to a global memory supply shortage amid a broader competition for advanced chips, power, and infrastructure. Major tech companies are estimated to spend $650 billion on AI data centers in 2026. == Architecture == Data centers for building and running large machine learning models contain specialized computer chips, GPUs, that use 2 to 4 times as much energy as their regular CPU counterparts (250-500 watts). AI data centers use 60 or more kilowatts per server rack, whereas more standard data centers typically use 5 to 10 kilowatts per rack. == Operators == As of August 2025, The Information tracked 18 planned or existing AI data centers in the United States, operated by Amazon Web Services, CoreWeave, Crusoe, Meta, Microsoft/OpenAI, Oracle, Tesla, and xAI. Other AI data center operators include Digital Realty and Alibaba. Data centers are also being built in China, India, Europe, Saudi Arabia, and Canada. The New Yorker described CoreWeave as the most prominent AI data center operator in the United States. Two types of data center providers for machine learning have been noted: hyperscalers and neoclouds. The Verge listed large technology companies such as Google, Meta, Microsoft, Oracle and Amazon as hyperscalers. The New York Times described neoclouds as "a new generation of data center providers". CoreWeave, Nebius, Nscale, and Lambda have been described as examples of neoclouds. In January 2025, OpenAI, in partnership with Oracle and Softbank, announced the Stargate project, which as of September 2025 is composed of six built or proposed AI data centers in the United States. In response to the Stargate project, Amazon launched in October 2025 an AI data center on 1,200 acres of farmland in Indiana. This data center, known as Project Rainier, is one of the largest AI data centers in the world, with Amazon spending $11 billion on the project. Rainier is specifically intended for training and running machine learning models from Anthropic. As of that time, this facility contains seven data centers (out of an estimated 30 planned) and will use 2.2 gigawatts of electricity (equivalent to 1 million households) and millions of gallons of water per year. Computer chips from Annapurna Labs and Anthropic, Trainium 2, were designed for use in such facilities. Amazon pumped millions of gallons of water out of the ground to construct the data center, and as of June 2025, Indiana state officials are investigating whether this dewatering process led to dry wells for local residents. In November 2025, Anthropic announced a plan in partnership with Fluidstack to develop artificial intelligence infrastructure in the United States, including data centers in New York and Texas, worth $50 billion. Other AI data center projects include the Colossus supercomputer from xAI, a Louisiana-based project from Meta, Hyperion, expected to use 5 GW of power, and a second Ohio-based Meta project, Prometheus, with a capacity of 1 GW. A 3,200-acre AI data center, capable of 4.4-4.5 GW of power and located on the decommissioned Homer City Generating Station, is under construction as of 2025, and will use seven 30-acre gas generating stations supplied by EQT. As of December 2025, CRH is working on over 100 data centers in the United States. In 2025, ExxonMobil and NextEra announced plans to build a data center powered by natural gas and using carbon capture technology, with 1.2 GW of power capacity. They previously purchased 2,500 acres of land in the Southeastern United States and plan to market the data center to an artificial intelligence company. The increased interest in AI data centers has led to several executives from companies in that space becoming billionaires, including CoreWeave, QTS, Nebius, Astera Labs, Groq, Fermi (which is connected to former United States Secretary of Energy Rick Perry), Snowflake and Cipher Mining. Several companies involved in cryptocurrency mining, such as Bitdeer, CoreWeave, Cipher Mining, TeraWulf, IREN, Core Scientific, and CleanSpark have also been involved with AI data centers. == Finances == Between January and August 2024, Microsoft, Meta, Google and Amazon collectively spent $125 billion on AI data centers. Citigroup forecasted that $2.8 trillion would be spent on AI data centers by 2030, while McKinsey and Company estimated that almost $7 trillion would be spent globally by that time. According to S&P Global, $61 billion has been spent on the data center market as a whole in 2025, while debt issuance for data centers was $182 billion during the same year. Large technology companies have offloaded the financial risks of building AI data centers by setting up special purpose vehicles or by contracting with neoclouds. For example, Meta's Hyperion was mostly funded by Blue Owl Capital, which did so using a bond offering from PIMCO. Those bonds were sold to a number of clients, including BlackRock. Meta did not borrow money itself and instead established a special purpose vehicle from which it would rent the data center. This deal was structured by Morgan Stanley for $30 billion, the largest known private capital transaction as of 2025. Neoclouds such as CoreWeave have gone into debt to buy computer chips from Nvidia for their data centers, and the chips themselves have been used for loan collateral. As of December 2025, CoreWeave took out three GPU-backed loans, collectively worth $12.4 billion, from private credit firms (Blackstone, Coatue, BlackRock, PIMCO) and from banks (Goldman Sachs, JPMorgan Chase, Wells Fargo). Thus, these companies provide an indirect connection between private credit and established banks. Data centers have also established asset-backed securities, and debt for data centers has its own derivative financial products. The real estate industry, including asset managers, public companies and private investors, has also invested in data centers. == Energy sourcing == == Environmental footprint == Average AI data centers have an electricity footprint equivalent to 100,000 households, and use billions of gallons of water for cooling their hardware. In 2025, the International Energy Agency estimated that the larger AI data centers currently under construction could consume as much electricity as 2 million households. A 2024 report from the United States Department of Energy stated that data centers overall used 17 billion gallons of water per year in the United States, primarily due to "rapid proliferation of AI servers", and that this usage was forecasted to grow to nearly 80 billion gallons by 2028. Researchers estimated that AI data centers in the United States would emit 24-44 million metric tons of carbon dioxide and use 731–1,125 million cubic meters of water per year between 2024 and 2030. Peaking power plants, which have been proposed as a power source for AI data centers, emit sulfur dioxide and have historically been located disproportionately near communities of color in the United States. Reciprocating internal combustion engines, proposed as another power source for a data center, emit PM 2.5, nitrogen oxides, and volatile organic compounds. == AI data centers in the United States == In the United States, both the Biden administration and second Trump administration supported the construction of AI data centers. In January 2025, then-president Joe Biden signed an executive order for federal government agencies to support AI data centers on federal sites built by private companies, study their effect on energy prices, and encourage their use of renewable energy. In April 2025, the United States Department of Energy suggested 16 possible sites, including Los Alamos National Laboratory, Sandia National Laboratories and Oak Ridge National Laboratory. In its July 2025 AI Action Plan, the second Trump administration supported increased production of AI data centers. Several US states have incentivized local data center construction. For example, in 2024, lawmakers in Michigan approved tax breaks for data center equipment and construction material. Some data center companies have also invested or promised to invest in the infrastructure of local communities. In December 2025, Democratic senators Elizabeth Warren, Chris Van Hollen, and Richard Blumenthal wrote to seven technology companies (Google, Microsoft, Amazon, Meta, CoreWeave, Digital Realty, and Equinix) that they w
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Mosaik Solutions

Mosaik Solutions (formerly American Roamer) was a company that specializes in wireless coverage data and wireless coverage maps, based in Memphis, Tennessee before being acquired by Ookla. The company collects and crowdsources carrier signal quality from major telecommunications providers or users who have its consumer or enterprise mobile application installed. The data is used to provide insights into places around the world without access to cellular coverage and the development of new coverage patterns, as well as to provide maps showing what provider offers the best service in an area. In 2011, the Federal Communications Commission (FCC), recognized Mosaik Solutions as the "industry standard" for the presence of wireless service at the census-block level. == History == In 2016, Mosaik purchased Sensorly, a free app developed to crowdsource cellular network performance service and provide coverage mapping for wireless networks worldwide. == Products and services == === MapELEMENTS === MapELEMENTS software is a visualization tool that allows users to analyze data from the largest cellular coverage database in the world. === CellMaps === CellMaps is an interactive mapping solution that allows companies to show their network coverage directly on their website through an iframe or API. In 2013 Mosaik launched an android app for CellMaps that provides data directly from carriers so that users can determine what carrier meets their needs in a given area. On the map you can overlay multiple carriers, zoom to street-view level, and drop a pin onto any given spot to get a breakdown of carrier service in that area. === Signal Insights App === Signal Insights is an SaaS platform service available for android users that measures and analyzes the customer's experience in cellular or Wi-Fi networks. Indoor mode allows a user to upload a building floor plan and then map and test specific points in the building for cellular or Wi-Fi connectivity. === Sensorly App === Sensorly is a free app that crowdsources cellular network performance to provide coverage mapping worldwide and mobile speed data to help consumers make informed decisions when choosing a cellular carrier. In February 2017, Sensorly launched Map Trip, a feature that allows users to map their routes and share with others their signal data at a particular point in real time. === TowerSource === TowerSource is a resource for locating cell towers and identifying ownership, availability, fiber routes, type and height. It was acquired by Mosaik Solutions in September 2014. === Network Validator === Network Validator is a SaaS solution designed for users to quickly determine whether global cellular networks exist - by country, operator and wireless technology. === CoverageRight === CoverageRight is composed of licensed GIS file datasets that identify the marketed coverage of wireless operators in the United States and worldwide. It enables users to perform spatial analyses, monitor competitive build-outs, analyze coverage trends and assemble roaming footprints. This data has been utilized by the FCC to analyze wireless coverage nationwide. === Network QoE === Network QoE is an enterprise platform that uses crowdsourced data from cellular devices to detect wireless network issues including 3G, 4G and wifi accessibility, network coverage holes and data performance issues. === Wireless Spectrum Report === In March 2017, Mosaik Solutions launched the Wireless Spectrum Report, a tabular dataset detailing facts about spectrum ownership and availability in the United States.
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BitClout

BitClout was an open source blockchain-based social media platform. On the platform, users could post short-form writings and photos, award money to posts they particularly like by clicking a diamond icon, as well as buy and sell "creator coins" (personalized tokens whose value depends on people's reputations). BitClout ran on a custom proof of work blockchain, and was a prototype of what can be built on DeSo (short for "Decentralized Social"). BitClout's founder and primary leader is Nader al-Naji, known pseudonymously as "Diamondhands". Under development since 2019, BitClout's blockchain created its first block in January 2021, and BitClout itself launched publicly in March 2021. The platform launched with 15,000 "reserved" accounts — a move intended to prevent impersonation, but which backfired as some people with reserved accounts tried to actively distance themselves. Later, in September 2021, BitClout was revealed to be the flagship product of the DeSo blockchain. == History == === Origins (2019 - March 2021) === In early 2019, Nader al-Naji became interested in "mixing investing and social media". He started creating a custom blockchain in May 2019, but didn't tell anyone else until November 2020. However, in the fall of 2020, al-Naji pitched BitClout's own investors under his real name and began posting job listings for a "new operation". Although BitClout was not originally intended to launch until mid-2021, its development was sped up due to "zeitgeist about decentralized social media" in January 2021. BitClout's first block was mined on 18 January 2021. Its next block was mined on 1 March 2021. === As BitClout (March - September 2021) === In early March 2021, about fifty investors received links to a password-protected website with the BitClout white paper. They were encouraged to explore the site and send the same link to "two or three other 'trusted contacts'". Within weeks users were spending millions of dollars per day on the platform. The platform's founders said they were "completely unprepared", having planned to have a "soft-launch". The leader went by the name "diamondhands" on the platform. On 24 March 2021, BitClout launched out of private beta. Investors include Sequoia Capital, Andreessen Horowitz, the venture capital firm Social Capital, Coinbase Ventures, Winklevoss Capital Management, Alexis Ohanian, Polychain, Pantera, and Digital Currency Group (CoinDesk's parent company). During its initial launch, BitClout's currency could be bought with bitcoin, but not sold except on Discord servers or Twitter threads. A single bitcoin wallet related to BitClout received more than $165M worth of deposits. In March 2021, law firm Anderson Kill P.C. sent Nader al-Naji, the presumed leader of the BitClout platform, a cease-and-desist letter, demanding the removal of Brandon Curtis's account and alleging that BitClout violated sections 1798 and 3344 of the California Civil Code by using Curtis's name and likeness without his consent. Curtis also tweeted, "Adopting Bitcoin's aesthetic to raise VC funding to carry out unethical and blatantly illegal schemes like BitClout: not cool". (However, Curtis's coin, despite not being listed on the official website, can still be bought by users searching for the original username.) Additionally, in April 2021, Lee Hsien Loong asked for his name and photograph to be removed from the site, stating that he has "nothing to do with the platform" and that "it is misleading and done without [his] permission". On 18 May 2021, diamondhands announced that 100% of the BitClout code went public. On 12 June 2021, the supply of BitClout was capped at around 11 million coins. On 18 July 2021, BitClout added the ability for users to mint and purchase NFTs within the platform. === As part of DeSo (September 2021 - July 2024) === On 21 September 2021, it was revealed that BitClout was a prototype built on DeSo, short for "Decentralized Social". As a part of this revelation, diamondhands confirmed his identity as Nader al-Naji. (As early as April 2021, it had been believed that diamondhands indeed was that person.)The Bitclout project raised $200M in funding, which went to setting up the DeSo Foundation. === End and aftermath (July 2024 - present) === In July 2024, al-Naji was arrested by the FBI and charged with wire fraud involving BitClout. He also faced civil charges of securities fraud and unregistered offers and sales of securities from the Securities and Exchange Commission. In response, the official "deso" account posted that al-Naji was "safe and at home" and "that this experience has only reinforced [his] commitment to DeSo". In February 2025, the Justice Department dropped its case against al-Naji. In March 2026, the SEC voluntarily dismissed the enforcement case with prejudice. == Design == BitClout is a social media platform. Its users can post short-form writings and photos (similarly to Twitter). They can award money to posts they particularly like by clicking a diamond icon (similarly to Twitch Bits). The prices of each account's "creator coin" goes up and down with the popularity of the celebrity behind it. For example, if someone says something negative, the value of their corresponding account may go down. This price is computed automatically according to the formula p r i c e _ i n _ b i t c l o u t = .003 ∗ c r e a t o r _ c o i n s _ i n _ c i r c u l a t i o n 2 {\displaystyle price\_in\_bitclout=.003creator\_coins\_in\_circulation^{2}} . At launch time, BitClout scraped 15,000 profiles of celebrities from Twitter to create "reserved" accounts in their names. To claim a reserved account, the account holder would need to tweet about it (which also serves as a marketing strategy). At least 80 such reserved profiles have been claimed. Proof of stake was introduced in March 2024.
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