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.
Model-based clustering
In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group. == Model-based clustering == Suppose that for each of n {\displaystyle n} observations we have data on d {\displaystyle d} variables, denoted by y i = ( y i , 1 , … , y i , d ) {\displaystyle y_{i}=(y_{i,1},\ldots ,y_{i,d})} for observation i {\displaystyle i} . Then model-based clustering expresses the probability density function of y i {\displaystyle y_{i}} as a finite mixture, or weighted average of G {\displaystyle G} component probability density functions: p ( y i ) = ∑ g = 1 G τ g f g ( y i ∣ θ g ) , {\displaystyle p(y_{i})=\sum _{g=1}^{G}\tau _{g}f_{g}(y_{i}\mid \theta _{g}),} where f g {\displaystyle f_{g}} is a probability density function with parameter θ g {\displaystyle \theta _{g}} , τ g {\displaystyle \tau _{g}} is the corresponding mixture probability where ∑ g = 1 G τ g = 1 {\displaystyle \sum _{g=1}^{G}\tau _{g}=1} . Then in its simplest form, model-based clustering views each component of the mixture model as a cluster, estimates the model parameters, and assigns each observation to cluster corresponding to its most likely mixture component. === Gaussian mixture model === The most common model for continuous data is that f g {\displaystyle f_{g}} is a multivariate normal distribution with mean vector μ g {\displaystyle \mu _{g}} and covariance matrix Σ g {\displaystyle \Sigma _{g}} , so that θ g = ( μ g , Σ g ) {\displaystyle \theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle \theta _{g}} for g = 1 , … , G {\displaystyle g=1,\ldots ,G} , are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. The Bayesian approach also allows for the case where the number of components, G {\displaystyle G} , is infinite, using a Dirichlet process prior, yielding a Dirichlet process mixture model for clustering. === Choosing the number of clusters === An advantage of model-based clustering is that it provides statistically principled ways to choose the number of clusters. Each different choice of the number of groups G {\displaystyle G} corresponds to a different mixture model. Then standard statistical model selection criteria such as the Bayesian information criterion (BIC) can be used to choose G {\displaystyle G} . The integrated completed likelihood (ICL) is a different criterion designed to choose the number of clusters rather than the number of mixture components in the model; these will often be different if highly non-Gaussian clusters are present. === Parsimonious Gaussian mixture model === For data with high dimension, d {\displaystyle d} , using a full covariance matrix for each mixture component requires estimation of many parameters, which can result in a loss of precision, generalizabity and interpretability. Thus it is common to use more parsimonious component covariance matrices exploiting their geometric interpretation. Gaussian clusters are ellipsoidal, with their volume, shape and orientation determined by the covariance matrix. Consider the eigendecomposition of a matrix Σ g = λ g D g A g D g T , {\displaystyle \Sigma _{g}=\lambda _{g}D_{g}A_{g}D_{g}^{T},} where D g {\displaystyle D_{g}} is the matrix of eigenvectors of Σ g {\displaystyle \Sigma _{g}} , A g = diag { A 1 , g , … , A d , g } {\displaystyle A_{g}={\mbox{diag}}\{A_{1,g},\ldots ,A_{d,g}\}} is a diagonal matrix whose elements are proportional to the eigenvalues of Σ g {\displaystyle \Sigma _{g}} in descending order, and λ g {\displaystyle \lambda _{g}} is the associated constant of proportionality. Then λ g {\displaystyle \lambda _{g}} controls the volume of the ellipsoid, A g {\displaystyle A_{g}} its shape, and D g {\displaystyle D_{g}} its orientation. Each of the volume, shape and orientation of the clusters can be constrained to be equal (E) or allowed to vary (V); the orientation can also be spherical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: It can be seen that many of these models are more parsimonious, with far fewer parameters than the unconstrained model that has 90 parameters when G = 4 {\displaystyle G=4} and d = 9 {\displaystyle d=9} . Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also be used as the basis for a method to choose the variables in the clustering model, eliminating variables that are not useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. The mixture-of-experts framework extends model-based clustering to include covariates. == Example == We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but we use this information only for evaluating clustering methods, not for classifying subjects. The BIC plot shows the BIC values for each combination of the number of clusters, G {\displaystyle G} , and the clustering model from the Table. Each curve corresponds to a different clustering model. The BIC favors 3 groups, which corresponds to the clinical assessment. It also favors the unconstrained covariance model, VVV. This fits the data well, because the normal patients have low values of both sspg and insulin, while the distributions of the chemical and overt diabetes groups are elongated, but in different directions. Thus the volumes, shapes and orientations of the three groups are clearly different, and so the unconstrained model is appropriate, as selected by the model-based clustering method. The classification plot shows the classification of the subjects by model-based clustering. The classification was quite accurate, with a 12% error rate as defined by the clinical classification. Other well-known clustering methods performed worse with higher error rates, such as single-linkage clustering with 46%, average link clustering with 30%, complete-linkage clustering also with 30%, and k-means clustering with 28%. == Outliers in clustering == An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivariate normal densities by t {\displaystyle t} -distributions, with the idea that the long tails of the t {\displaystyle t} -distribution would ensure robustness to outliers. However, this is not breakdown-robust. A third approach is the "tclust" or data trimming approach which excludes observations identified as outliers when estimating the model parameters. == Non-Gaussian clusters and merging == Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. == Non-continuous data == === Categorical data === Clustering multivariate categorical data is most often done using the latent class model. This assumes that the data arise from a finite mixture model, where within each cluster the variables are independent. === Mixed data === These arise when variables are of different types, such as continuous, categorical or ordinal data. A latent class model for mixed data assumes local independence between the variable. The location model relaxes the local independence assumption. The clustMD approach assumes that the observed variables are manifestations of underlying continuous Gaussian latent
CADE ATP System Competition
The CADE ATP System Competition (CASC) is an annual competition of fully automated theorem provers for classical logic. == Competition == CASC is associated with the Conference on Automated Deduction and the International Joint Conference on Automated Reasoning organized by the Association for Automated Reasoning. It has inspired similar competition in related fields, in particular the successful SMT-COMP competition for satisfiability modulo theories, the SAT Competition for propositional reasoners, and the modal logic reasoning competition. The first CASC, CASC-13, was held as part of the 13th Conference on Automated Deduction at Rutgers University, New Brunswick, NJ, in 1996. Among the systems competing were Otter and SETHEO.
Imagen (text-to-image model)
Imagen is a series of text-to-image models developed by Google DeepMind. They were developed by Google Brain until the company's merger with DeepMind in April 2023. Imagen is primarily used to generate images from text prompts, similar to Stability AI's Stable Diffusion, OpenAI's DALL-E, or Midjourney. The original version of the model was first discussed in a paper from May 2022. The tool produces high-quality images and is available to all users with a Google account through services including Gemini, ImageFX, and Vertex AI. == History == Imagen's original version was first presented in a paper published in May 2022. It featured the ability to generate high-fidelity images from natural language. The second version, Imagen 2 was released in December 2023. The standout feature was text and logo generation. Imagen 3 was released in August 2024. Google claims that the newest version provides better detail and lighting on generated images. On 20 May 2025 at Google I/O 2025 the company released an improved model, Imagen 4. == Technology == Imagen uses two key technologies. The first is the use of transformer-based large language models, notably T5, to understand text and subsequently encode text for image synthesis. The second is the use of cascaded diffusion models providing high-fidelity image generation. Imagen generates image in three stages, starting from a base of 64x64, then upsampled to 256x256 and 1024x1024. Imagen 4 generates image up to 2k. == Capabilities == Imagen can generate photorealistic images from text prompts. It can also create various styles, such as cinematic, 35mm film, illustration, and surreal. Like most text-to-image generative AI models, Imagen has difficulty rendering human fingers, text, ambigrams and other forms of typography. The model can generate images in five aspect ratios, namely 9:16, 3:4, 1:1, 4:3, and 16:9. Imagen can also refine already generated images by editing existing text prompts.
AI-generated content in American politics
In American politics since the 2020s, political figures have deployed AI-generated images, videos, and audio to attack opponents, create misleading narratives, or inflame emotions. The use of generative AI by American political figures has been subject to criticism from many sides of the political spectrum. Republican president Donald Trump has notably used generative AI in several posts to Truth Social during his second term, many of which have made headlines due to their inflammatory nature. == Background == Generative artificial intelligence is a subfield of artificial intelligence that uses generative models to generate text, images, videos, audio, software code or other forms of data. In the mid 2020s with the release of 15.ai, ChatGPT, DALL-E and other generative artificial intelligence applications there was an AI boom. There has been an increase of usage of generative-AI within the United States political field during this boon, with both Republican and Democratic party members using it. The Trump administration during his second term, have embraced the use of AI-generated images, causing some misinformation experts to raise concerns about the continued usage would cause the erosion of public perception of the truth. In response to some criticisms White House deputy communications director Kaelan Dorr posted on X that the "memes will continue" with White House deputy press secretary Abigail Jackson also mocking concerns. == History of usage == === 2023 === In April 2023, the Republican National Committee released an attack ad made entirely with AI-generated images depicting a dystopian future under Joe Biden's re-election. === 2024 === Generative AI has increased the efficiency with which political candidates were able to raise money by analyzing donor data and identifying possible donors and target audiences. In March 2024 Democratic consultant working for Dean Phillips has admitted to using AI to generate a robocall which used Joe Biden's voice to discourage voter participation. In August 2024, The Atlantic noted that AI slop was becoming associated with the political right in the United States, who were using it for shitposting and engagement farming on social media, with the technology offering "cheap, fast, on-demand fodder for content". AI slop is frequently used in political campaigns in an attempt at gaining attention through content farming. === 2025 === The initial version of the Make Our Children Healthy Again Assessment of children's health issues, released by a commission of cabinet members and officials of the Trump administration, and led by US Department of Health and Human Services Secretary Robert F. Kennedy Jr., reportedly cited nonexistent and garbled references generated using artificial intelligence. Democratic governor Gavin Newsom has used AI-generated images to criticize Trump. In the midst of disruptions to food stamp distribution during the 2025 US government shutdown, anonymous social media users began using OpenAI's Sora to post slop videos of welfare queens complaining, stealing, and rioting in supermarkets; many comments to the videos appeared unaware that they were AI-generated, or acknowledged that they were AI-generated but nonetheless useful in pushing a narrative of widespread welfare fraud. On September 6, 2025, Trump posted an image on Truth Social making a reference to "Chipocalypse Now". Trump's post consisted of an AI-generated image showing Trump frowning and wearing a U.S. Cavalry hat and sunglasses, in front of Lake Michigan with the city of Chicago behind him with a smoke and fire spread across the background with five U.S. Army helicopters in the sky. The words "Chipocalypse Now" are rendered in a font resembling that in which the title of the 1979 film Apocalypse Now was styled. === 2026 === On February 5, 2026, Donald Trump shared a video of Barack and Michelle Obama depicted as apes in a Truth Social post. The two-second AI-generated clip of the Obamas portrayed as apes set to "The Lion Sleeps Tonight" appeared at the end of a one-minute two second long video, the rest of which was about false claims of voter fraud during the 2020 presidential election. The post received at least 4,650 likes, 409 comments, and 1,470 reTruths before it was deleted the next morning. The short clip was part of a longer AI-generated video posted in October 2025. The post received widespread backlash and bipartisan condemnation of the video as racist. In April 2026, Trump posted a picture of himself depicted as Jesus, drawing widespread criticism from Evangelicals and Catholics, resulting in Trump deleting the post hours later and claiming he believed he was depicted as a doctor. == Examples of use == === Election campaigns === In 2023, while he was still running for re-election, the presidential campaign of Joe Biden prepared a task force to respond to AI images and videos. The campaign for the 2024 Republican nominee, Donald Trump, has used deepfake videos of political opponents in campaign ads and fake images showing Trump with black supporters. During the first five months of his second term in 2025, Trump posted several AI-generated images of himself on official government social media accounts, including him as the Pope, him as a Jedi, and him as a muscular man. In August 2024, Trump posted a series of AI-generated images on his social media platform, Truth Social, that portrayed fans of the singer Taylor Swift in "Swifties for Trump" T-shirts, as well as a photo of the singer herself appearing to endorse Trump's 2024 presidential campaign. The images originated from the conservative Twitter account @amuse, which posted numerous AI slop images leading up to the 2024 United States elections that were shared by other high-profile figures within the US Republican Party, such as Elon Musk, who has publicly endorsed the utilization of generative AI, furthering this association. In 2024, Michigan GOP candidate Anthony Hudson posted an AI-generated video showing Martin Luther King Jr. endorsing his campaign, later claiming it was uploaded by a volunteer. In his 2025 bid to be the Democratic nominee for governor of New Jersey, Rep. Josh Gottheimer drew attention and criticism when he released a TV ad that used AI to portray him as a shirtless boxer sparring with Donald Trump in a boxing ring. In November 2025, the campaign of Mike Collins, a GOP candidate in the 2026 United States Senate election in Georgia released a fake video, generated by artificial intelligence, that depicted Democrat Jon Ossoff defending his vote on the 2025 United States federal government shutdown by declaring he could never say no to Chuck Schumer and that SNAP recipients did not attend his out-of-state fundraisers. The Collins campaign also shared an AI-generated video featuring Collins as a shirtless blue jeans model, referencing an American Eagle Outfitters advertisement featuring Sydney Sweeney. During the 2026 Los Angeles mayoral election, candidate Spencer Pratt reposted an AI-generated video portraying Pratt as Batman and prominent California politicians such as Karen Bass, Gavin Newsom, and Kamala Harris, as unruly aristocrats. Former governor of Florida Jeb Bush described the ad as “maybe the best political ad of the year.” In response, a spokesperson for Bass's campaign said, he was "doing his best Trump impression." Bass further responded that the AI ads are "taking on a violent trend." === Protests === In response to the nation-wide No Kings protests in October 2025, Donald Trump posted a video depicting himself flying a fighter jet and releasing feces on crowds of demonstrators, including Democratic influencer Harry Sisson. === Foreign interference === Officials from the ODNI and FBI have stated that Russia, Iran, and China used generative artificial intelligence tools to create fake and divisive text, photos, video, and audio content to foster anti-Americanism and engage in covert influence campaigns. The use of artificial intelligence was described as an accelerant rather than a revolutionary change to influence efforts. Regulation of AI with regard to elections was unlikely to see a resolution for most of the 2024 United States general election season. === Disasters and wars === In the aftermath of Hurricane Helene in the United States, members of the Republican Party circulated an AI-generated image of a young girl holding a puppy in a flood, and used it as evidence of the failure of President Joe Biden to respond to the disaster. Some, like Trump supporter Amy Kremer, shared the image on social media but acknowledged that it was not genuine. In February 2025, Donald Trump shared an AI-generated video on Truth Social depicting a hypothetical Gaza after a Trump takeover. The video's creator claimed it was made as political satire. == Reception == Ramesh Srinivasan, a professor at UCLA raised concerns about the use of AI-generative images stating that many people are questioning where they can find trustab
QANDA
QANDA (stands for 'Q and A') is an AI-based learning platform developed by Mathpresso Inc., a South Korea-based education technology company. Its best known feature is a solution search, which uses optical character recognition technology to scan problems and provide step-by-step solutions and learning content. As of March 2024, QANDA solved over 6.3 billion questions. QANDA has 90 million total registered users and has reached 8 million monthly active users (MAU) in 50 countries. 90% of the cumulative users are from overseas such as Vietnam and Indonesia. In January 2024, its MathGPT, a math-specific small large language model set a new world record, surpassed Microsoft's 'ToRA 13B', the previous record holder in benchmarks assessing mathematical performance such as 'MATH' (high school math) and 'GSM8K' (grade school math). 'MathGPT' was co-developed with Upstage and KT. In March 2024, Mathpresso launched 'Cramify' (formerly known as Prep.Pie), an AI-powered study material generator designed to create personalized exam prep materials for U.S. college students. It uses generative AI to create customized study materials uploaded by students. Its features include a range of tools including study summarizer and question solver. == History == Co-founder Jongheun ‘Ray’ Lee first came up with the idea of QANDA during his freshman year in college. While he was tutoring to earn money, Lee realized that the quality of education a student receives is greatly based on their location. Lee saw his K-12 students were regularly asking similar questions and realized that these questions were from a pre-selected number of textbooks currently being used in schools. He decided to team up with his high school friend, Yongjae ‘Jake’ Lee to build a platform whereby, one uses a mobile app to scan and submit questions, and students can ask and receive detailed responses. Lee's school friends, Wonguk Jung and Hojae Jeong, joined the team. In June 2015, Mathpresso, Inc. was founded in Seoul, South Korea. In January 2016, Mathpresso's first product QANDA was launched. It supported a Q&A feature between students and tutors. In October 2017, QANDA introduced an AI-based search capability that permitted users to search for answers in seconds. In April 2020, Jake Yongjae Lee(CEO & co-founder) and Ray Jongheun Lee (co-founder) were selected as Forbes 30 under 30 Asia. In June 2021, QANDA raised $50 million in series C funding. Jake Yongjae Lee was recognized as an Innovator Under 35 by MIT Technology Review. In November 2021, QANDA secured a strategic investment from Google. Since its inception, it has received backing in Series C funding from investors namely Google, Yellowdog, GGV Capital, Goodwater Capital, KDB, and SKS Private Equity with participation from SoftBank Ventures Asia, Legend Capital, Mirae Asset Venture Investment, and Smilegate Investment. In September 2023, Mathpresso has raised $8 million (10 billion KRW) from Korea's telecom giant, KT. The total cumulative investment is about 130 million US dollars. The partnership aims to accelerate the development of an education-specific Large Language Model. The company intends to incorporate the LLM model to fortify its AI tutor, which later will be integrated into the existing services: QANDA App, B2B & B2G Saas, and 1:1 online tutoring (QANDA Tutor). == Features == QANDA features OCR-based solution search, one-on-one Q&A tutoring, a study timer. In 2021, QANDA launched additional features, including the premium subscription model that offers unlimited “byte-sized” micro-video lectures and the community feature that enhances collaborative learning. In 2021, QANDA launched QANDA Tutor, a tablet-based 1:1 tutoring service and QANDA Study, a 1:N online school in Vietnam. In 2022, QANDA launched an exam prep feature that offers past exam materials from school via online. This feature is currently available in South Korea. In August 2023, QANDA launched a beta version of an LLM-powered AI Tutor. == Awards and recognition == Best Hidden Gems of 2017 by Google Playstore 2018 AWS AI Startup Challenge Award National representative for the Google AI for Social Good APAC, 2018 Best Self-Improvement Apps of 2018 by Google Playstore GSV Edtech 150 — the Most Transformational Growth Companies in Digital Learning Speaker at the Google App Summit, 2021 Selected as a prospect unicorn company by Korea Technology Finance Corporation in 2023 Winner of G20-DIA Global Pitching in 2023 2021, 2022, 2023 East Asia EdTech 150 by HolonIQ
Sourcegraph
Sourcegraph Inc. is a company developing code search and code intelligence tools that semantically index and analyze large codebases so that they can be searched across commercial, open-source, local, and cloud-based repositories. The company has two core products: Code Search and Amp. A previous core product, Cody, retains limited legacy support for existing customers. Code Search was initially released in 2013 under the name Sourcegraph, but was rebranded to Code Search when the company unveiled Cody in 2023. As of 2021, the platform has around 800,000 developers and has indexed around 54 billion lines of code. In July 2025, new accounts for Cody were discontinued, and a new AI coding project, Amp, was released. In December 2025, Amp was spun-off to become a separate company. == History == Sourcegraph Inc. was founded by Stanford graduates Quinn Slack and Beyang Liu to drive the development of a code search and code intelligence tool, formerly called Sourcegraph. It was first released in 2013 but was rebranded to Code Search in 2023. It was partly inspired by Liu's experience using Google Code Search while he was a Google intern, It was designed to "tackle the big code problem" by enabling developers to manage large codebases that span multiple repositories, programming languages, file formats, and projects. Code Search was initially self-hosted by each customer on their own infrastructure. Early customers included Uber, Dropbox, and Lyft. In 2016, Code Search was criticized for being provided with a Fair Source License with the developers explaining that "all of Sourcegraph's source code is publicly available and hackable" and was intended to "help open sourcers strike a balance between getting paid and preserving their values". In 2018, Code Search was licensed under the Apache License 2.0, and Sourcegraph OSS has since been released under the Apache License 2.0. The commercial version, Code Search Enterprise, has been released under its own license. In 2023, Code Search was criticized for dropping the Apache license for most of its code, leaving it public but only available under its Enterprise license. In 2024, the main repository was made completely private. In 2019, Code Search was integrated into the GitLab codebase, giving GitLab users access to a browser-based developer platform. In 2021, a browser-based portal became available, allowing users to browse open-source projects and personal private code for free. In 2022, Sourcegraph Cloud, a commercial single-tenant cloud solution for organizations with more than 100 developers, was launched. Sourcegraph has raised a total of $223 million in financing to date. Its most recent $125 million Series D investment in 2021 valued the company at $2.625 billion, a 300% growth from its previous valuation in 2020. In 2023 Sourcegraph Inc. unveiled their new product Cody, and rebranded Sourcegraph to Code Search. In 2025, Sourcegraph announced the discontinuation of Cody Free, Pro, and Enterprise Starter plans, effective July 23, 2025, and launched Amp, a new AI coding agent. == Products == The company has three major products: Code Search, Amp, and Cody. === Sourcegraph Code Search === Code Search tool is used to search and summarize code. It supports over 30 programming languages and integrates with GitHub and GitLab for code hosting, Codecov for code coverage, and Jira Software for project management. Sourcegraph's Code Search uses a variant of Google's PageRank algorithm to rank results by relevance. While it was originally launched under the Apache License, on June 13, 2023, it was relicensed to the non-open-source "Sourcegraph Enterprise" license. Then, on August 22, 2024, the source code was moved to a private repository, and thus no longer source-available. === Sourcegraph Amp === Launched in 2025, Amp can generate code, generate documentation, write tests, and perform refactoring operations on projects. The tool operates on a credit-based pricing model and is available through web interfaces, command-line tools, and IDE extensions. In December 2025, Sourcegraph announced that Amp would be spun-off to become a separate company. === Sourcegraph Cody === Cody is an AI coding application for writing and maintaining code. Cody was released in December 2023 and was available for Microsoft Visual Studio Code and most JetBrains IDEs. As of July 2025, Cody Free, Pro, and Enterprise Starter plans have been discontinued, with only Cody Enterprise remaining available for existing enterprise customers.