Kernel-phases are observable quantities used in high resolution astronomical imaging used for superresolution image creation. It can be seen as a generalization of closure phases for redundant arrays. For this reason, when the wavefront quality requirement are met, it is an alternative to aperture masking interferometry that can be executed without a mask while retaining phase error rejection properties. The observables are computed through linear algebra from the Fourier transform of direct images. They can then be used for statistical testing, model fitting, or image reconstruction. == Prerequisites == In order to extract kernel-phases from an image, some requirements must be met: Images are nyquist-sampled (at least 2 pixels per resolution element ( λ D {\displaystyle {\frac {\lambda }{D}}} )) Images are taken in near monochromatic light Exposure time is shorter than the timescale of aberrations Strehl ratio is high (good adaptive optics) Linearity of the pixel response (i.e. no saturation) Deviations from these requirements are known to be acceptable, but lead to observational bias that should be corrected by the observation of calibrators. == Definition == The method relies on a discrete model of the instrument's pupil plane and the corresponding list of baselines to provide corresponding vectors φ {\displaystyle \varphi } of pupil plane errors and Φ {\displaystyle \Phi } of image plane Fourier Phases. When the wavefront error in the pupil plane is small enough (i.e. when the Strehl ratio of the imaging system is sufficiently high), the complex amplitude associated to the instrumental phase in one point of the pupil φ k {\displaystyle \varphi _{k}} , can be approximated by e i φ k ≈ 1 + i φ k {\displaystyle e^{i\varphi _{k}}\approx 1+{\mathit {i}}\varphi _{k}} . This permits the expression of the pupil-plane phase aberrations φ {\displaystyle \varphi } to the image plane Fourier phase as a linear transformation described by the matrix A {\displaystyle A} : Φ = Φ 0 + A ⋅ φ {\displaystyle \Phi =\Phi _{0}+A\cdot \varphi } Where Φ 0 {\displaystyle \Phi _{0}} is the theoretical Fourier phase vector of the object. In this formalism, singular value decomposition can be used to find a matrix K {\displaystyle K} satisfying K ⋅ A = 0 {\displaystyle K\cdot A=0} . The rows of K {\displaystyle K} constitute a basis of the kernel of A T {\displaystyle A^{T}} . K ⋅ Φ = K ⋅ Φ 0 + K ⋅ A ⋅ φ {\displaystyle K\cdot \Phi =K\cdot \Phi _{0}+{\cancel {K\cdot A\cdot \varphi }}} The vector K . Φ {\displaystyle K.\Phi } is called the kernel-phase vector of observables. This equation can be used for model-fitting as it represents the interpretation of a sub-space of the Fourier phase that is immune to the instrumental phase errors to the first order. == Applications == The technique was first used in the re-analysis of archival images from the Hubble Space Telescope where it enabled the discovery of a number of brown dwarf in close binary systems. The technique is used as an alternative to aperture masking interferometry, especially for fainter stars because it does not require the use of masks that typically block 90% of the light, and therefore allows higher throughput. It is also considered to be an alternative to coronagraphy for direct detection of exoplanets at very small separations (below 2 λ D {\displaystyle 2{\frac {\lambda }{D}}} ) where coronagraphs are limited by the wavefront errors of adaptive optics. The same framework can be used for wavefront sensing. In the case of an asymmetric aperture, a pseudo-inverse of A {\displaystyle A} can be used to reconstruct the wavefront errors directly from the image. A Python library called xara is available on GitHub and maintained by Frantz Martinache to facilitate the extraction and interpretation of kernel-phases. The KERNEL project, has received funding from the European Research Council to explore the potential of these observables for a number of use-cases, including direct detection of exoplanets, image reconstruction, and image plane wavefront sensing for adaptive optics.
AI agent
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents that can pursue goals, use tools, and take actions with varying degrees of autonomy. In practice, they usually operate within human-defined objectives, constraints, and available tools. == Overview == AI agents possess several key attributes, including goal-directed behavior, natural language interfaces, the capacity to use external tools, and the ability to perform multi-step tasks. Their control flow is frequently driven by large language models (LLMs). Agent systems may also include memory components, planning logic, tool interfaces, and orchestration software for coordinating agent components. AI agents do not have a standard definition. NIST describes agentic AI as an emerging area requiring standards for secure operation, interoperability, and reliable interaction with external systems. A common application of AI agents is task automation: for example, booking travel plans based on a user's prompted request. Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents. Several protocols have been proposed for standardizing inter-agent communication, with examples including the Model Context Protocol, Gibberlink, and many others. Some of these protocols are also used for connecting agents to external applications. In December 2025, Linux Foundation announced the formation of the Agentic AI Foundation (AAIF), with the goal of ensuring agentic AI evolves transparently and collaboratively. == History == AI agents have been traced back to research from the 1990s, with Harvard professor Milind Tambe noting that the definition of an AI agent was not clear at the time. Researcher Andrew Ng has been credited with spreading the term "agentic" to a wider audience in 2024. == Training and testing == Researchers have attempted to build world models and reinforcement learning environments to train or evaluate AI agents. For example, video games such as Minecraft and No Man's Sky as well as replicas of company websites, have also been used for training such agents. == Autonomous capabilities == The Financial Times compared the autonomy of AI agents to the SAE classification of self-driving cars, likening most applications to level 2 or level 3, with some achieving level 4 in highly specialized circumstances, and level 5 being theoretical. == Cognitive architecture == The following are some internal design options for reasoning within an agent: Retrieval-augmented generation ReAct (Reason + Act) pattern is an iterative process in which an AI agent alternates between reasoning and taking actions, receives observations from the environment or external tools, and integrates these observations into subsequent reasoning steps. Reflexion, which uses an LLM to create feedback on the agent's plan of action and stores that feedback in a memory cache. A tool/agent registry, for organizing software functions or other agents that the agent can use. One-shot model querying, which queries the model once to create the plan of action. === Reference architecture === Ken Huang proposed an AI agent reference architecture, which consists of seven interconnected layers, with each layer building on the functionality of the layers beneath it: Layer 1: Foundation models - provide the core AI engines to power agent capabilities. Layer 2: Data operations - manage the complex data infrastructure required for AI agent operations, including Vector database, data loaders, RAG. Layer 3: Agent frameworks - sophisticated software and tools that simplify the development and management of the AI agents. Layer 4: Deployment and infrastructure - provide the robust technical foundation for running AI agents. Layer 5: Evaluation and observability - focus on assessing the safety and performance of AI agents. Layer 6: Security and compliance - a crucial protective framework ensuring AI agents operate safely, securely, and conform to regulatory boundaries. At this layer security and compliance features embedded into all the AI agent stack layers are integrated together. Layer 7: Agent ecosystem - represents the AI agents' interface with real-world applications and users. == Orchestration patterns == To execute complex tasks, autonomous agents are often integrated with other agents or specialized tools. These configurations, known as orchestration patterns or workflows, include the following: Prompt chaining: A sequence where the output of one step serves as the input for the next. Routing: The classification of an input to direct it to a specialized downstream task or tool. Parallelization: The simultaneous execution of multiple tasks. Sequential processing: A fixed, linear progression of tasks through a predefined pipeline. Planner-critic: An iterative pattern where one agent generates a proposal and another evaluates it to provide feedback for refinement. == Multimodal AI agents == In addition to large language models (LLMs), vision-language models (VLMs) and multimodal foundation models can be used as the basis for agents. In September 2024, Allen Institute for AI released an open-source vision-language model. Nvidia released a framework for developers to use VLMs, LLMs and retrieval-augmented generation for building AI agents that can analyze images and videos, including video search and video summarization. Microsoft released a multimodal agent model – trained on images, video, software user interface interactions, and robotics data – that the company claimed can manipulate software and robots. == Applications == As of April 2025, per the Associated Press, there are few real-world applications of AI agents. As of June 2025, per Fortune, many companies are primarily experimenting with AI agents. The Information divided AI agents into seven archetypes: business-task agents, for acting within enterprise software; conversational agents, which act as chatbots for customer support; research agents, for querying and analyzing information (such as OpenAI Deep Research); analytics agents, for analyzing data to create reports; software developer or coding agents (such as Cursor); domain-specific agents, which include specific subject matter knowledge; and web browser agents (such as OpenAI Operator). By mid-2025, AI agents have been used in video game development, gambling (including sports betting), cryptocurrency wallets (including cryptocurrency trading and meme coins) and social media. In August 2025, New York Magazine described software development as the most definitive use case of AI agents. Likewise, by October 2025, noting a decline in expectations, The Information noted AI coding agents and customer support as the primary use cases by businesses. In November 2025, The Wall Street Journal reported that few companies that deployed AI agents have received a return on investment. === Applications in government === Several government bodies in the United States and United Kingdom have deployed or announced the deployment of agents, at the local and national level. The city of Kyle, Texas deployed an AI agent from Salesforce in March 2025 for 311 customer service. In November 2025, the Internal Revenue Service stated that it would use Agentforce, AI agents from Salesforce, for the Office of Chief Counsel, Taxpayer Advocate Services and the Office of Appeals. That same month, Staffordshire Police announced that they would trial Agentforce agents for handling non-emergency 101 calls in the United Kingdom starting in 2026. In December 2025, the Department of Neighborhoods in Detroit, Michigan, in partnership with a local business, deployed a pilot project in two Detroit districts for an AI agent to be used for customer service calls. In February 2025, Thomas Shedd, the director of the Technology Transformation Services, proposed using AI coding agents across the United States federal government. A recruiter for the Department of Government Efficiency proposed in April 2025 to use AI agents to automate the work of about 70,000 United States federal government employees, as part of a startup with funding from OpenAI and a partnership agreement with Palantir. This proposal was criticized by experts for its impracticality, if not impossibility, and the lack of corresponding widespread adoption by businesses. In December 2025, the Food and Drug Administration announced that it would offer "agentic AI capabilities" to its staff for "meeting management, pre-market reviews, review validation, post-market surveillance, inspections and compliance and administrative functions." That same month, the United States Department of Defense launched GenAI.mil, an internal platform for American military personnel to use generative AI-based applications based on Google Gemini, including "intelligent agentic workflows". Defense Secretary Pete Hegseth listed applications such as "[conducting] deep r
Content management
Content management (CM) are a set of processes and technologies that support the collection, managing, and publishing of information in any form or medium. When stored and accessed via computers, this information may be more specifically referred to as digital content, or simply as content. Digital content may take the form of text (such as electronic documents), images, multimedia files (such as audio or video files), or any other file type that follows a content lifecycle requiring management. The process of content development and management is complex enough that various commercial software vendors (large and small), such as Interwoven and Microsoft, offer content management software to control and automate significant aspects of the content lifecycle. == Process == Content management practices and goals vary by mission and by organizational governance structure. News organizations, e-commerce websites, and educational institutions all use content management, but in different ways. This leads to differences in terminology and in the names and number of steps in the process. For example, some digital content is created by one or more authors. Over time that content may be edited. One or more individuals may provide some editorial oversight, approving the content for publication. Publishing may take many forms: it may be the act of "pushing" content out to others, or simply granting digital access rights to certain content to one or more individuals. Later that content may be superseded by another version of the content and thus retired or removed from use (as when this wiki page is modified). Content management is an inherently collaborative process. It often consists of the following basic roles and responsibilities: Creator – responsible for creating and editing content. Editor – responsible for tuning the content message and the style of delivery, including translation and localization. Publisher – responsible for releasing the content for use. Administrator – responsible for managing access permissions to folders, collections and files, usually accomplished by assigning access rights to user groups or roles. Admins may also assist and support users in various ways. Consumer, viewer or guest – the person who reads or otherwise consumes the content after it is published or shared. A critical aspect of content management is the ability to manage versions of content as it evolves (see also version control). Authors and editors often need to restore older versions of edited products due to a process failure or an undesirable series of edits. Time-sensitive content may also require updates as the subject matter evolves over time. Another equally important aspect of content management involves the creation, maintenance, and application of review standards. Each member of the content creation and review process has a unique role and set of responsibilities in the development or publication of the content. Each review team member requires clear and concise review standards. These must be maintained on an ongoing basis to ensure the long-term consistency and health of the knowledge base. A content management system is a set of automated processes that may support the following features: Import and creation of documents and multimedia material Identification of all key users and their roles The ability to assign roles and responsibilities to different instances of content categories or types Definition of workflow tasks often coupled with messaging so that content managers are alerted to changes in content The ability to track and manage multiple versions of a single instance of content The ability to publish the content to a repository to support access The ability to personalize content based on a set of rules Increasingly, the repository is an inherent part of the system, and incorporates enterprise search and retrieval. Content management systems take the following forms: Web content management system—software for web site management (often what content management implicitly means) Output of a newspaper editorial staff organization Workflow for article publication Document management systems Knowledge management software Single source content management system—content stored in chunks within a relational database Variant management system—where personnel tag source content (usually text and graphics) to represent variants stored as single source "master" content modules, resolved to the desired variant at publication (for example: automobile owners manual content for 12 model years stored as single master content files and "called" by model year as needed)—often used in concert with database chunk storage (see above) for large content objects == Governance structures == Content management expert Marc Feldman defines three primary content management governance structures: localized, centralized, and federated—each having its unique strengths and weaknesses. === Localized governance === By putting control in the hands of those closest to the content, the context experts, localized governance models empower and unleash creativity. These benefits come, however, at the cost of a partial-to-total loss of managerial control and oversight. === Centralized governance === When the levers of control are strongly centralized, content management systems are capable of delivering an exceptionally clear and unified brand message. Moreover, centralized content management governance structures allow for a large number of cost-savings opportunities in large enterprises, realized, for example, through (1) the avoidance of duplicated efforts in creating, editing, formatting, repurposing and archiving content; (2) process management and the streamlining of all content related labor; and/or (3) an orderly deployment or updating of the content management system. === Federated governance === Federated governance models potentially realize the benefits of both localized and centralized control while avoiding the weaknesses of both. While content management software systems are inherently structured to enable federated governance models, realizing these benefits can be difficult because it requires, for example, negotiating the boundaries of control with local managers and content creators. In the case of larger enterprises, in particular, the failure to fully implement or realize a federated governance structure equates to a failure to realize the full return on investment and cost savings that content management systems enable. == Implementation == Content management implementations must be able to manage content distributions and digital rights in content life cycle. Content management systems are usually involved with digital rights management in order to control user access and digital rights. In this step, the read-only structures of digital rights management systems force some limitations on content management, as they do not allow authors to change protected content in their life cycle. Creating new content using managed (protected) content is also an issue that gets protected contents out of management controlling systems. A few content management implementations cover all these issues.
Semiotics of social networking
The semiotics of social networking discusses the images, symbols and signs used in systems that allow users to communicate and share experiences with each other. Examples of social networking systems include Facebook, Twitter and Instagram. == Semiotics == Semiotics is a discipline that studies images, symbols, signs and other similarly related objects in an effort to understand their use and meaning. Semiotic structuralism seeks the meaning of these objects within a social context. Post-structuralist theories take tools from structuralist semiotics in combination with social interaction, creating social semiotics. Social semiotics is “a branch of the field of semiotics which investigates human signifying practices in specific social and cultural circumstances and which tries to explain meaning-making as a social practice.” “Social semiotics also examines semiotic practices, specific to a culture and community, for the making of various kinds of texts and meanings in various situational contexts and contexts of culturally meaningful activity”. Social semiotics is concerned with studying human interactions. == Social networking == Social networking is the communication among people within a virtual social space. This medium of communication allows insight into the significance of social semiotics. “Millions of people now interact through blogs, collaborate through wikis, play multiplayer games, publish podcasts and video, build relationships through social network sites and evaluate all the above forms of communication through feedback and ranking mechanisms”. Social semiotics “unlike speech, writing necessitates some sort of technology in the form of person device interaction”. Social semiotics functions through the triad of communication or Peircean semiotics in the form of sign, object, interpretant (Chart 1) and “Human, Machine, Tag (Information)” (Chart 2). In Peircean semiotics (Chart 1), "A sign…[in the form of representamen] is something which stands to somebody for something in some respect or capacity. It addresses somebody, that is, creates in the mind of that person an equivalent sign, or perhaps a more developed sign. That sign which it creates I call the interpretant of the first sign. The sign stands for an object, not in all respects, but in reference to a sort of idea which I have something called the ground of the representamen". This example of the triangle of Human, Machine, Tag is shown when looking at tagging photographs on Facebook (Chart 3). The Human takes the photo on a camera and puts the digital file (information) on the Machine, the Machine is then navigated to Facebook where the file is downloaded. The Human has the Machine Tag the photo with information (e. g., names, places, data) for other Humans to see. This process then can be continued (see Chart 2). “Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing large amounts of information”.
Social media use in the financial services sector
Social media in the financial services sector refers to the use of social media by the financial services sector to promote and distribute financial services. Social media is used in various aspects of the financial industry including customer service, marketing, and product development. It has enabled financial institutions to extend their reach through direct and real-time communication with customers, fostering more personal connections. It also allows individuals to talk to other individuals creating lending and trading via social groups as well as developing new financial services by fintech startup companies. In terms of marketing, social media is utilized by both traditional financial companies as well as disruptive fintech companies such as peer-to-peer lending (P2P) companies. The financial industry has used information technology since its inception in the 1960s and social media fits in with this ongoing development. Larger, traditional financial firms have integrated social media into their marketing strategies. Companies in the financial sector are subject to strict regulations that include how they use social media. In the United States, the Financial Industry Regulatory Authority (FINRA) is a key regulator that sets rules how financial firms can interact with consumers. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that financial advice is not provided by unqualified personnel, such as influencers. == History == In 2003, at the beginning of social media development, MySpace was founded as a "social networking service." It allowed people to create a profile, connect with other people, and post videos, pictures, and songs. As MySpace grew in popularity, it attracted interest from companies wishing to promote their brands on the social platform. They were joined by Facebook and in 2010 by Instagram. Financial service firms were initially slow to adapt to promotion via social media but soon joined other big firms after they saw the success other industries had in engaging with younger people. == Uses == === Branding === While companies are able to connect with more people remotely through providing online financial services, their branding strategy has shifted from customized to standardized. Prior to the outbreak of technology, most banks used customized branding where they targeted only customers in their regions. Businesses can now use technology to operate beyond their geographic location and maintain a consistent image across multiple countries with standardized branding. By being able to extend a consistent brand reputation across a wider geographic location, financial services companies can take advantage of economies of scale in advertising cost, lower administrative complexity, lower entry into new markets, and improved cross-border learning within the company. === Customer engagement === Online banking reduced face-to-face interaction between customers and their banks. Most banking transactions can now be conducted online or through mobile devices, rather than at a local branch with a teller. Social media provides a channel for firms to maintain personal contact with customers, replicating some of the interaction that was previously available at local branches. For example, a bank's Facebook page may feature an employee profile describing their job duties, which serves to present a more human face for larger institutions. === Lending === Social media is a core marketing channel for online peer-to-peer lending as well as small business lenders. Since these companies operate exclusively online, it makes sense for them to market online through social media channels. They are able to grow and find new lenders and buyers by utilizing social networks. === Trading === Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing, copying and discussing their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Investors also use platform like Reddit, Signal messaging or WeChat to create social communities to discuss investments and finance. In some cases they use this to join together using meme stocks to move financial markets, such as the 2021 GameStop short squeeze incident. They can also use social groups to launch and promote new products such as cryptocurrencies. Investing application like WeBull incorporate a forum style messaging system on each stock that is available for trading. Financial brokers such as Fidelity Investments, Interactive Brokers, and E-Trade have moved to incorporate community features in their investment apps. == Regulations == The use of social media by investors and financial services professionals for business purposes is subject to regulatory oversight, in the United States this is done primarily by the Financial Industry Regulatory Authority (FINRA). FINRA's rules, designed to protect investors from misleading information in all communications and this also applies to social media communications. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that advice is not provided by unqualified personnel, such as influencers and bank staff acting in a personal capacity. Financial firms have to maintain books and records of all interaction with customers and this includes social media. == New products and services == Social media has created entirely new products for the financial services sector, revolutionizing products and developing new industries through the merging of social technology and financial services. Fintech startups use social media to promote products to get them established. Several developing nations have used social media to leapfrog traditional financial technology; for example, WeChat Pay, which developed from the Chinese WeChat social media platform, became a major payment system in China within a few years. In 2015, according to consulting firm Accenture, 390 million people in China had registered to use mobile banking. This figure is more than the population of the United States. In the United States, the fintech company Venmo combines technology and financial services on a social platform. Other financial technology companies that have used social media to develop or promote financial products include: Lending Club – One of the first peer-to-peer lending businesses OnDeck Capital – A US online-only lending business Funding Circle – A UK-based online lending company Wise – A global online money transfers company Kabbage – A US online unsecured loan company later acquired by American Express Avant – A US online unsecured loan company Zopa – A UK online neobank providing peer-to-peer lending == Risks == === Reputational damage === Due to the real-time nature of social media, financial services companies can be impacted by potential reputational issues. Any negative experience by customers can easily be shared online and could become a viral phenomenon, those comments could likely have a detrimental effect on the company’s stock price and reputation. On the other hand, any positive experience a customer has can also be shared online. However, positive experiences are much less likely to become viral. === Scams === The nature of social media makes it easy to target individuals without being seen by the wider community, this allows scammers to target individuals. Example include romance scams such as the pig butchering scam where an individual is tricked to transfer funds or assets to the scammer over social media making it hard for law enforcement to track them or recover funds. === Customer privacy === Customer privacy is important for the financial services industry. It is critical that customer information such as a bank account numbers and other personal information is kept private. However, this information can be leaked if for example, a customer is unhappy with a bank’s service, they may tweet at the bank expressing their frustrations and include their name and account number.
Aporia (company)
Aporia is a machine learning observability platform based in Tel Aviv, Israel. The company has a US office located in San Jose, California. Aporia has developed software for monitoring and controlling undetected defects and failures used by other companies to detect and report anomalies, and warn in the early stages of faults. == History == Aporia was founded in 2019 by Liran Hason and Alon Gubkin. In April 2021, the company raised a $5 million seed round for its monitoring platform for ML models. In February 2022, the company closed a Series A round of $25 million for its ML observability platform. Aporia was named by Forbes as the Next Billion-Dollar Company in June 2022. In November, the company partnered with ClearML, an MLOPs platform, to improve ML pipeline optimization. In January 2023, Aporia launched Direct Data Connectors, a novel technology allowing organizations to monitor their ML models in minutes (previously the process of integrating ML monitoring into a customer’s cloud environment took weeks or more.) DDC (Direct Data Connectors) enables users to connect Aporia to their preferred data source and monitor all of their data at once, without data sampling or data duplication (which is a huge security risk for major organizations. In April 2023, Aporia announced the company partnered with Amazon Web Services (AWS) to provide more reliable ML observability to AWS consumers by deploying Aporia's architecture to their AWS environment, this will allow customers to monitor their models in production regardless of platform.
Instagram egg
The Instagram egg is a photo of an egg posted by the account @world_record_egg on the social media platform Instagram. It became a global phenomenon and an internet meme within days of its publication on 4 January 2019. It is the second most-liked Instagram post and was the most-liked Instagram post from 14 January 2019 until 20 December 2022, when it was overtaken by Lionel Messi's post showing him and his teammates celebrating after Argentina won the 2022 FIFA World Cup. The owner of the account was revealed to be Chris Godfrey, a British advertising creative, who later worked with his two friends Alissa Khan-Whelan and CJ Brown on a Hulu commercial featuring the egg, intended to raise mental health awareness. == Background == The photo was originally taken by Serghei Platanov, who then posted it to Shutterstock on 23 June 2015 with the title "eggs isolated on white background". == History == On 4 January 2019, the @world_record_egg account was created, and posted an image of a bird egg with the caption, "Let's set a world record together and get the most liked post on Instagram. Beating the current world record held by Kylie Jenner (18 million)! We got this." Jenner's previous record, the first photo of her daughter Stormi, had garnered a total of 18.4 million likes. The post quickly reached 18.4 million likes in just under 10 days, becoming the most-liked Instagram post at the time. It then continued to rise over 45 million likes in the next 48 hours, surpassing the "Despacito" music video and taking the world record for the most-liked online post (on any media platform) in history. After the account became verified on 14 January 2019, the post rose in popularity and likes, which snowballed into coverage in various media outlets. By 18 March 2019, the post had accumulated over 53.3 million likes, nearly three times the previous record of 18.4 million. It posted frequent updates for a few days in the form of Instagram Stories. Alongside the like tally, as of January 2023 the post has 3.8 million comments. Several individuals tried to claim that they were the account's creator, the claims being dismissed by "the egg" on Instagram direct messages. On 3 February 2019, the creator of the Instagram egg was revealed by Hulu and The New York Times to be Chris Godfrey, a British advertising creative. Alissa Khan-Whelan, his colleague, was also outed. On 18 January 2019, the account posted a second picture of an egg, almost identical to the first one apart from a small crack at the top left. As of 25 February 2019, the post accumulated 11.8 million likes. On 22 January 2019, the account posted a third picture of an egg, this time having two larger cracks. In less than 25 minutes, the post accumulated 1 million likes, and by 25 February 2019, it had accumulated 9.5 million likes. On 29 January 2019, a fourth picture of an egg was posted to the account which has another large crack on the right hand side, attracting 7.6 million likes by 25 February 2019. On 1 February 2019, a fifth picture of an egg was posted with stitching like that of a football, referencing the upcoming Super Bowl. That post had accumulated 6.5 million likes by 25 February 2019. The account promised that it would reveal what was inside the egg on 3 February, on the subscription video on demand service Hulu. The Hulu Instagram egg reveal was used to promote an animation about a mental health campaign. A caption from the clip read, "Recently I've started to crack, the pressure of social media is getting to me. If you're struggling too, talk to someone." The video was later posted on the @world_record_egg Instagram account, and this post received over 33 million views by May 2019. As of May 2020, it had received over 41 million views. On 16 July 2019, Chris Godfrey (the creator of the account) was listed as one of the top 25 most influential people on the internet. On 20 December 2022, the record for the most-liked Instagram post was surpassed by a post from Argentine footballer Lionel Messi, showing him and his teammates celebrating after winning the 2022 FIFA World Cup with their national team. The world record egg responded to being overtaken in likes by Messi with "Today [Lionel Messi] has taken the crown, for now. But I'm still left with one question… Who is the greatest of all time – Cristiano Ronaldo or Leo Messi?" The account sold to Dubai-based investor Mustafa El Fishawy in April 2024 for an undisclosed seven-figure sum. Reed Smith, who advised Godfrey, Brown, and Khan-Whelan in the transaction, stated they opted to sell it to "focus on new ventures." On 3 June, @world_record_egg posted an egg with the flag of Palestine in support of the country during the Gaza war; the post's caption described it as an "Egg for Peace" and hoped to "set a new world record together and get the most liked post on Instagram for a good cause." == Reception == In response to breaking the world record for the most-liked Instagram post, the account's owner wrote "This is madness. What a time to be alive." Hours later, Jenner posted a video on Instagram of her cracking open an egg and pouring its yolk onto the ground, with the caption: "Take that little egg." Pundits pontificated on the meaning of the egg picture's dominance over social media's "first family". As Vogue observed, tapping a heart pictogram is easy, and eggs are "lovable". More pointedly: [T]he attention economy is a scam based on requiring little to no labor from both producer and consumer despite commanding the most space, and therefore value, in our digital lives... but it very well could be: As a metaphor for the fragility of the influencer ecosystem, the egg has broken the Internet. The significance of the event and its massive republishing are a topic of discussion. A University of Westminster researcher of internet memes compared it to the movement to name a scientific research vessel in the United Kingdom as Boaty McBoatface. The Instagrammer's success is a rare victory for the unpaid viral campaign on social media. "There is a bit of an anti-celebrity revolt here – 'look what we can do with a simple egg'" The researcher suggests that the accomplishment of becoming such a widely heralded unpaid viral post may become increasingly rare, as social networks rely more on paid and business promotion. The post's spread has been characterized as a populist backlash against "consumerism" and is seen by some as a triumph of community over celebrity. However, propelled by their popular success, the creators promised to release 'egg-centric' memorabilia. Hundreds of games based on the Instagram egg have appeared on Apple's App Store. The creators of the Instagram egg also reached a deal to promote Hulu.