A social media newsroom is a company resource, set up to increase the functionality and usability of the traditional online newsroom. Social media newsrooms (SMNs) are intended to encourage dialogue and information sharing. Unlike online newsrooms, content is accessible to more than just journalists, but to all those with whom the company engages such as bloggers, their prospects, customers, business partners and investors. It gives these stakeholders access to news, public relations announcements, images, audio, video and other multimedia files. In addition to posting press releases and corporate news, companies can integrate other social content from sites such as YouTube, Flickr and Slideshow as well as streams from corporate Twitter accounts. Traditional tools for journalists such as corporate fast facts, leadership information, a multimedia library, financial information, awards and other recent media coverage are also included in an SMN. Examples of companies effectively using social media newsrooms include Opel Group, Pressat, First Direct, MyNewsdesk, Scania and Newport Beach.
Voyages: The Trans-Atlantic Slave Trade Database
Voyages: The Trans-Atlantic Slave Trade Database is a database hosted at Rice University that aims to present all documentary material pertaining to the transatlantic slave trade. It is a sister project to African Origins. The database breaks down the kingdoms and countries that engaged in the Atlantic trade. By 2008, the project had gathered data on nearly 35,000 transatlantic slave voyages from 1501 to 1867. For each voyage they sought to establish dates, owners, vessels, captains, African visits, American destinations, numbers of slaves embarked, and numbers landed. They have been able to find much of this material for an estimated 80 percent of the entire transatlantic African slave trade. With corrections for missing voyages, the Project has estimated the entire size of the transatlantic slave trade with more comprehension, precision, and accuracy than before. They reckon that in 366 years, slaving vessels embarked about 12.5 million captives in Africa, and landed 10.7 million in the New World. A horrific discovery is a careful estimate that the Middle Passage took a toll of more than 1.8 million African lives. In this quantitative database, the numbers are enslaved people.
The Sword in the Stoned
"The Sword in the Stoned" is the fifth episode of the second season of the American fantasy comedy television series Ted. Written by Julius Sharpe, and directed by Seth MacFarlane, it premiered on the American streaming service Peacock, along with the rest of season two, on March 5, 2026. The series acts as a precursor to the Ted film franchise, showcasing the childhood lives of the protagonists. The series, set in 1994, focuses on John Bennett (Max Burkholder), the series' primary protagonist, an awkward high-school aged boy; along with Ted (MacFarlane), the series' titular anthropomorphic teddy bear. The two live with John's family, Susan (Alanna Ubach), his mild mannered mother, and Matty (Scott Grimes), his conservative father. Also residing with the family is Blaire (Giorgia Whigham), his radically liberal cousin whom often clashes with Matty. In the episode, Ted and John join the school play so they can have more extracurricular activities for their college applications, but the latter grows a connection with the school's popular teenager, Erin (Francesca Xuereb). Concurrently, Susan and Matty get a job at Dunkin' Donuts to help with their financial troubles, and Matty is given an opportunity to tell off Bill Clinton. Burkholder wore prop armor during the episode's play scenes. Bill Clinton’s appearance in the episode was portrayed by MacFarlane. After conventional makeup and visual techniques failed to convincingly resemble Clinton, the production used artificial intelligence to digitally replace MacFarlane's face with Clinton's likeness. Upon release, the episode received generally positive reviews from critics, though the use of AI in the Clinton scene was polarizing among audiences and reviewers. == Plot == John tells Ted that he is the last single guy left at their school, to which Ted points out the popular, single cheerleader, Erin, but John dismisses this. At home, Blaire tells John that he needs extracurricular activities to get into college, while Susan and Matty discuss their financial troubles, especially regarding John's college tuition. Looking over their options, they decide to audition for a school production of the play Camelot. Matty takes a job at Dunkin' Donuts, despite being told that nobody will give him a tip, and having to wear an incorrect name tag. Waiting for their auditions, John and Ted watch several poor auditions for the play before seeing Erin's, who delivers a flawless performance; John and Ted do less serious auditions, getting cast as knights, while Erin gets the role of Guinevere. Matty complains about his low salary, and Susan decides to get a job at Dunkin' Donuts beside him to help earn more income. Erin clashes with Lancelot's actor while rehearsing, and John compliments her performance, which she ignores, but, seeing Ted and John give good performances in a repetition exercise, she becomes interested in him, particularly since he treats her better than her stage-partner. Matty and Susan watch an employee training video, explaining how they should treat customers politely, not affecting Matty's nihilistic attitude. The manager announces that Bill Clinton is visiting their Dunkin' Donuts for publicity, and Matty sees this as a chance to tell Bill off. John and Erin practice lines, as she reveals the show is being taped so it can be sent to Emerson College in hopes of her getting in; Erin asks John to go out with her after the show. At dinner, Matty enthusiastically reveals what he plans to tell Bill, as John becomes stressed about the play when Susan tells there will be a large audience. Bill comes to the Dunkin' Donuts, and, seeing Matty is nervously insulting him, stages a private meeting with him, where Bill yells at Matty, calling him a loser before posing for a picture with Matty and subsequently throwing the cold coffee onto him. To ease the pressure, Ted and John take edibles from Blaire, but learn at the show that they contained mushrooms, causing them to stress further. On stage, Ted and John yell nervously that they're on drugs as the latter urinates in his costume, causing Erin to angrily storm off. == Production == "The Sword in the Stoned" was directed by series creator and lead Seth MacFarlane, and written by Julius Sharpe in his third and final writing credit for the series. When Ted and John are doing repetition exercises, they tackle each other to the ground, which required a stuntman named Ashton to play the role of Ted, according to Max Burkholder, who portrays John. Burkholder also recalled that, when Ted was choking John in the scene, he kept making a noise during the choking, which made Bill, the cameraman, laugh, despite being a "stone face" that never laughs, noting that seeing him be amused by the noise he was making assured Burkholder that what he was doing was "hilarious". Burkholder found the filming of the play scenes "weird", as he was put in fake armor with a hose inside his suit—which was filled with water mixed with yellow food coloring—that was made to create the urine stream that comes out of John's armor in the episode; he also noted that it took around 45 minutes to put on and take off the armor. He revealed that he himself had to urinate during the filming, as doing a scene about a character having to do so "really [broke] my brain", with the fact that it took 45 minutes to get the suit off adding to the frustration. Jennifer Ashley Connell, who worked for wardrobe, had to repeatedly go to Burkholder quickly between takes to dry off his pants with two hair dryers to make it look like the fake urine hadn't already streamed down his pants, so they could get as many shots of it as possible. Francesca Xuereb guest stars in the episode as Erin, the cheerleader who stars in the play. Incumbent president Bill Clinton was portrayed by MacFarlane, with artificial intelligence (AI) being used to digitally make MacFarlane's face look like Clinton's during post-production. Before settling on AI, the crew tried to use traditional computer-generated imagery and prosthetics, which made him look "terrifying", resulting in them deciding that AI would give them a more accurate look. One of the original technologies considered was one where, after scanning MacFarlane, a mesh of his head was created, and they had to use computer graphics to replace MacFarlane's face with Clinton's. An issue was faced, however, when they found the archival footage used as reference from the Clinton Library—an official Presidential Library containing information related to Clinton—to be extremely low-quality, making it hard to properly emulate his face, since only still images were of acceptable quality, and there weren't references of his moving face to work off of. A forensic artist was hired to help with this, and they created a 3D model of Clinton's head in ZBrush, based off of his presidential portrait. The model head worked for still frames, but movement was still difficult to do realistically, due to it being made for a "single-point perspective", which made details like the cheekbones or other minor issues more noticeable when using it for the scene. Since this did not work, AI was ultimately chosen through the studio Deep Voodoo, which used large language models to teach the tool how to correctly replicate Clinton's appearance. Defending the episode's use of AI, MacFarlane noted that the crew did not want people to focus on the tool being used, trying to utilize it in a way that wouldn't distract from the humor and narrative. Like the rest of the series, the episode was shot using ViewScreen; MacFarlane was able to act live with the cast as Ted due to ViewScreen, a technology that allows the production crew to visualize what Ted will look like in each scene in real time. == Release and reception == "The Sword in the Stoned" was first released on March 5, 2026, on the American streaming service Peacock, along with the rest of the second season. Nate Richards of Collider highlighted the Dunkin' Donuts subplot as an example of Scott Grimes delivering a "lot of laughs" through his performance as Matty. Dustin Rowles of Pajiba called "The Sword in the Stoned" one of the season's many episodes he'd recommend, particularly for the scenes of Ted and John being high on mushrooms during the play. Oppositely, Nick Valdez of ComicBook.com ranked the episode as the worst of the second season, criticizing it for not having a "huge impact" on the Bennett family dynamic like other episodes of the season do, and Susan and Matty's side story as the main reason he felt it was "[kept] from being great". Valdez noted the episode for likely being an advertisement for Dunkin' Donuts, calling the plot's ending scene involving Clinton the reason "it just all sticks out like a sore thumb". === Response to AI usage === The episode's use of AI for MacFarlane's portrayal of Clinton proved controversial, mainly on social media, where audiences asserted that the crew should have gotten an actor that resembl
Distributed artificial intelligence
Distributed Artificial Intelligence (DAI) (also called Decentralized Artificial Intelligence) is a melding of artificial intelligence with distributed computing. From artificial intelligence comes the theory and technology for constructing or analyzing an intelligent system. But where artificial intelligence uses psychology as a source of ideas, inspiration, and metaphor, DAI uses sociology, economics, and management science for inspiration. Where the focus of artificial intelligence is on the individual, the focus of DAI is on the group. Distributed computing provides the computational substrate on which this group focus can occur. Using techniques from artificial intelligence, communication theory, control theory, and interaction theory, it produces a cooperative solution to problems by a decentralized group of computational entities (agents). DAI is closely related to and a predecessor of the field of multi-agent systems. They are distinguished generally by multi-agent systems being open, where the entities might arise from different interests and have individual goals, and distributed artificial-intelligence systems, where the entities have common goals. There are numerous applications and tools. == Definition == Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment. DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems, which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system. == Development == In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents. As a scientific discipline, it progressed through a series of workshops in the USA (International Workshop on Distributed Artificial Intelligence, held in 13 editions from 1978 - 1994), Europe (Workshop on Modelling Autonomous Agents in a Multi-Agent World https://link.springer.com/conference/maamaw), and Asia (Multi-Agent and Cooperative Computation Workshop (MACC) https://sites.google.com/view/sig-macc/macc-workshop?authuser=0). Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence, or by competition. DAI is categorized into multi-agent systems and distributed problem solving. In multi-agent systems the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized. == Goals == The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires: A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled Coordination of the actions and communication of the nodes Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following: Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that multiprocessor systems and clusters of computers can be used to speed up calculation. Distributed problem solving (DPS): the concept of agent, autonomous entities that can communicate with each other, was developed to serve as an abstraction for developing DPS systems. See below for further details. Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level, as it is in many social simulation scenarios. == Approaches == Two types of DAI has emerged: In Multi-agent systems agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive their environment, and communicate with other agents. An agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques. In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions. DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence. === Challenges === The challenges in Distributed AI are: How to carry out communication and interaction of agents and which communication language or protocols should be used. How to ensure the coherency of agents. How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation. == Applications and tools == Areas where DAI have been applied are: Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network Routing, e.g. model vehicle flow in transport networks Scheduling, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency Search engines, e.g. in LLM federated search like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation Multi-Agent systems, e.g. artificial life, the study of simulated life Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System DAI integration in tools has included: ECStar is a distributed rule-based learning system. == Agents == === Systems: Agents and multi-agents === Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving. Notion of Multi-Agents: Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve. === Software agents === The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language. A first classification that is useful is to divide agents into: reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output. deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation. Well-recognized agent architectures that describe how an agent is internally structured are: ASMO (emergence of distributed modules) BDI (Believe Desire Intention, a general architecture that describes how plans are made) InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer) PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior). Soar (a rule-based approach)
Mata v. Avianca, Inc.
Mata v. Avianca, Inc. was a U.S. District Court for the Southern District of New York case in which the Court dismissed a personal injury case against the airline Avianca and issued a $5,000 fine to the plaintiffs' lawyers who had submitted fake precedents generated by ChatGPT in their legal briefs. == Background == In February 2022, Roberto Mata filed a personal injury lawsuit in the U.S. District Court for the Southern District of New York against Avianca, alleging that he was injured when a metal serving cart struck his knee during an international flight. The plaintiff's lawyers used ChatGPT to generate a legal motion, which contained numerous fake legal cases involving fictitious airlines with fabricated quotations and internal citations. Avianca's lawyers notified the Court that they had been "unable to locate" a few legal cases cited in the legal motion. The Court could not locate the cases either and ordered the plaintiff's lawyers to provide copies of the cited legal cases. Mata's lawyers provided copies of documents purportedly containing all but one of the legal cases, after ChatGPT assured that the cases "indeed exist" and "can be found in reputable legal databases such as LexisNexis and Westlaw." == Opinion == In May 2023, Judge P. Kevin Castel dismissed the personal injury case against Avianca and ordered the plaintiff's attorneys to pay a $5,000 fine. Judge Castel noted numerous inconsistencies in the opinion summaries, describing one of the legal analyses as "gibberish." Judge Castel held that Mata's lawyers had acted with "subjective bad faith" sufficient for sanctions under Federal Rule of Civil Procedure Rule 11. == Impact == In July 2024, the American Bar Association issued its first formal ethics opinion on the responsibilities of lawyers using generative AI (GAI). The 15-page opinion outlines how the Rules of Professional Conduct apply to the use of GAI in the practice of law. Experts caution that lawyers cannot reasonably rely on the accuracy, completeness, or validity of content generated by GAI tools. Due to the continued usage of GAI in the practice of law, Mata has been described as a landmark case by legal professionals, as it is frequently cited by courts in cases where usage of GAI during the course of proceedings leads to the creation and citation of nonexistent caselaw.
Display list
A display list, also called a command list in Direct3D 12 and a command buffer in Vulkan, is a series of graphics commands or instructions that are run when the list is executed. Systems that make use of display list functionality are called retained mode systems, while systems that do not are as opposed to immediate mode systems. In OpenGL, display lists are useful to redraw the same geometry or apply a set of state changes multiple times. This benefit is also used with Direct3D 12's bundle command lists. In Direct3D 12 and Vulkan, display lists are regularly used for per-frame recording and execution. == Origins in vector displays == The vector monitors or calligraphic displays of the 1960s and 1970s used electron beam deflection to draw line segments, points, and sometimes curves directly on a CRT screen. Because the image would immediately fade, it needed to be redrawn many times a second (storage tube CRTs retained the image until blanked, but they were unsuitable for interactive graphics). To refresh the display, a dedicated CPU called a Display Processor or Display Processing Unit (DPU) was used, which had a memory buffer for a "display list", "display file", or "display program" containing line segment coordinates and other information. Advanced Display Processors also supported control flow instructions, which were useful for drawing repetitive graphics such as text, and some could perform coordinate transformations such as 3D projection. == Home computer display list functionality == One of the earliest systems with a true display list was the Atari 8-bit computers. The display list (actually called so in Atari terminology) is a series of instructions for ANTIC, the video co-processor used in these machines. This program, stored in the computer's memory and executed by ANTIC in real-time, can specify blank lines, any of six text modes and eight graphics modes, which sections of the screen can be horizontally or vertically fine-scrolled, and trigger Display List Interrupts (called raster interrupts or HBI on other systems). The Amstrad PCW family contains a Display List function called the 'Roller RAM'. This is a 512-byte RAM area consisting of 256 16-bit pointers in RAM, one for each line of the 720 × 256 pixel display. Each pointer identifies the location of 90 bytes of monochrome pixels that hold the line's 720 pixel states. The 90 bytes of 8 pixel states are spaced at 8-byte intervals, so there are 7 unused bytes between each byte of pixel data. This suits how the text-orientated PCW constructs a typical screen buffer in RAM, where the first character's 8 rows are stored in the first 8 bytes, the second character's rows in the next 8 bytes, and so on. The Roller RAM was implemented to speed up display scrolling as it would have been unacceptably slow for its 3.4 MHz Z80 to move up the 23 KB display buffer 'by hand' i.e. in software. The Roller RAM starting entry used at the beginning of a screen refresh is controlled by a Z80-writable I/O register. Therefore, the screen can be scrolled simply by changing this I/O register. Another system using a Display List-like feature in hardware is the Amiga, which, not coincidentally, was also designed by some of the same people who developed the custom hardware for the Atari 8-bit computers. Once directed to produce a display mode, it would continue to do so automatically for every following scan line. The computer also included a dedicated co-processor, called "Copper", which ran a simple program or 'Copper List' intended for modifying hardware registers in sync with the display. The Copper List instructions could direct the Copper to wait for the display to reach a specific position on the screen, and then change the contents of hardware registers. In effect, it was a processor dedicated to servicing raster interrupts. The Copper was used by Workbench to mix multiple display modes (multiple resolutions and color palettes on the monitor at the same time), and by numerous programs to create rainbow and gradient effects on the screen. The Amiga Copper was also capable of reconfiguring the sprite engine mid-frame, with only one scanline of delay. This allowed the Amiga to draw more than its 8 hardware sprites, so long as the additional sprites did not share scanlines (or the one scanline gap) with more than 7 other sprites. i.e., so long as at least one sprite had finished drawing, another sprite could be added below it on the screen. Additionally, the later 32-bit AGA chipset allowed the drawing of bigger sprites (more pixels per row) while retaining the same multiplexing. The Amiga also had dedicated block-shifter ("blitter") hardware, which could draw larger objects into a framebuffer. This was often used in place of, or in addition to, sprites. In more primitive systems, the results of a display list can be simulated, though at the cost of CPU-intensive writes to certain display modes, color control, or other visual effect registers in the video device, rather than a series of rendering commands executed by the device. Thus, one must create the displayed image using some other rendering process, either before or while the CPU-driven display generation executes. In many cases, the image is also modified or re-rendered between frames. The image is then displayed in various ways, depending on the exact way in which the CPU-driven display code is implemented. Examples of the results possible on these older machines requiring CPU-driven video include effects such as Commodore 64/128's FLI mode, or Rainbow Processing on the ZX Spectrum. == Usage in OpenGL == To delimit a display list, the glNewList and glEndList functions are used, and to execute the list, the glCallList function is used. Almost all rendering commands that occur between the function calls are stored in the display list. Commands that affect the client state are not stored in display lists. Display lists are named with an integer value, and creating a display list with the same name as one already created overrides the first. The glNewList function expects two arguments: an integer representing the name of the list, and an enumeration for the compilation mode. The two modes include GL_COMPILE_AND_EXECUTE, which compiles and immediately executes, and GL_COMPILE, which only compiles the list. Display lists enable the use of the retained mode rendering pattern, which is a system in which graphics commands are recorded (retained) to execute in succession at a later time. This is contrary to immediate mode, where graphics commands are immediately executed on client calls. == Usage in Direct3D 12 == Command lists are created using the ID3D12Device::CreateCommandList function. Command lists may be created in several types: direct, bundle, compute, copy, video decode, video process, and video encoding. Direct command lists specify that a command list the GPU can execute, and doesn't inherit any GPU state. Bundles, are best used for storing and executing small sets of commands any number of times. This is used differently than regular command lists, where commands stored in a command list are typically executed only once. Compute command lists are used for general computations, with a common use being calculating mipmaps. A copy command list is strictly for copying and the video decode and video process command lists are for video decoding and processing respectively. Upon creation, command lists are in the recording state. Command lists may be re-used by calling the ID3D12GraphicsCommandList::Reset function. After recording commands, the command list must be transitioned out of the recording state by calling ID3D12GraphicsCommandList::Close. The command list is then executed by calling ID3D12CommandQueue::ExecuteCommandLists.
Vibe coding
Vibe coding is a software development practice assisted by artificial intelligence (AI) where the software developer describes a project or task in a prompt to a large language model (LLM), which generates source code automatically. Vibe coding may involve accepting AI-generated code without thorough review of the output, instead relying on results and follow-up prompts to guide changes. The term was coined in February 2025 by computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla. Merriam-Webster listed the term in March 2025 as a "slang & trending" expression. It was named the Collins English Dictionary Word of the Year for 2025. Advocates of vibe coding say that it allows even amateur programmers to produce software without the extensive training and skills required for software engineering. Critics point out a lack of accountability, maintainability, and the increased risk of introducing security vulnerabilities in the resulting software. == Definition == The concept refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing in a formal programming language. Karpathy described it as a form of coding where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists". When vibe coding, the programmer guides, tests, and gives feedback about the AI-generated source code, rather than manually writing code. The concept of vibe coding elaborates on Karpathy's claim from 2023 that "the hottest new programming language is English", meaning that the capabilities of LLMs were such that humans would no longer need to learn specific programming languages to command computers. Some commentators argue that a key to the definition is a lack of knowledge about the code, and that thorough review and testing is incompatible with the definition of vibe coding. Programmer Simon Willison said: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding in my book—that's using an LLM as a typing assistant." == Reception and use == In February 2025, New York Times journalist Kevin Roose, who is not a professional coder, experimented with vibe coding to create several small-scale applications. He described these as "software for one" due to the ability to personalize the software. However, Roose also stated that the results are often limited and prone to errors. In one case, the AI-generated code fabricated fake reviews for an e-commerce site. In response to Roose, cognitive scientist Gary Marcus said that the algorithm that generated Roose's LunchBox Buddy app had presumably been trained on existing code for similar tasks. Marcus said that Roose's enthusiasm stemmed from reproduction, not originality. In March 2025, Y Combinator reported that 25% of startup companies in its Winter 2025 batch had codebases that were 95% AI-generated, reflecting a shift toward AI-assisted development within newer startups. The question asked was about AI-generated code in general, and not specifically about vibed code. Inspired by "vibe coding", The Economist suggested the term "vibe valuation" to describe the very large valuations of AI startups by venture capital firms that ignore accepted metrics such as annual recurring revenue. In June 2025, Andrew Ng took issue with the term, saying that it misleads people into assuming that software engineers just "go with the vibes" when using AI tools to create applications. In July 2025, The Wall Street Journal reported that vibe coding was being adopted by professional software engineers for commercial use cases. In July 2025, SaaStr founder documented his negative experiences with vibe coding: Replit's AI agent deleted a database despite explicit instructions not to make any changes. In September 2025, Fast Company reported that the "vibe coding hangover" is upon us, with senior software engineers citing "development hell" when working with AI-generated code. It was reported in January 2026 that Linus Torvalds had made use of Google Antigravity to vibe code a tool component of his AudioNoise random digital audio effects generator. Torvalds explained in the project's README file that "the Python visualizer tool has been basically written by vibe-coding". == Criticism == === Quality of code and security issues === Vibe coding has raised concerns about understanding and accountability. Developers may use AI-generated code without comprehending its functionality, leading to undetected bugs, errors, or security vulnerabilities. While this approach may be suitable for prototyping or "throwaway weekend projects" as Karpathy originally envisioned, it is considered by some experts to pose risks in professional settings, where a deep understanding of the code is crucial for debugging, maintenance, and security. Ars Technica cites Simon Willison, who stated: "Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial." In May 2025, Lovable, a Swedish vibe coding app, was reported to have security vulnerabilities in the code it generated, with 170 out of 1,645 Lovable-created web applications having an issue that would allow personal information to be accessed by anyone. In October 2025 Veracode released a study that showed that over the last 3 years LLMs had become dramatically better at generating functional code, but that the security of generated code had generally not improved. Moreover, larger models were not better than small ones at generating secure code. There was a small increase in security from the OpenAI reasoning models, but not in other reasoning models, and this increase was nothing like the improvement in generated functionality. In December 2025, computer security researcher Etizaz Mohsin discovered a security flaw in the Orchids vibe coding platform, which he demonstrated to a BBC News reporter in February 2026. A December 2025 analysis by CodeRabbit of 470 open-source GitHub pull requests found that code that was co-authored by generative AI contained approximately 1.7 times more "major" issues compared to human-written code. The study revealed that AI co-authored code showed elevated rates of logic errors, including incorrect dependencies, flawed control flow, misconfigurations (75% more common), and security vulnerabilities (2.74x higher). Additionally, they also reported high code readability issues, including formatting errors and naming inconsistencies. === Code maintainability and technical debt === Vibe coding has the potential of making code harder to maintain in the longer term, leading to technical debt. In early 2025, GitClear published the results of a longitudinal analysis of 211 million lines of code changes from 2020 to 2024. They found that the volume of code refactoring dropped from 25% of changed lines in 2021 to under 10% by 2024, code duplication increased approximately four times in volume, copy-pasted code exceeded moved code for the first time in two decades, and code churn (prematurely merged code getting rewritten shortly after merging) nearly doubled. === Task complexity and developer productivity === Generative AI is highly capable of handling simple tasks like basic algorithms. However, such systems struggle with more novel, complex coding problems like projects involving multiple files, poorly documented libraries, or safety-critical code. In July 2025, METR, an organization that evaluates frontier models, ran a randomized controlled trial to understand developer productivity involving generative AI programming tools available in early 2025. They found that experienced open-source developers were 19% slower when using AI coding tools, despite predicting they would be 24% faster and still believing afterward they had been 20% faster. === Challenges with debugging === LLMs generate code dynamically, and the structure of such code may be subject to variation. In addition, since the developer did not write the code, the developer may struggle to understand its syntax and concepts. === Impact on open-source software === In January 2026, a paper authored by experts from several universities titled "Vibe Coding Kills Open Source" argued that vibe coding has negative impact on the open-source software ecosystem. The authors say that increased vibe coding reduces user engagement with open-source maintainers, which has hidden costs for said maintainers. Speaking with The Register about their paper, the authors argued:"Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns," the authors argue. "When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers e