Canva Pty Ltd. is an Australian multinational proprietary software company launched in 2013 based in Sydney, Australia. The platform provides a graphic design platform to create visual content for presentations, websites, and other digital products. Its uses include templates for presentations, posters, and social media content, as well as photo and video editing functionality. The platform uses a drag-and-drop interface designed for users without professional design training or experience. Canva operates on a freemium model and has added features such as print services and video editing tools over time. == History == === 2013–2020 === Canva was founded in Perth, Australia, by Melanie Perkins, Cliff Obrecht and Cameron Adams on 1 January 2013. One of the company's early investors was Susan Wu, an American entrepreneur. In its first year, Canva had more than 750,000 users. In 2017, the company reached profitability and had 294,000 paying customers. In January 2018, Perkins announced that the company had raised A$40 million from Sequoia Capital, Blackbird Ventures, and Felicis Ventures, and the company was valued at A$1 billion. It raised A$70 million in May 2019, followed by A$85 million in October 2019 and the launch of Canva for Enterprise. In December 2019, Canva announced Canva for Education, a free product for schools and other educational institutions intended to facilitate collaboration between students and teachers. === 2021–2025 === In June 2020, Canva announced a partnership with FedEx Office and with Office Depot the following month. As of June 2020, Canva's valuation had risen to A$6 billion, rising to A$40 billion by September 2021. In September 2021, Canva raised US$200 million, with its value peaking that year at US$40 billion. By September 2022, the valuation of the company had leveled at US$26 billion. While Canva's value declined from its 2021 peak by mid-2022, it remained one of Australia's most prominent technology companies, alongside Atlassian. In March 2022, Canva had over 75 million monthly active users. In 2023, the pair were named in the Australian Financial Review's AFR Rich List as among the 10 most wealthy people in Australia. On 7 December 2022, Canva launched Magic Write, which is the platform's AI-powered copywriting assistant. On 22 March 2023, Canva announced its new Assistant tool, which makes recommendations on graphics and styles that match the user's existing design. On 11 January 2024, Canva launched its own GPT in OpenAI's GPT Store. The company has announced it intends to compete with Google and Microsoft in the office software category with website and whiteboard products. In May 2024, the company announced the launch of Canva Enterprise, a plan designed for large organisations, alongside new tools including Work Kits, Courses and AI capabilities. In 2024, it announced a co-funded solar energy project to enhance its sustainability efforts. On 10 April 2025, Canva released Visual Suite 2. The new interface combines Canva's design and productivity tools. New features include a spreadsheets application (Canva Sheets), a generative AI coding assistant (Canva Code), a chatbot, and an updated photo editor that can modify or remove background objects. In August 2025, Canva launched a stock sale to employees, valuing the company at US$42 billion. == Acquisitions == In 2018, the company acquired presentations startup Zeetings for an undisclosed amount, as part of its expansion into the presentations space. In May 2019, the company announced the acquisitions of Pixabay and Pexels, two free stock photography sites based in Germany, which enabled Canva users to access their photos for designs. In February 2021, Canva acquired Austrian startup Kaleido.ai and the Czech-based Smartmockups. In 2022, Canva acquired Flourish, a London-based data visualization startup. In March 2024, Canva acquired UK-based Serif, the developers of the Affinity suite of graphic design software, for approximately $380 million. In August 2024, Canva acquired the AI image generation platform and startup, Leonardo AI, for an undisclosed amount. In June 2025, it was announced that Canva had acquired Australian AI marketing startup MagicBrief for an undisclosed amount. In February 2026, Canva acquired two startups: Cavalry, which specializes in animation software, and MangoAI, which focuses on improving advertising performance. In April 2026, Canva acquired Simtheory, an AI Workflow Tool, and Ortto, a marketing automation tool. == Philanthropy == Canva's co-founders, Melanie Perkins and Cliff Obrecht, have publicly stated their intention to donate a significant portion of their personal wealth to charity. In 2021, Canva started a partnership with GiveDirectly, a nonprofit organization operating in low income areas that makes unconditional cash transfers to families living in extreme poverty. Since then, the company has donated $50 million to support GiveDirectly's work across Malawi. In 2025, Canva announced an additional $100 million commitment to expand its GiveDirectly partnership. == Controversies == === Data breach === In May 2019, Canva experienced a data breach in which the data of roughly 139 million users was exposed. The exposed data included real names of users, usernames, email addresses, geographical information, and password hashes for some users. In January 2020, approximately 4 million user passwords were decrypted and shared online. Canva responded by resetting the passwords of every user who had not changed their password since the initial breach. === Russian operations === In May 2022 Canva was criticized for continuing to provide free access to its services in Russia, even after suspending payment processing in the country. Activists from the Ukrainian diaspora in Australia and others said this could be viewed as indirectly supporting Russia’s war effort. They noted the company was the only one of several major Australian firms to receive the lowest “digging in” rating on a tracker run by the Yale School of Management for failing to pull out of Russia. Canva responded that it had suspended financial transactions in Russia from March 2022 and maintained the free version to allow the continued creation and sharing of “pro-peace and anti-war” content for its 1.4 million Russian users.
Frame grabber
A frame grabber is an electronic device that captures (i.e., "grabs") individual, digital still frames from an analog video signal or a digital video stream. It is usually employed as a component of a computer vision system, in which video frames are captured in digital form and then displayed, stored, transmitted, analyzed, or combinations of these. Historically, frame grabber expansion cards were the predominant way to interface cameras to PCs. Other interface methods have emerged since then, with frame grabbers (and in some cases, cameras with built-in frame grabbers) connecting to computers via interfaces such as USB, Ethernet and IEEE 1394 ("FireWire"). Early frame grabbers typically had only enough memory to store a single digitized video frame, whereas many modern frame grabbers can store multiple frames. Modern frame grabbers often are able to perform functions beyond capturing a single video input. For example, some devices capture audio in addition to video, and some devices provide, and concurrently capture frames from multiple video inputs. Other operations may be performed as well, such as deinterlacing, text or graphics overlay, image transformations (e.g., resizing, rotation, mirroring), and conversion to JPEG or other compressed image formats. To satisfy the technological demands of applications such as radar acquisition, manufacturing and remote guidance, some frame grabbers can capture images at high frame rates, high resolutions, or both. == Circuitry == Analog frame grabbers, which accept and process analog video signals, include these circuits: Input signal conditioner that buffers the analog video input signal to protect downstream circuitry Video decoder that converts SD analog video (e.g., NTSC, SECAM, PAL) or HD analog video (e.g., AHD, HD-TVI, HD-CVI) to a digital format Digital frame grabbers, which accept and process digital video streams, include these circuits: Digital video decoder that interfaces to and converts a specific type of digital video source, such as Camera Link, CoaXPress, DVI, GigE Vision, LVDS, or SDI Circuitry common to both analog and digital frame grabbers: Memory for storing the acquired image (i.e., a frame buffer) A bus interface through which a processor can control the acquisition and access the data General purpose I/O for triggering image acquisition or controlling external equipment == Applications == === Healthcare === Frame grabbers are used in medicine for many applications, including telenursing and remote guidance. In situations where an expert at another location needs to be consulted, frame grabbers capture the image or video from the appropriate medical equipment, so it can be sent digitally to the distant expert. === Manufacturing === "Pick and place" machines are often used to mount electronic components on circuit boards during the circuit board assembly process. Such machines use one or more cameras to monitor the robotics that places the components. Each camera is paired with a frame grabber that digitizes the analog video, thus converting the video to a form that can be processed by the machine software. === Network security === Frame grabbers may be used in security applications. For example, when a potential breach of security is detected, a frame grabber captures an image or a sequence of images, and then the images are transmitted across a digital network where they are recorded and viewed by security personnel. === Personal use === In recent years with the rise of personal video recorders like camcorders, mobile phones, etc. video and photo applications have gained ascending prominence. Frame grabbing is becoming very popular on these devices. === Astronomy & astrophotography === Amateur astronomers and astrophotographers use frame grabbers when using analog "low light" cameras for live image display and internet video broadcasting of celestial objects. Frame grabbers are essential to connect the analog cameras used in this application to the computers that store or process the images.
Representational harm
Systems cause representational harm when they misrepresent a group of people in a negative manner. Representational harms include perpetuating harmful stereotypes about or minimizing the existence of a social group, such as a racial, ethnic, gender, or religious group. Machine learning algorithms often commit representational harm when they learn patterns from data that have algorithmic bias, and this has been shown to be the case with large language models. While preventing representational harm in models is essential to prevent harmful biases, researchers often lack precise definitions of representational harm and conflate it with allocative harm, an unequal distribution of resources among social groups, which is more widely studied and easier to measure. However, recognition of representational harms is growing and preventing them has become an active research area. Researchers have recently developed methods to effectively quantify representational harm in algorithms, making progress on preventing this harm in the future. == Types == Three prominent types of representational harm include stereotyping, denigration, and misrecognition. These subcategories present many dangers to individuals and groups. Stereotypes are oversimplified and usually undesirable representations of a specific group of people, usually by race and gender. This often leads to the denial of educational, employment, housing, and other opportunities. For example, the model minority stereotype of Asian Americans as highly intelligent and good at mathematics can be damaging professionally and academically. Representational harm happens when the representation of details teams improves damaging stereotypes, developing social exclusion and prejudice. This experience is particularly noticeable in the depiction of marginalised groups, containing people of color, women, LGBTQ+ people, and people with handicaps. Media depictions of these groups generally stop working to catch their array and intricacy. Instead, they are typically reduced to one-dimensional caricatures, which ultimately continue social prejudices. These organised depictions contribute to the help of hazardous stereotypes and the marginalisation of these locations. Denigration is the action of unfairly criticizing individuals. This frequently happens when the demeaning of social groups occurs. For example, when searching for "Black-sounding" names versus "white-sounding" ones, some retrieval systems bolster the false perception of criminality by displaying ads for bail-bonding businesses. A system may shift the representation of a group to be of lower social status, often resulting in a disregard from society. Research shows that hazardous depictions in the media can have substantial emotional and social impacts on both individuals and areas. Lawrence Bobo examined the issue of Ethnic stereotype in film, tv, and marketing. African Americans are commonly received duties specified by features such as "violent tendencies," "laziness," or being "merely for contentment features." While these representations might appear varied externally, they stay to boost underlying frameworks of white prominence and racial inequality. As a circumstances, Black individuals are frequently represented as law offenders or in secondary roles, which adds to the support of Ethnic stereotype and Institutional racism. Misrecognition, or incorrect recognition, can display in many forms, including, but not limited to, erasing and alienating social groups, and denying people the right to self-identify. Erasing and alienating social groups involves the unequal visibility of certain social groups; specifically, systematic ineligibility in algorithmic systems perpetuates inequality by contributing to the underrepresentation of social groups. Not allowing people to self-identify is closely related as people's identities can be 'erased' or 'alienated' in these algorithms. Misrecognition causes more than surface-level harm to individuals: psychological harm, social isolation, and emotional insecurity can emerge from this subcategory of representational harm. == Quantification == As the dangers of representational harm have become better understood, some researchers have developed methods to measure representational harm in algorithms. Modeling stereotyping is one way to identify representational harm. Representational stereotyping can be quantified by comparing the predicted outcomes for one social group with the ground-truth outcomes for that group observed in real data. For example, if individuals from group A achieve an outcome with a probability of 60%, stereotyping would be observed if it predicted individuals to achieve that outcome with a probability greater than 60%. The group modeled stereotyping in the context of classification, regression, and clustering problems, and developed a set of rules to quantitatively determine if the model predictions exhibit stereotyping in each of these cases. Other attempts to measure representational harms have focused on applications of algorithms in specific domains such as image captioning, the act of an algorithm generating a short description of an image. In a study on image captioning, researchers measured five types of representational harm. To quantify stereotyping, they measured the number of incorrect words included in the model-generated image caption when compared to a gold-standard caption. They manually reviewed each of the incorrectly included words, determining whether the incorrect word reflected a stereotype associated with the image or whether it was an unrelated error, which allowed them to have a proxy measure of the amount of stereotyping occurring in this caption generation. These researchers also attempted to measure demeaning representational harm. To measure this, they analyzed the frequency with which humans in the image were mentioned in the generated caption. It was hypothesized that if the individuals were not mentioned in the caption, then this was a form of dehumanization. == Examples == One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference between Black people and gorillas. Google issued an apology and fixed the issue by blocking its algorithms from classifying anything as a primate. In 2023, Google's photos algorithm was still blocked from identifying gorillas in photos. Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space, which allows an individual to calculate the relationships and similarities between words. However, recent studies have shown that these word embeddings may commonly encode harmful stereotypes, such as the common example that the phrase "computer programmer" is oftentimes more closely related to "man" than it is to "women" in vector space. This could be interpreted as a misrepresentation of computer programming as a profession that is better performed by men, which would be an example of representational harm. == Addressing representational harm == Initiatives to minimise representational harm include advertising for even more inclusive and accurate portrayals of marginalised teams in the media. Scholars and protestors recommend that the method to reducing representational injury depends on raising the selection of voices both behind and before the digital video camera. When marginalized groups are provided the chance to represent themselves, they can check traditional stereotypes and present their experiences additional authentically. Over the last few years, efforts to increase representation of people of color, women, and LGBTQ+ people in conventional media have made some progression. Films such as Selma, routed by Ava DuVernay, and tv series like Pose, developed by Ryan Murphy, have actually been extensively applauded for their nuanced and respectful representations of marginalised communities. These tasks existing complex individualities and stories that move past streamlined stereotypes. Self-representation is one more crucial method to addressing representational harm. By equipping marginalised locations to create their really own tales, media designers can effectively reduce the perpetuation of hazardous stereotypes. This procedure consists of both the manufacturing of media product by participants of these communities and proactively difficult typical media structures that have actually historically omitted them.
Jensen Huang
Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i
Responsible AI Safety and Education Act
The Responsible AI Safety and Education Act (RAISE Act) is a New York State law that imposes transparency, safety, and reporting requirements on developers of large frontier artificial intelligence models. The law was signed by Governor Kathy Hochul on December 19, 2025. It was sponsored by State Senator Andrew Gounardes and Assemblymember Alex Bores. The RAISE Act is the second U.S. state law to regulate frontier AI model developers, following California's Transparency in Frontier Artificial Intelligence Act (TFAIA), which was signed in September 2025. Hochul signed the bill on the condition that the legislature would pass chapter amendments to bring the law closer to the California model. The amending bills (A9449/S8828) were introduced in January 2026; as of February 2026 they remain in committee, though the Governor's office and legal commentators treat the agreed-upon amendments as representing the final form of the law. == Provisions == The following describes the RAISE Act as it is expected to operate after the agreed-upon chapter amendments take effect. The law is expected to take effect on January 1, 2027. === Scope === The law applies to "large frontier developers," defined as companies with annual revenues exceeding $500 million that develop "frontier models," which are foundation models trained using more than 1026 floating-point operations (FLOPs). The version passed by the legislature in June 2025 had instead defined large developers based on having spent over $100 million in aggregate compute costs, and also included a provision prohibiting deployment of frontier models posing "unreasonable risk of critical harm"; both were removed as part of the negotiations between Hochul and the legislature. Accredited colleges and universities engaged in academic research are exempt, as is the state's Empire AI consortium. === Safety and transparency framework === Large frontier developers must write, implement, and publicly publish a "frontier AI framework" describing how they assess and mitigate catastrophic risks, secure unreleased model weights against unauthorized access, use third-party evaluators, govern internal use of frontier models, and respond to safety incidents. The framework must describe these measures "in detail," a requirement that goes beyond the California TFAIA's requirement to describe a developer's "approach." The framework must be reviewed at least annually, and material modifications must be published with justification within 30 days. Before or concurrently with deploying a new or substantially modified frontier model, developers must publish a transparency report including the model's release date, supported languages and output modalities, intended uses, and any restrictions on use. Large frontier developers must additionally include summaries of catastrophic risk assessments and the extent of third-party involvement. === Catastrophic risk and incident reporting === The law defines "catastrophic risk" as a foreseeable and material risk that a frontier model will contribute to the death of or serious injury to more than 50 people, or more than $1 billion in property damage, arising from a frontier model providing expert-level assistance in creating chemical, biological, radiological, or nuclear weapons; engaging in cyberattacks or conduct equivalent to crimes such as murder, assault, or theft without meaningful human oversight; or evading the control of its developer or user. Loss of equity value is explicitly excluded from the definition of property damage. "Critical safety incidents" include unauthorized access to model weights resulting in death or injury, materialization of a catastrophic risk, loss of control of a frontier model causing death or injury, and a model using deceptive techniques to subvert developer controls outside of an evaluation context in a manner that increases catastrophic risk. Frontier developers must report critical safety incidents within 72 hours, or within 24 hours if the incident poses an imminent risk of death or serious physical injury. === Enforcement === The chapter amendments establish a new office within the New York State Department of Financial Services to oversee compliance, receive incident reports, and publish annual reports on AI safety beginning in 2028. Large frontier developers must file disclosure statements with this office and pay pro rata assessments to fund its operations. The New York Attorney General may bring civil actions, with penalties of up to $1 million for a first violation and $3 million for subsequent violations. The version passed by the legislature in June 2025 had set penalties at up to $10 million and $30 million respectively. The law does not create a private right of action. == Legislative history == The bill was introduced in the Assembly on March 5, 2025, by Assemblymember Alex Bores, and in the Senate on March 27, 2025, by Senator Andrew Gounardes. After a series of amendments, the legislature passed the bill in June 2025. Governor Hochul did not immediately sign the bill, using nearly all the time available under New York law before acting; had she not signed by the end of 2025, the bill would have been pocket vetoed. The tech industry lobbied against the bill during this period, and Hochul initially proposed a near-complete rewrite modeled on California's TFAIA. Legislators resisted the extent of the changes, and the two sides ultimately agreed on a version that used the California law as a base but preserved several provisions that went beyond it, including the 72-hour incident reporting timeline and the creation of a dedicated enforcement office. Hochul signed the original bill (S6953-B/A6453-B) on December 19, 2025, with the legislature committing to pass chapter amendments formalizing the agreed changes in the January 2026 session. The amending bills (A9449 in the Assembly, S8828 in the Senate) were introduced on January 6 and January 8, 2026. OpenAI and Anthropic expressed support for the law. Anthropic's head of external affairs Sarah Heck said the two state laws "should inspire Congress to build on them." The super PAC network Leading the Future, backed by Andreessen Horowitz and OpenAI president Greg Brockman, subsequently announced plans to challenge Bores in a future election. == Federal preemption debate == Hochul signed the RAISE Act eight days after President Donald Trump issued an executive order on December 11, 2025, directing the Department of Justice to challenge state AI laws deemed to conflict with a "minimally burdensome" national AI policy. On January 9, 2026, the Department of Justice announced the establishment of an AI Litigation Task Force as called for by the executive order. The executive order also threatened states with loss of certain federal broadband funding if their AI laws were found to be onerous. Legal commentators have noted several potential avenues for federal challenge, including arguments that the law constitutes compelled speech, violates the dormant Commerce Clause by creating a patchwork of state regulations, or is preempted by federal AI policy. == Comparison with California's TFAIA == The RAISE Act was designed to align with California's Transparency in Frontier Artificial Intelligence Act, signed on September 29, 2025. Both laws use the same 1026 FLOP threshold to define frontier models and the same $500 million revenue threshold to define large developers. Both require public safety frameworks, transparency reports, and incident reporting. The RAISE Act's 72-hour incident reporting window is stricter than the TFAIA's 15-day window, though both require faster reporting for incidents posing imminent physical risk (24 hours under the RAISE Act, immediate under the TFAIA). The RAISE Act establishes a dedicated enforcement office within the Department of Financial Services, whereas California routes reports through the Office of Emergency Services. The RAISE Act requires developers to describe their safety measures "in detail" and how they "handle" various risks, whereas the TFAIA requires developers to describe their "approach."
GNU Binutils
The GNU Binary Utilities, or binutils, is a collection of programming tools maintained by the GNU Project for working with executable code including assembly, linking and many other development operations. The tools are originally from Cygnus Solutions. The tools are typically used along with other GNU tools such as GNU Compiler Collection, and the GNU Debugger. == Tools == The tools include: == elfutils == Ulrich Drepper wrote elfutils, to partially replace GNU Binutils, purely for Linux and with support only for ELF and DWARF. It distributes three libraries with it for programmatic access.
Stockfish (chess)
Stockfish is a free and open-source chess engine, available for various desktop and mobile platforms. It can be used in chess software through the Universal Chess Interface. Stockfish has been one of the strongest chess engines in the world for several years. It has won all main events of the Top Chess Engine Championship (TCEC) and the Chess.com Computer Chess Championship (CCC) since 2020 and, as of May 2026, is the strongest CPU chess engine in the world with an estimated Elo rating of 3653 in a time control of 40/15 (15 minutes to make 40 moves), according to CCRL. The Stockfish engine was developed by Tord Romstad, Marco Costalba, and Joona Kiiski, and was derived from Glaurung, an open-source engine by Tord Romstad released in 2004. It is now being developed and maintained by the Stockfish community. Stockfish historically used only a classical hand-crafted function to evaluate board positions, but with the introduction of the efficiently updatable neural network (NNUE) in August 2020, Stockfish 12 adopted a hybrid evaluation system that primarily used the neural network and occasionally relied on the hand-crafted evaluation. In July 2023, Stockfish removed the hand-crafted evaluation and transitioned to a fully neural network-based approach. == Features == Stockfish uses a tree-search algorithm based on alpha–beta search with several hand-designed heuristics. Stockfish represents positions using bitboards. Stockfish supports Chess960, a feature it inherited from Glaurung. Support for Syzygy tablebases, previously available in a fork maintained by Ronald de Man, was integrated into Stockfish in 2014. In 2018, support for the 7-man Syzygy was added, shortly after the tablebase was made available. Stockfish supports an unlimited number of CPU threads in multiprocessor systems, with a maximum transposition table size of 32 TB. Stockfish has been a very popular engine on various platforms. On desktop, it is the default chess engine bundled with the Internet Chess Club interface programs BlitzIn and Dasher. On mobile, it has been bundled with the Stockfish app, SmallFish and Droidfish. Other Stockfish-compatible graphical user interfaces (GUIs) include Fritz, Arena, Stockfish for Mac, and PyChess. Stockfish can be compiled to WebAssembly or JavaScript, allowing it to run in the browser. Both Chess.com and Lichess provide Stockfish in this form in addition to a server-side program. Release versions and development versions are available as C++ source code and as precompiled versions for Microsoft Windows, macOS, Linux 32-bit/64-bit and Android. == History == The program originated from Glaurung, an open-source chess engine created by Tord Romstad and first released in 2004. Four years later, Marco Costalba forked the project, naming it Stockfish because it was "produced in Norway and cooked in Italy" (Romstad is Norwegian and Costalba is Italian). The first version, Stockfish 1.0, was released in November 2008. For a while, new ideas and code changes were transferred between the two programs in both directions, until Romstad decided to discontinue Glaurung in favor of Stockfish, which was the stronger engine at the time. The last Glaurung version (2.2) was released in December 2008. Around 2011, Romstad decided to abandon his involvement with Stockfish in order to spend more time on his new iOS chess app. On 18 June 2014 Marco Costalba announced that he had "decided to step down as Stockfish maintainer" and asked that the community create a fork of the current version and continue its development. An official repository, managed by a volunteer group of core Stockfish developers, was created soon after and currently manages the development of the project. === Fishtest === Since 2013, Stockfish has been developed using a distributed testing framework named Fishtest, where volunteers can donate CPU time for testing improvements to the program. Changes to game-playing code are accepted or rejected based on results of playing of tens of thousands of games on the framework against an older "reference" version of the program, using sequential probability ratio testing. Tests on the framework are verified using the chi-squared test, and only if the results are statistically significant are they deemed reliable and used to revise the software code. After the inception of Fishtest, Stockfish gained 120 Elo points in 12 months, propelling it to the top of all major rating lists. As of May 2026, the framework has used a total of more than 20,100 years of CPU time to play over 10 billion chess games. === NNUE === In June 2020, Stockfish introduced the efficiently updatable neural network (NNUE) approach, based on earlier work by computer shogi programmers. Instead of using manually designed heuristics to evaluate the board, this approach introduced a neural network trained on millions of positions which could be evaluated quickly on CPU. On 2 September 2020, the twelfth version of Stockfish was released, incorporating NNUE, and reportedly winning ten times more game pairs than it loses when matched against version eleven. In July 2023, the classical evaluation was completely removed in favor of the NNUE evaluation. == Competition results == === Top Chess Engine Championship === Stockfish is a TCEC multiple-time champion and the current leader in trophy count. Ever since TCEC restarted in 2013, Stockfish has finished first or second in every season except one. Stockfish finished second in TCEC Season 4 and 5, with scores of 23–25 first against Houdini 3 and later against Komodo 1142 in the Superfinal event. Season 5 was notable for the winning Komodo team as they accepted the award posthumously for the program's creator Don Dailey, who succumbed to an illness during the final stage of the event. In his honor, the version of Stockfish that was released shortly after that season was named "Stockfish DD". On 30 May 2014, Stockfish 170514 (a development version of Stockfish 5 with tablebase support) convincingly won TCEC Season 6, scoring 35.5–28.5 against Komodo 7x in the Superfinal. Stockfish 5 was released the following day. In TCEC Season 7, Stockfish again made the Superfinal, but lost to Komodo with a score of 30.5–33.5. In TCEC Season 8, despite losses on time caused by buggy code, Stockfish nevertheless qualified once more for the Superfinal, but lost 46.5–53.5 to Komodo. In Season 9, Stockfish defeated Houdini 5 with a score of 54.5–45.5. Stockfish finished third during season 10 of TCEC, the only season since 2013 in which Stockfish had failed to qualify for the superfinal. It did not lose a game but was still eliminated because it was unable to score enough wins against lower-rated engines. After this technical elimination, Stockfish went on a long winning streak, winning seasons 11 (59–41 against Houdini 6.03), 12 (60–40 against Komodo 12.1.1), and 13 (55–45 against Komodo 2155.00) convincingly. In Season 14, Stockfish faced a new challenger in Leela Chess Zero, eking out a win by one point (50.5–49.5). Its winning streak was finally ended in Season 15, when Leela qualified again and won 53.5–46.5, but Stockfish promptly won Season 16, defeating AllieStein 54.5–45.5, after Leela failed to qualify for the Superfinal. In Season 17, Stockfish faced Leela again in the superfinal, losing 52.5–47.5. However, Stockfish has won every Superfinal since: beating Leela 53.5–46.5 in Season 18, 54.5–45.5 in Season 19, 53–47 in Season 20, and 56–44 in Season 21. In Season 22, Komodo Dragon beat out Leela to qualify for the Superfinal, losing to Stockfish by a large margin 59.5–40.5. Stockfish did not lose an opening pair in this match. Leela made the Superfinal in Seasons 23 and 24, but was crushed by Stockfish both times (58.5–41.5 and 58–42). In Season 25, Stockfish once again defeated Leela, but this time by a narrower margin of 52–48. Stockfish also took part in the TCEC cup, winning the first edition, but was surprisingly upset by Houdini in the semifinals of the second edition. Stockfish recovered to beat Komodo in the third-place playoff. In the third edition, Stockfish made it to the finals, but was defeated by Leela Chess Zero after blundering in a 7-man endgame tablebase draw. It turned this result around in the fourth edition, defeating Leela in the final 4.5–3.5. In TCEC Cup 6, Stockfish finished third after losing to AllieStein in the semifinals, the first time it had failed to make the finals. Since then, Stockfish has consistently won the tournament, with the exception of the 11th edition which Leela won 8.5–7.5. === Chess.com Computer Chess Championship === Ever since Chess.com hosted its first Chess.com Computer Chess Championship in 2018, Stockfish has been the most successful engine. It dominated the earlier championships, winning six consecutive titles before finishing second in CCC7. Since then, its dominance has come under threat from the neural-network engines Leelenstein and Leela Chess Zero, but it has continued to perform w