In computational linguistics, a trigram tagger is a statistical method for automatically identifying words as being nouns, verbs, adjectives, adverbs, etc. based on second order Markov models that consider triples of consecutive words. It is trained on a text corpus as a method to predict the next word, taking the product of the probabilities of unigram, bigram and trigram. In speech recognition, algorithms utilizing trigram-tagger score better than those algorithms utilizing IIMM tagger but less well than Net tagger. The description of the trigram tagger is provided by Brants (2000).
Depth peeling
In computer graphics, depth peeling is an exact multipass method of order-independent transparency that extracts transparent fragments into depth layers and composites those layers in depth order. Depth peeling has the advantage of being able to generate correct results even for complex images containing intersecting transparent objects. == Method == Depth peeling works by rendering the image multiple times. Depth peeling uses two Z buffers, one that works conventionally, and one that is not modified, and sets the minimum distance at which a fragment can be drawn without being discarded. For each pass, the previous pass' conventional Z-buffer is used as the minimal Z-buffer, so each pass removes already-captured nearer fragments and draws the next depth layer behind them. The resulting images can then be composited in depth order to form a single image. A major drawback of classical depth peeling is performance: it requires one geometry pass per peeled layer, so scenes with high depth complexity require many passes that each re-rasterize the transparent geometry. Later variants reduce the number of passes by peeling multiple layers or both front and back layers in a pass. Dual depth peeling reduces the geometry-pass count from N to N/2+1 by peeling one layer from the front and one from the back in each pass, while multi-layer depth peeling peels several layers per pass and reported up to an 8x speed-up in RGBA8 settings.
Pivot to video
"Pivot to video" is a phrase referring to the trend, starting in 2015, of media publishing companies cutting staff resources for written content (generally published on their own web sites) in favor of short-form video content (often published on third-party platforms such as Facebook, Instagram, Twitter, YouTube, Snapchat, and TikTok). These moves were generally presented by publishers as a response to changes in social media traffic or to changes in the media consumption habits of younger audiences. However, many media commentators have argued that this shift was primarily motivated by advertising revenue, and that only advertisers, not consumers, prefer video over text. The pivot's contribution to job loss in the media industry has given the phrase "pivot to video" an association with decline, especially in a business context. Commentators have also noted a lack of transparency and accuracy in the viewership metrics reported by platforms such as Facebook, pointing out that abrupt shifts in platforms' proprietary algorithms can have devastating effects on publishers' viewership, traffic, and revenue. Following a scandal in which Facebook revealed it had artificially inflated numbers to its advertisers about how long viewers watched ads, many journalists and industry analysts concluded that the shift to video was based on such misleading or inaccurate metrics, which created a false impression that there was customer demand for additional video content. == History == Streaming media technology has been available since the early 1990s, though it was relatively low-fidelity and not widely available until the mid-2000s. In 2007, traditional media publishers including the New York Times, Washington Post and Time Inc. created new divisions to develop web videos, and Facebook launched its video platform. Twitter purchased micro-video service Vine in October 2012, began adding native video streaming in late 2014, and acquired video-streaming service Periscope in January 2015. An August 2014 profile on BuzzFeed noted the publisher's large investment into video production, and observed that "the future of BuzzFeed may not even be on BuzzFeed.com. One of the company’s nascent ideas, BuzzFeed Distributed, will be a team of 20 people producing content that lives entirely on other popular platforms, like Tumblr, Instagram or Snapchat." On 7 January 2015, Facebook issued a statement about "the shift to video," reporting that "since June 2014, Facebook has averaged more than 1 billion video views every day." Media critic John Herrman argued that "What the shift to Facebook video means is that Facebook is more interested in hosting the things media companies make than just spreading them, that it views links to outside pages as a problem to be solved, and that it sees Facebook-hosted video as an example of the solution." In February 2015, the digital video-journalism publisher NowThis announced that it would operate without a home page, producing content to be published directly on social media platforms. In April 2016, Mashable fired much of its editorial staff, attempting to pivot away from hard news coverage while "growing Mashable across every platform" and doubling down on branded content and video. By December 2017, following a sale to Ziff Davis, Mashable retreated from this focus on video; Bernard Gershon, president of GershonMedia, said that the announcement of many such "pivots" were actually aimed primarily at investors. By 2017, "advertiser interest in video [was] insatiable... Any CFO is going to say 'How can we get more video?'" according to an executive of the publishers' trade association Digital Content Next. Publishers such as Vanity Fair, the Washington Post, and Sports Illustrated began adapting their own articles into cheap video content, either dictated by a newsreader or animated as a slideshow with captions, which could be shared on social platforms or even played alongside the articles themselves. June 2017 saw numerous high-profile pivots to video. Vocativ laid off at least 20 staff, including its entire newsroom, explaining that "as the industry evolves, we are undertaking a strategic shift to focus exclusively on video content that will be distributed via social media and other platforms." Fox Sports eliminated its entire writing staff to focus on creating "premium video across all platforms." And MTV News announced a restructuring that would cut its writing team. Less than two years earlier, MTV News had hired Grantland co-founder Dan Fierman to lead a significant investment in "longform" political and cultural reporting, but Fierman left in April 2017, and in June MTV announced it was "shifting resources into short-form video content more in line with young people's media consumption habits." In July, Vice Media laid off at least 60 employees, including the editor-in-chief of Vice Sports, while expanding video production. August 2017 saw Mic cut ten writers and directed the remainder of the newsroom to generate videos for social platforms. CEO Chris Altchek said "When you think about how many hours people spend watching video versus reading, the audience has already spoken." The move was ultimately unsuccessful, and Mic laid off the majority of its staff a year later before being sold to Bustle Media Group for a fraction of its former value. In September 2017, the for-profit wiki-hosting company Fandom began adding commercially produced videos to its otherwise user-generated wiki subdomains, explicitly citing the need to "keep up with user and advertiser expectations" by "diversifying our content," claiming without substantiation that "consumer patterns are changing," necessitating the addition of "complementary video" to accommodate that supposed need. Objection to the content in these videos and its sharp contrast against the content of the wiki sites to which they were applied led to vocal user backlash, leading Fandom CCO Dorth Raphaely to offer the following non-committal response: "I agree that with these videos in particular we did not deliver the right type of content experience." Movie Pilot CEO Tobi Bauckhage explained his company's fall 2017 layoffs as part of moving "from a text-based publishing model to video... a reaction to the fact that Facebook has changed their algorithms in favor of video instead of referral traffic over the last 12 months and we were losing money in the publishing bit of our business." As part of the company's change in direction, the majority of its staff was laid off and its parent company was sold to Webedia. In November 2017, magazine publisher Condé Nast cut jobs, reduced the frequency of several magazines, and shut down the print edition of Teen Vogue, then invested significant new resources in video production, with a senior executive saying "In the next 24 months, I hope that video is half our business... It’s critical. It’s the macro trend of content consumption." In February 2018, Vox Media cut approximately 50 employees, primarily those assigned to "social video," as Vox CEO Jim Bankoff admitted that those efforts were not "viable audience or revenue growth drivers." In August 2020, Facebook Inc. (now Meta Platforms) pivoted Instagram to video in an effort to replicate the success of TikTok and appeal to a younger audience, introducing "reels" as a form of video and promoting them aggressively. Reels accounted more than half the 20 most-viewed posts on Facebook; however, most of these reels were anonymous aggregations of content from TikTok. Elon Musk declared in early 2024 that X (formerly Twitter) was now a "video-first platform", which has been described by critics as a "pivot to video". == As euphemism == In 2017, Journalist Brian Feldman said that "'Pivoting to video' has become a business strategy for digital publishers common enough in recent months to be a kind of cliché — a slick way to describe something else: layoffs." In response, writers use the phrase as gallows humor shorthand for death or cancellation, as in "how do i tell my bf i want our relationship to pivot to video" (SkyNews' Mollie Goodfellow) or "Horse broke its leg, so we had to take it out back and help it 'pivot to video'" (blogger Anil Dash). == Facebook metrics controversy == In September 2016, Facebook admitted that it had reported artificially inflated numbers to its advertisers about how long viewers watched ads leading to an overestimation of 60-80%. Plaintiffs in a later court case allege the discrepancy was as high as 150-900%. Facebook apologized in an official statement and in multiple staff appearances at New York Advertising Week. Two months later, Facebook disclosed additional discrepancies in audience metrics. In October 2018, a California federal court unsealed the text of a class action lawsuit filed by advertisers against Facebook, alleging that Facebook had known since 2015 that its viewership numbers were highly inflated, that internal records showed it "was far from an hon
Social search
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. == Research and implementations == Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles. In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results. In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. In December 2008, Twitter had re-introduced their people search feature. While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. == Social discovery == Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. == Social search engines == A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added. An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aar
Stegomalware
Stegomalware is a form of malicious software that leverages steganography techniques to conceal its code, configuration data, or command-and-control (C&C) communications within seemingly benign digital media such as images, audio files, videos, documents, or network traffic. It typically embeds encrypted or obfuscated payloads into digital media and only extracts and executes them at runtime, which makes traditional signature-based and sandbox-based detection significantly more difficult. Stegomalware has been observed in attacks ranging from advanced persistent threats (APTs) to financially motivated cybercrime, and is now the subject of dedicated academic surveys, research projects, and international law-enforcement initiatives. The key distinction between stegomalware and traditional obfuscated malware lies in the encoding location. After obfuscation, malicious code remains present within the executable and can theoretically be discovered through static analysis. In contrast, stegomalware hides the payload entirely within a cover medium (image, audio, etc.), remaining invisible until the malware dynamically extracts and executes it at runtime. == History == The term stegomalware was formally introduced by researchers Águila, Laskov, and others in the context of mobile malware and presented at the Inscrypt (Information Security and Cryptology) conference in 2014. This marked the first academic formalization of the concept, though earlier work had already identified that botnets and mobile malware could use steganography and covert channels for command-and-control communication over probabilistically unobservable channels. Since its introduction, stegomalware has evolved from a theoretical concern to a documented threat. In 2011, the APT operation known as "Operation Shady RAT" became one of the first documented cases of stegomalware in the wild, using digital images to hide Internet Protocol addresses and command-and-control server addresses. The same year, the Duqu malware (targeting industrial manufacturers) embedded victim data into JPEG image files before exfiltration, making the data transfer virtually undetectable to network-level security tools. From 2014 onwards, stegomalware became more prevalent in organized cybercrime and advanced persistent threat campaigns. Notable examples include Zeus/Zbot, which masked configuration data in images; Gatak/Stegoloader, which hid shellcode in PNG files; TeslaCrypt, which embedded C&C commands in JPEGs; and Cerber, which concealed ransomware payloads within images. By the 2010s, stegomalware had become established as a preferred evasion technique for espionage, financial theft, and ransomware distribution campaigns. Recent surveys (2020–2025) document that stegomalware has increasingly been exploited by adversaries targeting banks, enterprises, government agencies, educational institutions, and internet users via malvertising campaigns. The technique is now considered a sophisticated method of attack worthy of dedicated international law-enforcement attention. == Technical Characteristics and Definitions == Stegomalware operates through a three-component architecture: Stegotext (R): An innocent-looking digital asset (image, audio file, etc.) into which the malicious payload is embedded. Secret key (sk): A key used by the embedding and extraction algorithms, typically hardcoded into the malware. Payload (p): The actual malicious code, configuration data, or C&C commands hidden within the stegotext. The malware extracts the payload at runtime using the secret key and either executes it directly or uses it to download additional stages of the attack. Stegomalware can be classified into several types based on deployment method: Type 0 (Autonomous): Both the stegotext and extraction algorithm are embedded within the malware application itself. The malicious payload is extracted and executed locally without external communication. Type I (Update): The stegotext and secret key are downloaded from a remote server at runtime; only the extraction algorithm is included in the malware. This variant is more flexible, allowing attackers to push updated payloads. Type II (External Algorithm): Neither the stegotext nor the extraction algorithm are distributed with the malware; both are fetched from an attacker-controlled infrastructure, providing maximum flexibility and evasion. == Steganography techniques == === Spatial domain methods === Stegomalware predominantly uses steganographic methods designed for images, as images are the most common cover medium in the wild. The most basic spatial domain technique is Least Significant Bit (LSB) substitution, which replaces the least significant bits of pixel color values with payload bits. While simple and easy to implement, LSB is also relatively easy to detect through statistical analysis. More sophisticated spatial domain techniques include: HUGO (High Undetectable steGO) (2010): Minimizes detectable distortion by distributing the payload across multiple pixels, achieving embedding capacity with reduced statistical footprint. WOW (Wavelet Obtained Weights) (2012): Embeds data preferentially in textured regions of images where modifications are less perceptually noticeable. UNIWARD (Universal Wavelet Relative Distortion) (2014): Uses a universal distortion function applicable to multiple image formats, balancing payload capacity with undetectability. HILL (2014): Applies high-pass and low-pass filters to identify robust embedding regions. MiPOD (Minimizing the Power of Optimal Detector) (2016): Designed to minimize the power of theoretical optimal steganalysis detectors. === Transform domain methods === Transform domain techniques convert images into the frequency domain (e.g., using DCT or DWT) before embedding, allowing for more robust hiding in JPEG and other compressed formats: Embedding in DCT coefficients (used in JPEG compression) Embedding in DWT coefficients (used in lossless formats) Spread spectrum techniques, which distribute the payload across many frequency components Transform domain methods are generally more resistant to noise, compression, and image transformations than spatial methods. === Generative adversarial network (GAN) methods === Recent advances in machine learning have introduced GAN-based steganography, where a generative model produces stego images that minimize detectable artifacts: SGAN (Steganographic GAN) (2017): First GAN applied to steganography, using a generator, discriminator, and steganalysis network. ASDL-GAN (2017): Performs automatic steganographic distortion learning at the pixel level. SteganoGAN (2019): Improves upon earlier GAN models, achieving higher embedding capacity and robustness. HiGAN (Hiding Images GAN) (2020): Enables hiding one image within another while maintaining visual plausibility. GAN-based approaches are more resilient to standard steganalysis attacks but remain an emerging threat requiring further research. == Notable malware campaigns == Stegomalware has been documented in numerous high-profile cyber attacks and campaigns. Notable examples include: Operation Shady RAT (2011): Used digital images to hide command-and-control server addresses in targeted espionage. Duqu (2011): Embedded victim data into JPEG files to exfiltrate industrial control system information. Zeus/Zbot (2014): Masked banking configuration data inside JPEG files exploited via malvertising. Gatak/Stegoloader (2015): Hid shellcode in PNG files for software licensing attacks and bot command execution. TeslaCrypt (2015): Embedded C&C commands and ransomware keys in JPEG images. Cerber (2016): Concealed executable ransomware code in JPEG files distributed via phishing. DNSChanger (2016): Embedded malicious code in PNG files for DNS hijacking campaigns. Sundown Exploit Kit (2017): Distributed exploit code in PNG files via malvertising. AdGholas (2017): Used JPEG steganography to distribute ransomware via malvertising. Synccrypt (2017): Hidden ransomware components in JPEG-steganographic encrypted archives. ZeroT/PlugX (2017): Hid Remote Access Trojan payloads in BMP files for espionage. Loki Bot (2018): Concealed malware installers in JPEG and video files. Waterbug (APT28) (2019): Injected malicious DLLs into WAV audio files. Shlayer (macOS adware) (2019): Hid malicious URLs in JPEG files via malvertising. === Attack vectors === The most common attack vectors for stegomalware include: Phishing emails with malicious attachments or links Malvertising campaigns using malicious banner advertisements Exploit kits through compromised or malicious websites Legitimate application vulnerabilities (e.g., watering-hole attacks) Fake software distribution (cracked software, keygen tools) === Exploitation stages === Stegomalware typically serves one or more roles in attack lifecycles: Payload delivery: Stego images contain full executable code or shellcode. C&C communication: Hidden data contains server addresses or command instructio
Automation integrator
An automation integrator is a systems integrator company or individual who makes different versions of automation hardware and software work together, generally combining several subsystems to work together as one large system. The title may refer to those who only integrate hardware, although these will often work with software integrators. Software created by automation integrators allows devices to communicate with each other, as well as collecting and reporting data. The magazine Control Engineering publishes an annual “Automation Integrator Guide” which lists over 2,000 automation integrators. They also give an annual system integrator of the year award to three automation integration firms. The Control System Integrators Association (CSIA) maintains a buyers' guide of over 1200 member and nonmember systems integrators known as the Industrial Automation Exchange, or CSIA Exchange for short. == Certification == The Control System Integrators Association (CSIA) certifies automation integrators, through an audit based on 79 critical criteria from the best practices manual. Companies must be associate members of the CSIA to be eligible for certification. Integrators can also receive certification through a program launched in 2012 by the Robotics Industries Association. == Industries == Automation Integrators work in a wide variety of industries which use robotics and automation. Some of the most common include:
PitchYaGame
PitchYaGame or #PitchYaGame (sometimes abbreviated to PYG) is a volunteer movement hosted on the social media platform Twitter to showcase, and present awards for, independent video games from around the world. == Description == PitchYaGame is hosted on the social media platform Twitter to showcase independent video games from around the world. Video pitches are presented by developers in June and November each year, and use the hashtag #PitchYaGame to identify and reference news about the showcase and the individual pitches, and the presentation of awards. The showcase was founded in May 2020 by Liam Twose, with the mission of recognising independent video games, and "focused on empowering indie game developers to strengthen their position in the industry." Twose has made clear that PitchYaGame is a showcase and not a hardcore competition, with "[j]ust enough of a push to make sure people put their best pitch forward." The team now comprises Twose (@LiamTwose at Twitter), operations manager "Indie Game Lover" (@IndieGameLover), and host Sarah Clancy (@ImSarahNow). The pitches were originally made monthly, with entries split into a number of categories, but this proved unmanageable. PitchYaGame collaborator, Sarah Clancy reported that judging the many entries on a monthly basis was "difficult and unwieldy." Therefore, pitches were later switched to six monthly, "feature creep" was reduced, and awards streamlined into gold, silver, bronze, runners-up, and most viral. == Sponsorship == In June 2021, PitchYaGame prizes were sponsored by Xsolla, and in November 2021 by Aurora Punks and Cold Pixel. No cash prizes were available in 2022, as the organisers moved PitchYaGame into a less-competitive, "more showcase centric format". == Reception == In October 2020, Elijah Beahm at The Escapist wrote that "One of the greatest challenges for any game is landing a solid pitch. You have to sell people, maybe even a publisher, to take your idea seriously. Most of the time, it's an obfuscated process that leaves the average developer scratching their heads, but Liam Twose and his team behind #PitchYaGame, 'PYG' for short, are looking to change all that with some clever social engineering." In March 2021, Cameron Koch at GameSpot wrote that "Using the #PitchYaGame, thousands of indie developers tweeted out pitches for their games on November 2 as part of a social media contest, and the results are astounding." He went on to say that "There is no arguing with the results. According to Twose, around 1100-1300 games were shared with the hashtag, and some real gems look to have shined through." In November 2021, Stafano "Stef" Castelli at IGN Italia wrote that "I myself enjoyed 'browsing through' the competitors, discovering a handful of intriguing video games in development." (translated from Italian). In November 2022, Eric Bartelson at Premortem Games wrote that "It's a great way to get games noticed by fellow developers, but also publishers, investors and press." In June 2023, Mark Plunkett in Kotaku wrote about the impossibility of keeping up with all the video game releases, and described PitchYaGame, which has attracted over 10,000 pitches since 2020, as an "astoundingly simple idea" that has "become an increasingly useful spot to catch up on some excellent-looking games that we may have otherwise completely slept on."