AI Grammar Sentence Rewriter

AI Grammar Sentence Rewriter — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Apache Drill

  Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Drill supports a variety of NoSQL databases and file systems, including Alluxio, HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. Drill's datastore-aware optimizer automatically restructures a query plan to leverage the datastore's internal processing capabilities. In addition, Drill supports data locality, if Drill and the datastore are on the same nodes. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016. == Features == One explicitly stated design goal is that Drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. Schema-free JSON document model similar to MongoDB and Elasticsearch, without requiring a formal schema to be declared Industry-standard APIs: ANSI SQL, ODBC/JDBC, RESTful APIs Extremely user and developer friendly Pluggable architecture enables connectivity to multiple datastores Version 1.9 added dynamic user-defined functions Version 1.11 added cryptographic-related functions and PCAP file format support == Back-end support == Drill is primarily focused on non-relational datastores, including Apache Hadoop text files, NoSQL, and cloud storage. A notable feature also includes in situ querying of local JSON and Apache Parquet files. Some additional datastores that it supports include: All Hadoop distributions (HDFS API 2.3+), including Apache Hadoop, MapR, CDH and Amazon EMR NoSQL: MongoDB, Apache HBase, Apache Cassandra Online Analytical Processing: Apache Kudu, Apache Druid, OpenTSDB Cloud storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift, IBM Cloud Object Storage Diverse data formats, including Apache Avro, Apache Parquet and JSON RDBMs storage plugins (Using JDBC to connect to MySQL, PostgreSQL, and others) A new datastore can be added by developing a storage plugin. Drill's "schema-free" JSON data model enables it to query non-relational datastores in-situ . == Front-end support == Drill itself can be queried via JDBC, ODBC, or REST through a variety of methods and languages including Python and Java. The default install includes a web interface allowing end-users to execute ANSI SQL directly and export data tables as CSV files without any programming. The dashboard library, Apache Superset, is particularly well suited for visualization of data queried with Drill.

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  Pharmacy automation

  Pharmacy automation involves the mechanical processes of handling and distributing medications. Any pharmacy task may be involved, including counting small objects (e.g., tablets, capsules); measuring and mixing powders and liquids for compounding; tracking and updating customer information in databases (e.g., personally identifiable information (PII), medical history, drug interaction risk detection); and inventory management. This article focuses on the changes that have taken place in the local, or community pharmacy since the 1960s. == History == Dispensing medications in a community pharmacy before the 1970s was a time-consuming operation. The pharmacist dispensed prescriptions in tablet or capsule form with a simple tray and spatula. Many new medications were developed by pharmaceutical manufacturers at an ever-increasing pace, and medications prices were rising steeply. A typical community pharmacist was working longer hours and often forced to hire staff to handle increased workloads which resulted in less time to focus on safety issues. These additional factors led to use of a machine to count medications. The original electronic portable digital tablet counting technology was invented in Manchester, England between 1967 and 1970 by the brothers John and Frank Kirby. I had the original idea of how the machine would work and it was my patent, but it was a joint effort getting it to work in a saleable form. It was 3 years of very hard work. I had originally studied heavy electrical engineering before changing over to Medical School and qualifying as a Medical Doctor in 1968. In fact I was Senior House (Casualty) Officer (A&E or ER) in 1970 at North Manchester General Hospital when I filed the patent. I must have been the only hospital doctor in Britain with an oscilloscope, a soldering iron and a drawing board in his room in the Doctors' Residence. The housekeepers were bemused by all the wires. Frank originally trained as a Banker but quit to take a job with a local electronics firm during the development. He died in 1987, a terrible loss. [Extract from personal communication received in March 2010 from John Kirby.] Frank and John Kirby and their associate Rodney Lester were pioneers in pharmacy automation and small-object counting technology. In 1967, the Kirbys invented a portable digital tablet counter to count tablets and capsules. With Lester they formed a limited company. In 1970, their invention was patented and put into production in Oldham, England. The tablet counter aided the pharmacy industry with time-consuming manual counting of drug prescriptions. A counting machine consistently counted medications accurately and quickly. This aspect of pharmacy automation was quickly adopted, and innovations emerged every decade to aid the pharmacy industry to deliver medications quickly, safely, and economically. Modern pharmacies have many new options to improve their workflow by using the new technology, and can choose intelligently from the many options available. === Chronology === On 1 January 1971 commercial production of the first portable digital tablet counters in the World began. John Kirby had filed U.K. Patent number GB1358378(A) on 8 September 1970 and U.S. patent number 3789194 on 9 August 1971. These early electronic counters were designed to help pharmacies replace the common (but often inaccurate) practice of counting medications by hand. In 1975, the digital technology was exported to America. In early 1980 a dedicated research, development and production facility was built in Oldham, England at a cost of £500,000. Between 1982 and 1983, two separate development facilities had been created. In America, overseen by Rodney Lester; and in England, overseen by the Kirby brothers. In 1987, Frank Kirby died. In 1989, John Kirby moved his UK facility to Devon, England. A simple to operate machine had been developed to accurately and quickly count prescription medications. Technology improvements soon resulted in a more compact model. The price of such equipment in 1980 was around £1,300. This substantial investment in new technology was a major financial consideration, but the pharmacy community considered the use of a counting machine as a superior method compared to hand-counting medications. These early devices became known as tablet counter, capsule counter, pill counter, or drug counter. The new counting technology replaced manual methods in many industries such as, vitamin and diet supplement manufacturing. Technicians needed a small, affordable device to count and bottle medications. In England and America, the 1980s and 1990s saw new the development of high-speed machines for counting and bottle filling, Like their pharmacy-based counterparts, these industrial units were designed to be fast and simple to operate, yet remain small and cost effective. In America, in the late 1990s/early 2000s a new type of tablet counter appeared. It was simple to use, compact, inexpensive, and had good counting accuracy. At the turn of the millennium technical advances allowed the design of counters with a software verification system. With an onboard computer, displaying photo images of medications to assist the pharmacist or pharmacy technician to verify that the correct medication was being dispensed. In addition, a database for storing all prescriptions that were counted on the device. Between September 2005 and May 2007, American Capital made a major financial investment in Kirby Lester, which then relocated to a larger facility to expand its research and development capabilities. This move added extra space for product research and development facility (R&D). It allowed the opportunity to develop new advanced technology products that met the pharmacy's needs for simple, accurate, and cost-effective ways to dispense prescriptions safely. Pictured here is an early American type of integrated counter and packaging device. This machine was a third generation step in the evolution of pharmacy automated devices. Later models held pre-counted containers of commonly-prescribed medications. == Global variations == In the EU member states legislation was introduced in 1998 which had a major effect on UK Pharmacy operations. It effectively prohibited the use of tablet counters for counting and dispensing bulk packaged tablets. Both usage and sales of the machines in the UK declined rapidly as a result of the introduction of blister packaging for medicines. == Current state of the industry == A tablet counter has become a standard in more than 30,000 sites in 35 countries (as of 2010) (including many non-pharmacy sites, such as manufacturing facilities that use a counting machine as a check for small items). During the 1990s through 2012, numerous new pharmacy automation products came to market. During this timeframe, counting technologies, robotics, workflow management software, and interactive voice recognition (IVR) systems for retail (both chain and independent), outpatient, government, and closed-door pharmacies (mail order and central fill) were all introduced. Additionally, the concept of scalability - of migrating from an entry-level product to the next level of automation (e.g., counting technology to robotics) - was introduced and subsequently launched a new product line in 1997. Pharmacists everywhere are making the switch to automation for its increased speed, greater accuracy, and better security. As the industry evolves and customer expectations grow, automation is becoming less of a luxury and more of a necessity. Especially for independent pharmacies, automation is now a means of keeping up with the competition of large chain pharmacies. == Technological changes and design improvements == Constant developments in technology make the dispensing of prescription medications safer, more accurate and more efficient. In America, in 2008, "next-generation" counting and verification systems were introduced. Based on the counting technology employed in preceding models, later machines included the ability to help the pharmacy operate more effectively. Equipped with a new computer interface to a pharmacy management system, with workflow and inventory software. It also included "checks and balances" to ensure the technician and pharmacist were dispensing the correct medication for each patient. This is something that is important to keep reported correctly when dealing with controlled substances like narcotics. This was a step forward to verify all 100% of prescriptions that were dispensed by pharmacy staff. In America, in 2009, further advanced counters were designed that included the ability to dispense hands-free – a feature that many operators had desired. This allowed pharmacies to automate their most commonly dispensed medications via calibrated cassettes. Thirty of a pharmacy's common medications would now be dispensed automatically. Another new model doubled that throughput via an enclosed robotic mechanism. Robo

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  Uniphore

  Uniphore is an American software company that develops artificial intelligence platforms for business use. The company is headquartered in Palo Alto, California, with offices in the United States, United Kingdom, Spain, Israel, United Arab Emirates, and India. Uniphore is known for its "Business AI Cloud," an enterprise AI platform that combines data, knowledge, models, and software agents for use in sales, marketing, and service. The company has also acquired firms in video emotion AI, AI agents, low-code automation, knowledge automation, voice and screen capture, customer data platforms, and data engineering. == History == Uniphore Software Systems was founded by Umesh Sachdev and Ravi Saraogi in 2008 and was incubated at IIT Madras. The company received an initial grant of $100,000 from the National Research Development Corporation. Early work focused on speech technologies for emerging markets. Uniphore partnered with companies that specialized in English and European languages, and adapting the technology for Indian languages and dialects. In 2014, Uniphore released its first flagship products, auMina, along with two other products, Akeira and amVoice. Uniphore raised series A funding, led by Kris Gopalakrishnan (cofounder of Infosys), in April 2015. The next month, Uniphore received additional investment from IDG Ventures. With input from its investors, Uniphore changed its business model from license fee-based income to a software as a service-based subscription fee model in 2015. By June 2016, it had added more than 70 global languages and expanded its services to Southeast Asia, the Middle East, and the United States. The company opened operations in Singapore in October 2016. The company raised Series B funding in October 2017, led by John Chambers and existing investors. Series C funding of $51 million was announced in August 2019 and led by March Capital. Uniphore acquired an exclusive third-party license for robotic process automation technology from NTT DATA in October 2020. In January 2021, Uniphore acquired Emotion Research Lab, a startup based in Spain that uses artificial intelligence and machine learning to analyze video and interpret emotions. The company received $140 million in Series D funding, led by Sorenson Capital Partners, in March 2021, bringing total funding to $210 million. In January 2021, Uniphore acquired Emotion Research Lab. In July 2021, it agreed to acquire Jacada, a provider of low-code/no-code automation; the transaction closed in October 2021. On February 16, 2022, Uniphore announced a $400 million Series E financing led by NEA, which valued the company at $2.5 billion. Hilarie Koplow-McAdams, an NEA venture partner and former Salesforce/New Relic executive, joined Uniphore's board in 2022. Uniphore's board has also included former Cisco CEO John Chambers, former Convergys CEO Andrea J. Ayers, and CrowdStrike CFO Burt Podbere (appointed January 2021). In February 2023, Uniphore acquired UK-based Red Box, a platform for capturing voice and screen recordings used in regulated and large-scale environments. It also acquired France-based Hexagone, a behavioral analytics firm combining computer vision and natural-language techniques. On December 5, 2024, Uniphore announced agreements to acquire ActionIQ, a customer data platform (CDP) vendor, and Infoworks, an enterprise data engineering platform. Uniphore launched the Business AI Cloud on June 9, 2025. The Business AI Cloud consists of a single, unified platform that includes data, knowledge, AI models, and AI agents. Uniphore announced in August 2025 that it had acquired Orby AI and intended to acquire Autonom8 to extend multi-agent and workflow automation capabilities. As of September 2025, Uniphore's customers included the United States Coast Guard, Singapore Police Force, London Underground, DirecTV, JPMorgan Chase, LG, DHL, UPS, Vodafone, Verizon, NTT Data, and as of May 2021, Firstsource. In October 2025, Uniphore raised $260 million in a Series F round at a reported valuation of $2.5 billion. Investors included March Capital, NEA, Nvidia, AMD, Snowflake, and Databricks. In January 2026, KPMG and Uniphore announced a collaboration focused on deploying AI agents powered by specialized small language models. The announcement was made at the World Economic Forum held in Davos. Cognizant and Uniphore announced a partnership in February 2026 to develop industry-specific AI tools for regulated sectors, which would initially focus on life sciences and finance. Uniphore and Rackspace also announced a partnership in March 2026. This partnership was announced in order to create an "Infrastructure-to-Agents" architecture, focusing on Business AI as a private cloud service. == Products == As of 2025, Uniphore's core offering is the Business AI Cloud and Business AI Suite of agentic AI applications. === Business AI Cloud === Uniphore’s Business AI Cloud is a full-stack platform that organizes enterprise data and knowledge for agentic AI applications. The platform enables deployment across clouds and existing data sources. Key layers and capabilities include the following. Agentic layer: Includes prebuilt agents, a natural-language agent builder, and orchestration based on Business Process Model and Notation (BPMN) to run AI workflows across business units. Model layer: Supports an open, interoperable mix of closed and open-source large language models (LLMs). Models can be orchestrated, governed, and replaced as needed. Knowledge layer: Organizes raw data into structured knowledge used for retrieval, explainability, and fine-tuning of small language models (SLMs). Data layer: Connects to data across multiple platforms and clouds through a zero-copy, composable fabric, enabling in-place preparation and supporting data residency and sovereignty requirements. === Business AI Suite === The Uniphore Business AI Suite has various prebuilt AI agents that can be used in customer service, sales, marketing, and human resources. The Uniphore Business AI Suite includes several LOBs (Lines of Business) for business functions with intelligent agents that are prebuilt, but composable. Built on the Uniphore Business AI Cloud, each application combines agentic automation and fine-tuned models. Marketing AI, Customer Service AI, Sales AI, and People AI (for human resources) are included. Competitors include Palantir, Microsoft Azure, Amazon Bedrock, Google's Vertex AI, Databricks, and Snowflake. == Recognition == Deloitte Technology Fast 50 India identified Uniphore as the 17th fastest-growing technology company in India in 2012 and one of the top 500 fastest growing companies in the Asia-Pacific region in 2014. In 2016, Time included Sachdev on its list of "10 millennials who are changing the world" for “building a phone that can understand almost any language”. NASSCOM named Uniphore to its "League of 10" emerging Indian technology companies in 2017. In 2020, the San Francisco Business Times ranked Uniphore as No. 7 among small companies in its list of the best places to work in the San Francisco Bay Area. In 2022, the company was featured on the Forbes AI 50 list. Uniphore was mentioned in the Deloitte Technology Fast 500 list in 2023, 2024, and 2025. In 2025, Inc. included Uniphore in its Best in Business program.

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  Multiple satellite imaging

  Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.

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  Friendica

  Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. It forms one part of the Fediverse, an interconnected and decentralized network of independently operated servers. == Features == Friendica users can connect with others via their own Friendica server, but may also fully integrate contacts from other platforms including Diaspora, Pump.io, GNU social, email, Discourse and more recently ActivityPub (including Mastodon, Pleroma and Pixelfed) and Bluesky into their 'newsfeed'. In addition to these two way connections, users can also use Friendica as a publishing platform to post content to WordPress, Tumblr, Insanejournal and Libertree. Posting to Google+ was also supported until that service was shut down. In addition, RSS feeds can be ingested. Because users are distributed across many servers, their "addresses" consist of a username, the "@" symbol, and the domain name of the Friendica instance in the same manner email addresses are formed. Twitter support was available but was deprecated due to API changes under Elon Musk's leadership rendering it unusable. Most of the functionality from major microblogging and social networking platforms are available in Friendica; for example, tagging users and groups via "@ mentions"; direct messages; hashtags; photo albums; "likes"; "dislikes"; comments; and re-shares of publicly visible posts. Published items can be edited and updated across the network. Comprehensive settings for privacy and the public visibility of posts allow users to regulate who can read which contributions, or see specific information about the user. Users can also create multiple profiles, allowing different groups of people (such as friends, or work mates) to see a different profile entirely when viewing the same page. User accounts can be downloaded or deleted, and can be imported to a different Friendica server if so required. Public forums can be created under different accounts, which can be switched between if the accounts are registered with the same email address. == Development == There is no corporation behind Friendica. The developers work on a voluntary basis and the project is run informally; the platform itself is used for the communication between the developers. There are different forums within Friendica, such as "Friendica Developers" and "Friendica Support". The source code of Friendica is hosted on GitHub. == Installation == The developers aim to make installation of the software as simple as possible for technical laymen. They argue that decentralization on small servers is a key condition for the freedom of users and their self-determination. The difficulty level is similar to an installation of WordPress. However, the installing on shared hosting is sometimes difficult because of missing PHP5 modules. Some volunteers also run public servers so that newcomers can also avoid the installation of their own software. == List of clients == Friendica implements multiple client-server API variants simultaneously. Along with endpoints needed to use enhanced Friendica features, it also implements the API used by GNU social, Twitter and since version 2021.06 also the one used by Mastodon. As a result, most GNU social and Mastodon clients can be used for Friendica. Examples of Friendica compatible clients include: Raccoon for Friendica, Friendiqa, Fedilab, AndStatus, Twidere and DiCa for Android, friendly for Sailfish OS, friclicli (CLI client), choqok and Friendiqa for Linux and Friendica Mobile for Windows 10. == Reception == Friendica was cited in January 2012 by Infoshop News as an "alternative to Google+ and Facebook" to be used on the Occupy Nigeria movement. In January 2012 Free Software Foundation Europe's blog cited Friendica as a reasonable alternative to centralized and controlled social networks such as Facebook or Google+. Biblical Notes writer J. Randal Matheny described Friendica in January 2012 as "One social networking option flying under the radar until recently deserves consideration as an already stable platform with a wide range of options, applications, plug-ins, and possibilities for opening up the Internet." In February 2012, the German computer magazine c't wrote: "Friendica demonstrates how decentralized social networks can become widely accepted." Another German publication, the professional magazine t3n listed Friendica as a Facebook rival in an online article in March 2012 about Facebook alternatives. It compared Friendica with similar social networks like Diaspora and identi.ca. MSN Tech & Gadgets contributor Emma Boyes wrote about Friendica in May 2012: "why you'll love it: you can use it to access all the other social networks and get recommendations of new friends and groups to join. Friendica is open source and decentralised. There's no corporation behind it and there are extensive privacy settings. You can choose from a variety of user interfaces and it boasts some cool features—for instance, being able to key in a list of your interests and use the 'profile match' feature to recommend other users who share them with you. A word of warning, though, the site is not as user-friendly as the others on this list, so it may be this one is one for the geeks." == Later reviews == Acquisition of Twitter by Elon Musk had revitalized public interest in Fediverse technologies in April 2022. Friendica received favorable reviews, with a PCMag article describing it as "mostly comparable to Facebook", drawing a parallel to Google+ and highlighting using it "for planning events, and its multiple profile feature means you can show a different face to your friends, coworkers, and family". The September 2022 issue of Linux Magazine contains a detailed comparison and walk-through of registering to and using basic functions of Diaspora, Friendica and Mastodon. They describe Friendica as "intuitive" and highlight the "huge choice of account settings" and that "Friendica does not require any specific hardware, so you can use an old computer system as a server." == Vulnerabilities == In September 2020, a hotfix was released to patch a security vulnerability that could leak sensitive information from the server environment since versions released in April 2019 (develop branch) and June 2019 (stable).

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  Semi-automation

  Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.

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  Super app

  A super app or super-app (also known as an everything app) is a mobile or web application that can provide multiple services including payment and instant messaging services, effectively becoming an all-encompassing, self-contained, commerce and communication online platform that embraces many aspects of personal and commercial life. Notable examples of super apps include Tencent's WeChat in China, Tata Neu in India, Grab in Southeast Asia and Max in Russia. For end users, a super app is an application that provides a set of core features while also giving access to independently developed miniapps. For app developers, a super app is an application integrated with the capabilities of platforms and ecosystems that allows third-parties to develop and publish miniapps. == History == The super app term was first used to describe WeChat when it combined the instant messaging service with the digital wallet function. Recognition of WeChat as a super app stems from its combination of messaging, payments, e-commerce, and much more within a single application, making it indispensable for many users. WeChat's establishment of the super app model has led companies like Meta to try to build similar applications outside of China. In India, Tata Group has announced that it is currently developing a super app named Tata Neu. Major Indian companies like Paytm, PhonePe, and ITC Maars also have apps in development that might constitute super apps. In Southeast Asia, Grab and Gojek lay claim to the super app classification despite lacking many of the features offered by WeChat. Accordingly, growth-stage companies like Shopee, Traveloka, and AirAsia have also expanded the range of services offered by their respective applications. == Notable examples == === Alipay === Alipay is a third-party mobile and online payment platform established in Hangzhou, China in February 2004 by Alibaba Group and its founder Jack Ma. It operates in association with Ant Group, an affiliate company of the Chinese Alibaba Group. === Gojek === Gojek is an Indonesian on-demand multiservice digital platform and fintech payment super app. Established in Jakarta in 2010, as a call center to connect consumers to courier delivery and two-wheeled ride-hailing services, it launched its mobile app in 2015 with four services: GoRide, GoSend, GoShop, and GoFood, which has since expanded to offer over 20 services. In 2021, it merged with another Indonesian unicorn, Tokopedia, forming the decacorn GoTo Gojek Tokopedia. === Grab === Grab is a Southeast Asian technology company headquartered in Singapore and Indonesia. Founded in 2012 as the MyTeksi app in Kuala Lumpur, Malaysia, it expanded the following year as GrabTaxi, before moving its headquarters to Singapore in 2014 and rebranding officially as Grab. In addition to ride-hailing and transportation services, the company's mobile app also offers food delivery and digital payment services. === Max === Max is a messenger from the Russian company VK, positioned as a super app. The application combines messaging, calls, and channels features with the integration of additional services: payments, miniapps, taxi ordering, deliveries, and other everyday services are available within a single interface. The goal is to unite communication and routine tasks in a unified ecosystem. === Tata Neu === Tata Neu is a multipurpose super app, developed in India by the Tata Group. It is the country's first super app. The app was launched to coincide with the start of a 2022 Indian Premier League cricket match. === WeChat === WeChat is a Chinese multipurpose instant messaging, social media and mobile payment app. First released in 2011, it became the world's largest standalone mobile app in 2018, with over 1 billion monthly active users. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, the sharing of photographs and videos and location sharing. === X === X is an American social network, originally known as Twitter from its launch through 2023. Prior to his acquisition of the service, new owner Elon Musk stated that he planned for Twitter to become an "everything app" known as "X"; in 2023, the service added an AI chatbot known as "Grok" as well as integrated job search tools known as "X Hiring". In January 2025, X announced its intent to offer a digital wallet service in the future. Later in the year, X revamped its direct messaging system as "Chat". == Criticism == Although apps that fit the super app classification can offer users a wider variety of services in comparison to single-purpose alternatives, internet regulators in regions such as the US and Europe have become more concerned about the overall power of the technology industry and have become more critical of companies developing such apps. In China, WeChat and other local firms have been ordered to open up their platforms to rivals by local regulators. There are also reports that suggest it might be difficult to replicate WeChat's super app model. This stems partly from the peaking of smartphone penetration rates in many regions worldwide, which has led to overcrowded app stores and tighter restrictions on targeted advertising as regulators assert more control over the companies. From a technical viewpoint, single-purpose apps are comparatively faster, more responsive and easier to navigate than super apps, which helps improve the overall user experience. Super-apps are also likelier to store larger amounts of personal data to facilitate the delivery of their services, so users run a greater risk of becoming victims of severe data breaches. In 2020, this unfolded with Tokopedia, which had the data of 91 million of its users stolen and shared by crackers. It has also been noted that a user who loses access to their account or is banned from a super app generally loses access to multiple real-life services and digital applications; the Chinese government has used this approach to penalize people who shared the photos of the Sitong Bridge protest.

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  Reconstruction from projections

  The problem of reconstructing a multidimensional signal from its projection is uniquely multidimensional, having no 1-D counterpart. It has applications that range from computer-aided tomography to geophysical signal processing. It is a problem which can be explored from several points of view—as a deconvolution problem, a modeling problem, an estimation problem, or an interpolation problem. == Motivation and applications == Many fields in science and engineering use reconstruction from projections, especially in imaging. It is widely applied geophysical tomography, medical imaging and industrial radiography. For example, in a CT scanner, the 3D structure of the patient’s body being scanned is measured with beams going through the tissue and hitting a detector, giving a flat projection of the body from that angle. Multiple projections are put together to get an image of the position and shape of structures inside in 3D. == Problem statement and basics == A projection is a linear mapping of an M {\displaystyle M} dimensional signal into an N {\displaystyle N} dimensional one, where N ≤ M {\displaystyle N\leq M} . And the objective of reconstruction is to restore the M {\displaystyle M} dimensional signal based on the N {\displaystyle N} dimensional signal. The following case is a 2-D signal projected into 1D signal. The signal in the original coordinate is denoted as d ( u , v ) {\displaystyle d(u,v)} . Now consider a collimated beam of radiation coming from the opposite orientation of v ^ {\displaystyle {\hat {v}}} , producing a projection along u ^ {\displaystyle {\hat {u}}} . v ^ {\displaystyle {\hat {v}}} and u ^ {\displaystyle {\hat {u}}} are normal to each other, and the angle between u {\displaystyle u} and u ^ {\displaystyle {\hat {u}}} is theta. The signal obtained along u ^ {\displaystyle {\hat {u}}} axis is defined to be p θ ( u ^ ) {\displaystyle p_{\theta }({\hat {u}})} . The relationship between the original coordinate and the rotated coordinate is given by [ u ^ v ^ ] = [ cos ⁡ θ sin ⁡ θ − sin ⁡ θ cos ⁡ θ ] [ u v ] {\displaystyle {\begin{bmatrix}{\hat {u}}\\{\hat {v}}\end{bmatrix}}={\begin{bmatrix}\cos \theta &\sin \theta \\-\sin \theta &\cos \theta \end{bmatrix}}{\begin{bmatrix}u\\v\end{bmatrix}}} or inversely, [ u v ] = [ cos ⁡ θ − sin ⁡ θ sin ⁡ θ cos ⁡ θ ] [ u ^ v ^ ] {\displaystyle {\begin{bmatrix}u\\v\end{bmatrix}}={\begin{bmatrix}\cos \theta &-\sin \theta \\\sin \theta &\cos \theta \end{bmatrix}}{\begin{bmatrix}{\hat {u}}\\{\hat {v}}\end{bmatrix}}} Then we have p θ ( u ^ ) = ∫ − ∞ ∞ d ( u , v ) d v ^ = ∫ − ∞ ∞ d ( u ^ cos ⁡ ( θ ) − v ^ sin ⁡ ( θ ) , u ^ sin ⁡ ( θ ) + v ^ cos ⁡ ( θ ) ) d v ^ {\displaystyle p_{\theta }({\hat {u}})=\int _{-\infty }^{\infty }d(u,v)\,\mathrm {d} {\hat {v}}=\int _{-\infty }^{\infty }d({\hat {u}}\cos(\theta )-{\hat {v}}\sin(\theta ),{\hat {u}}\sin(\theta )+{\hat {v}}\cos(\theta ))\,\mathrm {d} {\hat {v}}} By varying theta, a large number of projections can be obtained. Given the projection-slice theorem, D ( Ω , θ ) {\displaystyle D(\Omega ,\theta )} ,the slice of the Fourier transform of d ( u , v ) {\displaystyle d(u,v)} at angle theta, is equivalent to P θ ( Ω ) {\displaystyle P_{\theta }(\Omega )} , the Fourier Transform of the projection p θ ( u ^ ) {\displaystyle p_{\theta }({\hat {u}})} . Therefore, the unknown d ( u , v ) {\displaystyle d(u,v)} can be obtained from its Fourier transform by means of the Fourier transform inversion integral d ( u , v ) = 1 4 π 2 ∫ − ∞ ∞ ∫ − ∞ ∞ D ( Ω 1 , Ω 2 ) e j Ω 1 u e j Ω 2 v d Ω 1 , Ω 2 {\displaystyle \mathrm {d} (u,v)={\frac {1}{4\pi ^{2}}}\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }D(\Omega _{1},\Omega _{2})e^{j\Omega _{1}u}e^{j\Omega _{2}v}\,\mathrm {d} \Omega _{1},\Omega _{2}} = 1 4 π 2 ∫ 0 ∞ ∫ − π π D ( Ω , θ ) e j Ω u cos ⁡ ( θ ) e j Ω v s i n θ | Ω | d Ω d θ {\displaystyle ={\frac {1}{4\pi ^{2}}}\int _{0}^{\infty }\int _{-\pi }^{\pi }D(\Omega ,\theta )e^{j\Omega u\cos(\theta )}e^{j\Omega vsin\theta }{\begin{vmatrix}\Omega \end{vmatrix}}\,\mathrm {d} \Omega \mathrm {d} \theta } = 1 4 π 2 ∫ − π π ∫ 0 ∞ P θ ( Ω ) e j Ω ( u cos ⁡ θ + v sin ⁡ θ ) | Ω | d Ω d θ {\displaystyle ={\frac {1}{4\pi ^{2}}}\int _{-\pi }^{\pi }\int _{0}^{\infty }P_{\theta }(\Omega )e^{j}\Omega (u\cos \theta +v\sin \theta ){\begin{vmatrix}\Omega \end{vmatrix}}\,\mathrm {d} \Omega \mathrm {d} \theta } = 1 4 π 2 ∫ 0 π ( ∫ − ∞ ∞ P θ ( Ω ) | Ω | {\displaystyle ={\frac {1}{4\pi ^{2}}}\int _{0}^{\pi }(\int _{-\infty }^{\infty }P_{\theta }(\Omega ){\begin{vmatrix}\Omega \end{vmatrix}}} e j Ω u ^ d Ω ) d θ {\displaystyle e^{j\Omega {\hat {u}}}\mathrm {d} \Omega )\mathrm {d} \theta } By taking the inverse Fourier Transform and assuming g ( u ^ ) = F − 1 ( | Ω | 2 ) {\displaystyle g({\hat {u}})={\mathcal {F}}^{-1}({{\begin{vmatrix}\Omega \end{vmatrix}}^{2}})} , we get d ( u , v ) = ∑ i △ θ i [ p θ ( u ^ ) ∗ g θ i ( u ^ ) ] {\displaystyle d(u,v)=\sum _{i}\vartriangle \theta _{i}[p_{\theta }({\hat {u}})g_{\theta i}({\hat {u}})]} == Approaches == In practice, there are a wide variety of methods that are utilized, most of which are reconstruct 3-D information (volume) from 2-D signals (image). Typically used methods are CT, MRI, PET and SPECT. And the filtered back projection based on the principles introduced above are commonly applied. === Computed Tomography (CT) === In CT, a volume is formed by stacking the axial slices. The software cuts the volume in a different plane (usually orthogonal). Commonly, slice data is generated using an X-ray source that rotates around the object. X-ray sensors are positioned on the opposite side of the circle from the X-ray source. === Magnetic resonance imaging (MRI) === In MRI, energy from an oscillating magnetic field is temporarily applied to the patient at the appropriate resonance frequency. The protons (hydrogen atoms) emit a radio frequency signal which is measured by a receiving coil. The radio signal can be made to encode position information by varying the main magnetic field using gradient coils. === Positron emission tomography (PET) === The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern PET-CT scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Single-photon emission computed tomography (SPECT) === SPECT imaging is performed by using a gamma camera to acquire multiple 2-D images (projections) from multiple angles. Multiple projections are used to yield a 3-D data set. This data set may then be manipulated to show thin slices along any chosen axis of the body. SPECT is similar to PET in its use of radioactive tracer material and detection of gamma rays, while the tracers used in SPECT emit gamma radiation that is measured more directly.

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  Clipping (computer graphics)

  Clipping, in the context of computer graphics, is a method to selectively enable or disable rendering operations within a defined region of interest. Mathematically, clipping can be described using the terminology of constructive geometry. A rendering algorithm only draws pixels in the intersection between the clip region and the scene model. Lines and surfaces outside the view volume (aka. frustum) are removed. Clip regions are commonly specified to improve render performance. Pixels that will be drawn are said to be within the clip region. Pixels that will not be drawn are outside the clip region. More informally, pixels that will not be drawn are said to be "clipped." == In 2D graphics == In two-dimensional graphics, a clip region may be defined so that pixels are only drawn within the boundaries of a window or frame. Clip regions can also be used to selectively control pixel rendering for aesthetic or artistic purposes. In many implementations, the final clip region is the composite (or intersection) of one or more application-defined shapes, as well as any system hardware constraints In one example application, consider an image editing program. A user application may render the image into a viewport. As the user zooms and scrolls to view a smaller portion of the image, the application can set a clip boundary so that pixels outside the viewport are not rendered. In addition, GUI widgets, overlays, and other windows or frames may obscure some pixels from the original image. In this sense, the clip region is the composite of the application-defined "user clip" and the "device clip" enforced by the system's software and hardware implementation. Application software can take advantage of this clip information to save computation time, energy, and memory, avoiding work related to pixels that aren't visible. == In 3D graphics == In three-dimensional graphics, the terminology of clipping can be used to describe many related features. Typically, "clipping" refers to operations in the plane that work with rectangular shapes, and "culling" refers to more general methods to selectively process scene model elements. This terminology is not rigid, and exact usage varies among many sources. Scene model elements include geometric primitives: points or vertices; line segments or edges; polygons or faces; and more abstract model objects such as curves, splines, surfaces, and even text. In complicated scene models, individual elements may be selectively disabled (clipped) for reasons including visibility within the viewport (frustum culling); orientation (backface culling), obscuration by other scene or model elements (occlusion culling, depth- or "z" clipping). Sophisticated algorithms exist to efficiently detect and perform such clipping. Many optimized clipping methods rely on specific hardware acceleration logic provided by a graphics processing unit (GPU). The concept of clipping can be extended to higher dimensionality using methods of abstract algebraic geometry. === Near clipping === Beyond projection of vertices & 2D clipping, near clipping is required to correctly rasterise 3D primitives; this is because vertices may have been projected behind the eye. Near clipping ensures that all the vertices used have valid 2D coordinates. Together with far-clipping it also helps prevent overflow of depth-buffer values. Some early texture mapping hardware (using forward texture mapping) in video games suffered from complications associated with near clipping and UV coordinates. === Occlusion clipping (Z- or depth clipping) === In 3D computer graphics, "Z" often refers to the depth axis in the system of coordinates centered at the viewport origin: "Z" is used interchangeably with "depth", and conceptually corresponds to the distance "into the virtual screen." In this coordinate system, "X" and "Y" therefore refer to a conventional cartesian coordinate system laid out on the user's screen or viewport. This viewport is defined by the geometry of the viewing frustum, and parameterizes the field of view. Z-clipping, or depth clipping, refers to techniques that selectively render certain scene objects based on their depth relative to the screen. Most graphics toolkits allow the programmer to specify a "near" and "far" clip depth, and only portions of objects between those two planes are displayed. A creative application programmer can use this method to render visualizations of the interior of a 3D object in the scene. For example, a medical imaging application could use this technique to render the organs inside a human body. A video game programmer can use clipping information to accelerate game logic. For example, a tall wall or building that occludes other game entities can save GPU time that would otherwise be spent transforming and texturing items in the rear areas of the scene; and a tightly integrated software program can use this same information to save CPU time by optimizing out game logic for objects that aren't seen by the player. == Algorithms == Line clipping algorithms: Cohen–Sutherland Liang–Barsky Fast-clipping Cyrus–Beck Nicholl–Lee–Nicholl Skala O(lg N) algorithm Polygon clipping algorithms: Greiner–Hormann Sutherland–Hodgman Weiler–Atherton Vatti Rendering methodologies Painter's algorithm

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  Edits (app)

  Edits is an American photo and short form video editing software service owned by Meta Platforms. It allows users to create videos and edit them by using features like green screens, and AI animation, and also provides real-time statistics to Instagram creators to track their accounts. Accounts directly from Instagram can be imported, and videos can be exported vice-versa. It is available solely on iOS and Android. On Apple, it supports over 32 different languages, including French, Spanish, and Chinese. It has been noted by critics as a direct competitor for apps like CapCut, owned by Chinese brand ByteDance. The Instagram head, Adam Mosseri, also acknowledged these similarities. Launched on April 22 for both iOS and Android. It received over 5M+ users on Apple and Android combined in its first 4 days since its launch. == History == On January 19, 2025, following the ban of all ByteDance Apps from the Google Play Store, and App Store, Instagram head Adam Mosseri announced on Threads that they would be launching the app in February for iOS, followed by an Android counterpart. He said the app is working with select people to test its features. In a separate post, he emphasized that the app is "more for creators than casual video makers". == Features == Edits contains many similar features to other competition of video editors like KineMaster, Inshot, and CapCut. When creating a video, users have the option to export in resolution of HD, 4K, and 2K, along with having HDR and SDR support. Like many traditional video editing software, it includes a timeline, and basic undo-redo buttons. On the bottom bar, 7 tabs for editing exist, namely the Split, Volume, Adjust, Speed, Delete, Filters, Green Screen, Voice FX, Extract Audio, Mirror, Slip, Replace and Duplicate bars. Basic features, like splitting, and adjusting speed and volume of clips are present, along with more advanced Green Screens, and AI features. Being a mobile video editor app, Edits also has drag-and-drop features to ease customer usage. Users have the ability to record videos directly within the app. This feature allows users to create content without needing extra software or devices. They can choose from several focal lengths, which affect how close or wide the shot appears. The app also supports different frame rates. Users have the ability to record videos directly within the app. This feature allows users to create content without needing extra software or devices. Once users are done filming your clips, they can simply transfer them into a project to start editing immediately. Upcoming features for the app include Keyframes, AI-powered modification, Collaboration, and Enhanced creativity. == Reception == Since its release, it received over 5 million downloads in 4 days. Critically, the app received great rankings from many. From users, the app received an average of 4.45 stars over Google Play Store and App Store in the first few days, with Google Play Store receiving the least stars. As in reviews, it was received mixed by the public. Many people praised the smoothness and intuivity of the app. "The app is more than just a basic editor, offering a full suite of creative tools, including a dedicated tab for inspiration and trending audio, as well as a tab for managing drafts," said a blogger. Some users were disappointed with the range of editing tools, some users have noted that it could benefit from more transition options between clips. Some even reported crashing between clips.

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  N-World

  N-World is a 3D graphics package developed by Nichimen Graphics in the 1990s, for Silicon Graphics and Windows NT workstations. Intended primarily for video game content creation, it has polygon modeling tools, 2D and 3D paint, scripting, color reduction, and exporters for several popular game consoles. After its initial release on Windows NT, N-World was renamed Mirai. The winged edge 3D modeler in N-World inspired the development at Nichimen Graphics of Nendo, a standalone 3D modeler, which in turn inspired the open source modeler Wings 3D. == History == N-World originated with Symbolics, a computer manufacturer notable for producing Lisp-based systems in the 1980s. Among the software packages that were produced for Symbolics computers are S-Graphics, a 3D animation suite that includes modules for polygon modeling, dynamics, paint, and rendering — titled S-Geometry, S-Dynamics, S-Paint, and S-Render, respectively. In 1992, Japanese trading company Nichimen Corporation purchased the rights to S-Graphics, ported it to Silicon Graphics IRIX, and marketed it as N-World. N-World retains the Lisp-based underpinnings of its predecessor, but was targeted at interactive content producers, with features useful for game developers. It was priced at US$16,995 (equivalent to $34,100 in 2025) for the full suite, later reduced to $9,995 when ported to Windows NT in 1997. N-World was used to create graphics for many console games in the 1990s, specifically most of the Nintendo 64 games, like Super Mario 64 and Final Fantasy VII. It was superseded by Mirai in 1999. == Features == The N-World package, like its predecessor S-Graphics, is divided into several components: N-Geometry: 3D polygon-based modeling tools, including smoothing, "magnet" geometry editing, and instancing. N-Dynamics: Animation tools including scripting, curve-based animation, and skeletal animation. N-Render: Surfacing and rendering tools with ray tracing and materials output to various game console formats. N-Paint: 2D and 3D paint with mattes, effects, color reduction, and a visual VRAM editor for PlayStation. Game Tools: Utilities for game developers, including exporters for PlayStation, Nintendo 64, and Saturn consoles. == Credits == The following games were created using N-World. Rap Stars Online

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  The Future of Work and Death

  The Future of Work and Death is a 2016 documentary by Sean Blacknell and Wayne Walsh about the exponential growth of technology. The film showed at several film festivals including Raindance Film Festival, International Film Festival Rotterdam, Academia Film Olomouc and CPH:DOX. In May 2017 it received an official screening at the European Commission. It was distributed by First Run Features and Journeyman Pictures and was released on iTunes, Amazon Prime and On-demand on 9 May 2017. The film was made available on Sundance Now on 27 November 2017. A companion piece to the film, The Cost of Living, a documentary concerning universal basic income in Britain, was released on Amazon Prime on 8 October 2020. == Synopsis == World experts in the fields of futurology, anthropology, neuroscience, and philosophy consider the impact of technological advances on the two 'certainties' of human life; work and death. Charting human developments from Homo habilis, past the Industrial Revolution, to the digital age and beyond, the film looks at the shocking exponential rate at which mankind has managed to create technologies to ease the process of living. As we embark on the next phase of our adaptation, with automation and artificial intelligence signifying the complete move from man to machine, the film asks what the implications are for human fulfilment in an approaching era of job obsolescence and extreme longevity. == Cast == Dudley Sutton – Narrator Aubrey de Grey – Biomedical gerontologist and CSO of the SENS Research Foundation Will Self – Writer, journalist, political commentator and Professor of Contemporary Thought at Brunel University Rudolph E. Tanzi – Professor of Neurology at Harvard University and Director of the Genetics and Aging Research Unit at Massachusetts General Hospital (MGH) Martin Ford – Futurist and author Steve Fuller – Auguste Comte Chair in Social Epistemology at the Department of sociology at University of Warwick Murray Shanahan – Professor of Cognitive Robotics at Imperial College London Gray Scott – Futurist, executive producer of this production Vivek Wadhwa – Entrepreneur, academic and Director of Research at the Center for Entrepreneurship and Research Commercialization at the Pratt School of Engineering, Duke University Zoltan Istvan – Transhumanist and journalist Joanna Cook – Anthropologist, University College London Nicholas Kamara – Physician, Kable Hospital David Pearce – Transhumanist philosopher and co-founder of Humanity+ Peter Cochrane – Futurist and entrepreneur John Harris – Bioethicist, philosopher and Director of the Institute for Science, Ethics and Innovation at the University of Manchester Riva Melissa-Tez – Entrepreneur and transhumanist Ian Pearson – Futurologist Stuart Armstrong – Artificial intelligence researcher at Future of Humanity Institute

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  Smart speaker industry in South Korea

  Smart speakers, or AI speakers, have been developed by multiple domestic electronics and telecommunications firms in South Korea. Since their introduction to the local market in 2016, they have been used by millions of people in the country. == Brands == === Google === In September 2018, Google Home (including the Google Home Mini) launched in South Korea. Running Google Assistant, it featured simultaneous recognition of two languages among a total of seven, including Korean. At launch, it could play music from Bugs!, in addition to YouTube. === Kakao === In November 2017, Kakao launched the Kakao Mini, featuring integrated KakaoTalk functionality. === KT === KT launched the GiGA Genie smart speaker in January 2017, using a Harman Kardon speaker. In November 2017, KT announced GiGA Genie LTE, a portable AI speaker with LTE support. They also released a mini speaker called GiGA Genie Buddy. In 2018, KT created a special version of GiGa Genie with a screen for use in hotels. On 29 April 2019, KT announced the GiGA Genie Table TV, a consumer-oriented smart speaker with a display. It featured paid TV access through Wi-Fi. Based on usage data from the hotel model, KT decided not to add a touchscreen. The Table TV also featured a limited-access "personalized-text-to-speech technology" which could use parents' voice recording inputs to read children books. In February 2022, KT began rolling out Amazon Alexa integration into its speakers for English support. === Naver === In August 2017, Naver announced the Wave smart speaker, operating on Clova. In October 2017, Naver launched the Friends smart speaker, which were designed based on Line characters. ==== LG Uplus ==== In December 2017, LG Uplus launched the Friends+ speaker with Naver, operating on U+ Home AI. === Samsung === In August 2018, Samsung announced the Samsung Galaxy Home in partnership with Spotify. The original size was delayed, while the Galaxy Home Mini appeared briefly as a bonus for Samsung Galaxy S20 preorders in South Korea in February 2020. === SK Telecom === SK Telecom launched the Nugu smart speaker in September 2016, using an Astell & Kern audio system. In August 2017, SKT released a portable speaker named Nugu mini. In July 2018, SKT launched the Nugu Candle, featuring expanded mood lighting. The first-generation Nugu was subsequently discontinued. On 18 April 2019, SKT released the NUGU Nemo AI, which featured a display and JBL stereo speaker. In August 2019, SKT collaborated with SM Entertainment, incorporating functions related to the agency's artists into Nugu. In January 2022, SKT showcased the NUGU Candle SE, introducing Alexa support. == Usage == In 2018, approximately 3 million people in South Korea used smart speakers. According to data from KT in 2018, the most common commands to its speakers were for controlling televisions. Based on a broader survey in 2017, music was selected as the most frequent use case. By 2018, smart speaker companies were partnering with reading and other education services, adding potential use-cases for children. By 2022, smart speakers were being utilized by the South Korean government. SKT, in partnership with 70 regional governments, distributed smart speakers to 12,000 senior citizens living alone. The government paid for monthly subscriptions to help seniors stay mentally engaged. Naver made an agreement with the Seoul Metropolitan Government to provide Clova CareCall, an automated health checkup program to hundreds of senior citizens living alone. KT's AI care service included an emergency dispatch call function and medication notifications. == Criticism == === Communication === In a survey of 300 users in 2017, approximately half reported having some type of communication issue with their smart speakers. === Privacy === South Korean smart speakers sparked privacy concerns when they were found to be collecting and documenting user audio data in 2019. The speaker companies responded that only a minority of data was collected and that it was anonymized. They stated that such recordings were collected for performance improvements.

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  Plotting algorithms for the Mandelbrot set

  There are many programs and algorithms used to plot the Mandelbrot set and other fractals, some of which are described in fractal-generating software. These programs use a variety of algorithms to determine the color of individual pixels efficiently. == Escape time algorithm == The simplest algorithm for generating a representation of the Mandelbrot set is known as the "escape time" algorithm. A repeating calculation is performed for each x, y point in the plot area and based on the behavior of that calculation, a color is chosen for that pixel. === Unoptimized naïve escape time algorithm === In both the unoptimized and optimized escape time algorithms, the x and y locations of each point are used as starting values in a repeating, or iterating calculation (described in detail below). The result of each iteration is used as the starting values for the next. The values are checked during each iteration to see whether they have reached a critical "escape" condition, or "bailout". If that condition is reached, the calculation is stopped, the pixel is drawn, and the next x, y point is examined. For some starting values, escape occurs quickly, after only a small number of iterations. For starting values very close to but not in the set, it may take hundreds or thousands of iterations to escape. For values within the Mandelbrot set, escape will never occur. The programmer or user must choose how many iterations–or how much "depth"–they wish to examine. The higher the maximal number of iterations, the more detail and subtlety emerge in the final image, but the longer time it will take to calculate the fractal image. Escape conditions can be simple or complex. Because no complex number with a real or imaginary part greater than 2 can be part of the set, a common bailout is to escape when either coefficient exceeds 2. A more computationally complex method that detects escapes sooner, is to compute distance from the origin using the Pythagorean theorem, i.e., to determine the absolute value, or modulus, of the complex number. If this value exceeds 2, or equivalently, when the sum of the squares of the real and imaginary parts exceed 4, the point has reached escape. More computationally intensive rendering variations include the Buddhabrot method, which finds escaping points and plots their iterated coordinates. The color of each point represents how quickly the values reached the escape point. Often black is used to show values that fail to escape before the iteration limit, and gradually brighter colors are used for points that escape. This gives a visual representation of how many cycles were required before reaching the escape condition. To render such an image, the region of the complex plane we are considering is subdivided into a certain number of pixels. To color any such pixel, let c {\displaystyle c} be the midpoint of that pixel. We now iterate the critical point 0 under P c {\displaystyle P_{c}} , checking at each step whether the orbit point has modulus larger than 2. When this is the case, we know that c {\displaystyle c} does not belong to the Mandelbrot set, and we color our pixel according to the number of iterations used to find out. Otherwise, we keep iterating up to a fixed number of steps, after which we decide that our parameter is "probably" in the Mandelbrot set, or at least very close to it, and color the pixel black. In pseudocode, this algorithm would look as follows. The algorithm does not use complex numbers and manually simulates complex-number operations using two real numbers, for those who do not have a complex data type. The program may be simplified if the programming language includes complex-data-type operations. for each pixel (Px, Py) on the screen do x0 := scaled x coordinate of pixel (scaled to lie in the Mandelbrot X scale (-2.00, 0.47)) y0 := scaled y coordinate of pixel (scaled to lie in the Mandelbrot Y scale (-1.12, 1.12)) x := 0.0 y := 0.0 iteration := 0 max_iteration := 1000 while (xx + yy ≤ 22 AND iteration < max_iteration) do xtemp := xx - yy + x0 y := 2xy + y0 x := xtemp iteration := iteration + 1 color := palette[iteration] plot(Px, Py, color) Here, relating the pseudocode to c {\displaystyle c} , z {\displaystyle z} and P c {\displaystyle P_{c}} : z = x + i y {\displaystyle z=x+iy\ } z 2 = x 2 + 2 i x y {\displaystyle z^{2}=x^{2}+2ixy} - y 2 {\displaystyle y^{2}\ } c = x 0 + i y 0 {\displaystyle c=x_{0}+iy_{0}\ } and so, as can be seen in the pseudocode in the computation of x and y: x = R e ⁡ ( z 2 + c ) = x 2 − y 2 + x 0 {\displaystyle x=\mathop {\mathrm {Re} } (z^{2}+c)=x^{2}-y^{2}+x_{0}} and y = I m ⁡ ( z 2 + c ) = 2 x y + y 0 . {\displaystyle y=\mathop {\mathrm {Im} } (z^{2}+c)=2xy+y_{0}.\ } To get colorful images of the set, the assignment of a color to each value of the number of executed iterations can be made using one of a variety of functions (linear, exponential, etc.). One practical way, without slowing down calculations, is to use the number of executed iterations as an entry to a palette initialized at startup. If the color table has, for instance, 500 entries, then the color selection is n mod 500, where n is the number of iterations. === Optimized escape time algorithms === The code in the previous section uses an unoptimized inner while loop for clarity. In the unoptimized version, one must perform five multiplications per iteration. To reduce the number of multiplications the following code for the inner while loop may be used instead: x2:= 0 y2:= 0 w:= 0 while (x2 + y2 ≤ 4 and iteration < max_iteration) do x:= x2 - y2 + x0 y:= w - x2 - y2 + y0 x2:= x x y2:= y y w:= (x + y) (x + y) iteration:= iteration + 1 The above code works via some algebraic simplification of the complex multiplication: ( i y + x ) 2 = − y 2 + 2 i y x + x 2 = x 2 − y 2 + 2 i y x {\displaystyle {\begin{aligned}(iy+x)^{2}&=-y^{2}+2iyx+x^{2}\\&=x^{2}-y^{2}+2iyx\end{aligned}}} Using the above identity, the number of multiplications can be reduced to three instead of five. The above inner while loop can be further optimized by expanding w to w = x 2 + 2 x y + y 2 {\displaystyle w=x^{2}+2xy+y^{2}} Substituting w into y = w − x 2 − y 2 + y 0 {\displaystyle y=w-x^{2}-y^{2}+y_{0}} yields y = 2 x y + y 0 {\displaystyle y=2xy+y_{0}} and hence calculating w is no longer needed. The further optimized pseudocode for the above is: x:= 0 y:= 0 x2:= 0 y2:= 0 while (x2 + y2 ≤ 4 and iteration < max_iteration) do x2:= x x y2:= y y y:= 2 x y + y0 x:= x2 - y2 + x0 iteration:= iteration + 1 Note that in the above pseudocode, 2 x y {\displaystyle 2xy} seems to increase the number of multiplications by 1, but since 2 is the multiplier the code can be optimized via ( x + x ) y {\displaystyle (x+x)y} . == Coloring algorithms == In addition to plotting the set, a variety of algorithms have been developed to efficiently color the set in an aesthetically pleasing way show structures of the data (scientific visualisation) === Histogram coloring === A more complex coloring method involves using a histogram which pairs each pixel with said pixel's maximum iteration count before escape/bailout. This method will equally distribute colors to the same overall area, and, importantly, is independent of the maximum number of iterations chosen. This algorithm has four passes. The first pass involves calculating the iteration counts associated with each pixel (but without any pixels being plotted). These are stored in an array IterationCounts[x][y], where x and y are the x and y coordinates of said pixel on the screen respectively. The first step of the second pass is to create an array NumIterationsPerPixel[n], where the array size n is the maximum iteration count. Next, one must iterate over the array of pixel-iteration count pairs IterationCounts[x][y], and retrieve each pixel's saved iteration count, i, via e.g. i = IterationCounts[x][y]. After each pixel's iteration count i is retrieved, it is necessary to index the NumIterationsPerPixel array at i and increment the indexed value (which is initially zero) -- e.g. NumIterationsPerPixel[i] = NumIterationsPerPixel[i] + 1. for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do i:= IterationCounts[x][y] NumIterationsPerPixel[i]++ The third pass iterates through the NumIterationsPerPixel array and adds up all the stored values, saving them in total. The array index represents the number of pixels that reached that iteration count before bailout. total: = 0 for (i = 0; i < max_iterations; i++) do total += NumIterationsPerPixel[i] After this, the fourth pass begins and all the values in the IterationCounts array are indexed, and, for each iteration count i, associated with each pixel, the count is added to a global sum of all the iteration counts from 1 to i in the NumIterationsPerPixel array . This value is then normalized by dividing the sum by the total value computed earlier. hue[][]:= 0.0 for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do iteration:= Iteration

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  Arattai

  Arattai Messenger (or simply Arattai) is an encrypted messaging service for instant messaging, voice calls, and video calls, developed by Zoho Corporation. The name Arattai means "chat" or "conversation" in Tamil. The app was soft-launched in January 2021. The app saw a sharp surge in downloads in September 2025, partially fueled by endorsements from Indian government officials. However, the app dropped from the top rankings in October 2025. == History == Arattai was initially tested internally among Zoho employees before being released publicly in early 2021. The launch coincided with a surge in interest for privacy-focused and messaging services, triggered by concerns over WhatsApp's updated terms of service. In September 2025, Arattai experienced a major surge in adoption, with daily sign-ups reportedly increasing 100-fold, from around 3,000 to more than 350,000 in three days. The surge in downloads was attributed to Zoho products being promoted by Indian government officials as part of their Make in India push for homegrown alternatives to foreign‐owned apps, amid deteriorating India–US relations. The growth temporarily strained Zoho's infrastructure, prompting rapid scaling of servers and capacity expansion. During the same period, the app reached the top position in Apple's App Store charts for the "Social Networking" category in India. The app dropped from the top ranking in late October 2025. == Reception == At launch, Arattai was positioned as a potential domestic rival to WhatsApp in India, but analysts noted that it faced challenges with encryption, ecosystem, and network effect. Critics pointed to occasional sync delays.

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