AI Content Creator Course Review

AI Content Creator Course Review — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing (CMAA) is an antialiasing technique originally developed by Filip Strugar at Intel. CMAA is an image-based, post processing technique similar to that of morphological antialiasing. CMAA uses 4 main steps which are image analysis for color discontinuities, locally dominant edge detection, simple shape handling, and lastly symmetrical long edge shape handling. A couple of years after CMAA was introduced, Intel unveiled an updated version which they named CMAA2.

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  • Figure AI

    Figure AI

    Figure AI, Inc. is an American robotics company developing humanoid robots that operate via artificial intelligence. The company was founded in 2022 by Brett Adcock. As of late 2025, the company has a $39 billion valuation. Three generations of humanoid robots (Figure 01–03) have been developed, as well as two iterations of a vision-language-action model (Helix 01–02), which can control up to two robots at once. By 2026, the robots demonstrated the potential ability to perform household work and the company gained publicity when a Figure 03 appeared at a White House event. == History == Figure AI was founded in 2022 by Brett Adcock, also known for founding Archer Aviation and Vettery. That year, the company introduced its prototype, Figure 01, a bipedal robot designed for manual labor, initially targeting the logistics and warehousing sectors. The initial model utilized external cabling for easier maintenance. In May 2023, Figure AI raised $70 million from investors including Adcock, who invested $20 million, and Parkway Venture Capital. In January 2024, Figure AI announced a partnership with BMW to deploy humanoid robots in automotive manufacturing facilities. In February 2024, Figure AI secured $675 million in venture capital funding from a consortium that includes Jeff Bezos, Microsoft, Nvidia, Intel, and the startup-funding divisions of Amazon and OpenAI; the company was then valued at $2.6 billion. Figure AI also announced a partnership with OpenAI, which would build specialized artificial intelligence (AI) models for Figure AI's humanoid robots, enabling its robots to process language; the collaboration ended after a year, with Adcock stating that large language models had become a smaller problem compared to those allowing for "high rate robot control". In August 2024, the company introduced Figure 02, describing it as the next step toward deploying humanoids for industrial use. The machine has 35 degrees of freedom (DOF), while the five-fingered hands have 16 DOF and the ability to carry up to 25 kilograms (55 lb). The model is equipped with cabling integrated into the limbs, a torso-placed battery, six RGB cameras, and an onboard vision-language-action (VLA) model. It has three times the computing power (including inference AI) of the previous model, including two graphics processing units, supported by Nvidia. Microphones, speakers, and custom AI models (developed with OpenAI) enable communication with humans. In early 2025, Figure AI announced BotQ, a manufacturing facility aiming to produce 12,000 humanoids per year with the help of its own humanoid robots, and Helix, a VLA model that can control up to two robots at once. Helix enables a robot to interact with the world without extensive manual training, according to the company allowing it to pick up nearly any small household object. By April, the company issued cease-and-desist letters to at least two secondary brokers promoting its private stock without authorization. In September, a third round of financing exceeded $1 billion, raising the company's total valuation to $39 billion. Investors included Brookfield Asset Management, Intel, Macquarie Capital, Nvidia, Parkway Venture Capital, Qualcomm, Salesforce, and T-Mobile. In October 2025, Figure 03 was introduced. According to the company, its hardware and software redesign aims to create a general-purpose robot able to learn directly from humans. An upgraded camera system delivers twice the frame rate, a quarter the latency, and a 60% wider field of view, in addition to a camera in each hand. Tactile sensors in the fingertips can detect forces as little as 3 grams (0.1 oz). It incorporates soft materials and a protected battery for safety, and removable, washable textiles. It supports wireless inductive charging. In November 2025, the former head of product safety sued the company on the basis of being fired for raising the concern that the company's robots were strong enough to fracture a human skull. By early 2026, Figure 02 had been used in demonstrations showing that it could load a washing machine, sort packages, and fold laundry. That January, Helix 02 was released, expanding the AI model to the entire body to allow for functional autonomy. A Helix 02–powered Figure 02 was shown to be capable of loading and unloading a dishwasher, based on hours of motion-capture data and simulation-based machine learning. In March, U.S. First Lady Melania Trump appeared at the White House with a Figure 03, promoting the presumptive eventual ability of AI to teach children. In May 2026, Figure AI livestreamed a group of their robots processing packages nonstop for almost a week, inspiring a 10-hour competition between their robot and a human, in which the robot performed 98.5% as well as the human.

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  • Softwarp

    Softwarp

    Softwarp is a software technique to warp an image so that it can be projected on a curved screen. This can be done in real time by inserting the softwarp as a last step in the rendering cycle. The problem is to know how the image should be warped to look correct on the curved screen. There are several techniques to auto calibrate the warping by projecting a pattern and using cameras and/or sensors. The information from the sensors is sent to the software so that it can analyze the data and calculate the curvature of the projection screen. == Usage == The softwarp can be used to project virtual views on curved walls and domes. These are usually used in vehicle simulators, for instance boat-, car- and airplane simulators. To make it possible to cover a dome with a 360 degree view you need to use several projectors. A problem with using several projectors on the same screen is that the edges between the projected images get about twice the amount of light. This is solved by using a technique called edge blending. With this technique a “filter” is inserted on the edge that fades the image from 100% light strength (luminance) to 0% (the lowest luminance depends on the contrast ratio of the projector). == History == The first warping technologies used a hardware image processing unit to warp the image. This processing unit was inserted between the graphics card and the projector. The problem with this technique is that it depends on the type of signal and the quality of the signal from the graphics card to warp it correctly. The process unit also needs several lines of image information before it can start sending out the warped image. This adds a latency to the display system that could be a problem in simulators that need fast response time, for instance fighter jet simulators. Softwarping eliminates the latency.

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  • Anthrobotics

    Anthrobotics

    Anthrobotics is the science of developing and studying robots that are either entirely or in some way human-like. The term anthrobotics was originally coined by Mark Rosheim in a paper entitled "Design of An Omnidirectional Arm" presented at the IEEE International Conference on Robotics and Automation, May 13–18, 1990, pp. 2162–2167. Rosheim says he derived the term from "...Anthropomorphic and Robotics to distinguish the new generation of dexterous robots from its simple industrial robot forebears." The word gained wider recognition as a result of its use in the title of Rosheim's subsequent book Robot Evolution: The Development of Anthrobotics, which focussed on facsimiles of human physical and psychological skills and attributes. However, a wider definition of the term anthrobotics has been proposed, in which the meaning is derived from anthropology rather than anthropomorphic. This usage includes robots that respond to input in a human-like fashion, rather than simply mimicking human actions, thus theoretically being able to respond more flexibly or to adapt to unforeseen circumstances. This expanded definition also encompasses robots that are situated in social environments with the ability to respond to those environments appropriately, such as insect robots, robotic pets, and the like. Anthrobotics is now taught at some universities, encouraging students not only to design and build robots for environments beyond current industrial applications, but also to speculate on the future of robotics that are embedded in the world at large, as mobile phones and computers are today. In 2016 philosopher Luis de Miranda created the Anthrobotics Cluster at the University of Edinburgh "a platform of cross-disciplinary research that seeks to investigate some of the biggest questions that will need to be answered" on the relationship between humans, robots and intelligent systems and "a think tank on the social spread of robotics, and also how automation is part of the definition of what humans have always been". to explore the symbiotic relationship between humans and automated protocols.

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  • Real-Time UML

    Real-Time UML

    Real-Time UML (RTUML) refers to the application of the Unified Modelling Language (UML) for the analysis, design, and implementation of real-time and embedded systems, where timing constraints, concurrency, and resource management are critical. It extends standard UML with profiles, notations, and semantics to handle hard and soft real-time requirements, such as modelling predictable response times and fault tolerance. RTUML is not a separate language but a methodology leveraging UML diagrams (e.g., statecharts, sequence diagrams) for time-sensitive applications like automotive controls, avionics, and medical devices. The term is closely associated with Bruce Powel Douglass, who popularised it through his books and the Harmony process for embedded software development. As of 2025, RTUML remains relevant in industries requiring certified systems, though its adoption varies with agile methodologies and model-driven engineering tools. == Background == Real-Time UML emerged in the late 1990s as UML was standardized by the Object Management Group (OMG) in 1997, addressing the need for object-oriented modeling in real-time systems previously dominated by procedural languages like C. Traditional real-time development relied on "bare metal" programming or theoretical models, but RTUML introduced visual notations for object structure, behaviour, and timing. Bruce Powel Douglass’s 1999 book, Real-Time UML: Developing Efficient Objects for Embedded Systems, formalised the approach, emphasising statecharts for concurrency and timing constraints. Later editions (2004, 2006) incorporated UML 2.0 features like activity and timing diagrams, aligning with OMG’s Real-Time Profile (now part of MARTE—Modelling and Analysis of Real-Time and Embedded Systems). The Harmony process integrates RTUML with executable models for simulation and code generation. RTUML addresses hard real-time systems (e.g., strict deadlines in avionics) versus soft real-time (e.g., media streaming), using UML extensions for schedulability analysis. == Key concepts == RTUML adapts UML diagrams and techniques for real-time needs: Statecharts and Behaviour Modelling: Extended state machines model reactive behaviour, using and-states for concurrency, pseudostates for transitions, and timing constraints (e.g., {duration < 10ms}). Examples include cardiac pacemaker models. Sequence and Interaction Diagrams: Capture message timing, priorities, and resource allocation in multi-threaded systems. Architectural Patterns: Define logical and physical architectures with active objects for concurrency and patterns like observer or publisher-subscriber. Timing and Constraints: Use Object Constraint Language (OCL) for specifying deadlines and priorities. Profiles and Extensions: OMG’s UML Profile for Schedulability, Performance, and Time (SPT) and MARTE add stereotypes like RT::ActiveObject. These support iterative development, from requirements to deployment, often with tools like IBM Rhapsody or Enterprise Architect. == Applications == RTUML is used in: Embedded Systems: Modelling automotive ECUs or UAV controls. Avionics and Defence: DO-178C-compliant designs for fault tolerance. Medical Devices: Pacemakers or ventilators with precise timing. Industrial Automation: RTOS task visualisation via sequence diagrams. Tools like IBM Rhapsody support RTUML for model-based development and code generation in C/C++. == Criticism and adoption == RTUML’s complexity can overwhelm simple systems, and its use in agile environments is limited, where lightweight diagrams are preferred. Surveys indicate UML (including RTUML) is used in 30–50% of embedded projects, often for documentation rather than full model-driven engineering. It remains standard in academia and certified industries like aerospace.

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  • Paint.NET

    Paint.NET

    Paint.NET (sometimes stylized as paint.net) is a freeware general-purpose raster graphics editor program for Microsoft Windows, developed with the .NET platform. Paint.NET was originally created by Rick Brewster as a Washington State University student project, and has evolved from a simple replacement for the Microsoft Paint program into a program for editing mainly graphics, with support for plugins. == History == Paint.NET originated as a computer science senior design project by Rick Brewster during spring 2004 at Washington State University. Version 1.0 consisted of 36,000 lines of code and was written in four months. In contrast, version 3.35 has approximately 162,000 lines of code. The Paint.NET project continued over the summer and into the autumn 2004 semester for both the version 1.1 and 2.0 releases. Development continued with one programmer who worked on previous versions of Paint.NET while he was a student at WSU. As of May 2006 the program had been downloaded at least 2 million times, at a rate of about 180,000 per month. Initially, Paint.NET was released under a modified version of the MIT License, with the exclusion of the installer, text, and graphics. However, citing issues with the open source code being plagiarized by others that had rebranded the software as their own and bundled user content without their permission, the availability of the source code was restricted, in December 2007 Brewster announced his intent to restrict access to components of the program (including its installer, resources, and user interface). In November 2009, the software was made proprietary, restricting the sale or creation of derivative works of the software. Starting with version 4.0.18, Paint.NET is published in two editions: A classic edition remains freeware, similar to all other versions since 3.5. Another edition, however, is published to Microsoft Store under a trialware license and is available to purchase for US$14.99. According to the developer, this was done to enable the users to contribute to the development with more convenience, even though the old avenue of donation was not closed. In May 2026, Brewster revealed that he obtained the paint.net domain after attempting to do so for 22 years. Historically, the editor was hosted on getpaint.net, and according to Brewster, the previous owners of paint.net would not sell the domain and asked for "lots and lots of money". In December of the previous year, paint.net began hosting content that impersonated Paint.NET, therefore becoming a clear case of trademark infringement and domain squatting. Brewster stated that he was able to obtain the domain afterwards with the help of a lawyer. == Overview == Paint.NET is primarily programmed in the C# programming language. Its native image format, .PDN, is a compressed representation of the application's internal object format, which preserves layering and other information. == Plugins == Paint.NET supports plugins, which add image adjustments, effects, and support for additional file types. They can be programmed using any .NET Framework programming language, though they are most commonly written in C#. These are created by volunteer coders on the program's discussion board, the Paint.NET Forum. Though most are simply published via the discussion board, some have been included with a later release of the program. For instance, a DirectDraw Surface file type plugin, (originally by Dean Ashton) and an Ink Sketch and Soften Portrait effect (originally by David Issel) were added to Paint.NET in version 3.10. Hundreds of plugins have been produced; such as Shape3D, which renders a 2D drawing into a 3D shape. Some plugins expand on the functionality that comes with Paint.NET, such as Curves+ and Sharpen+, which extend the included tools Curves and Sharpen, respectively. Examples of file type plugins include an Animated Cursor and Icon plugin and an Adobe Photoshop file format plugin. Several of these plugins are based on existing open source software, such as a raw image format plugin that uses dcraw and a PNG optimization plugin that uses OptiPNG. == Forks == === paint-mono === Paint.NET was created exclusively for Windows and has no native support for other operating systems. Due to its former open-source licensing, the development of alternative versions was possible. In May 2007, Miguel de Icaza officially started a porting project called paint-mono. This project had partially ported Paint.NET 3.0 to Mono, an open-source implementation of the Common Language Infrastructure on which the .NET Framework is based. This allowed Paint.NET to be run on Mono-supported platforms, such as Linux. This port is no longer maintained and has not been updated since March 2009. Newer Mono runtime 6 versions are able to run original Paint.NET releases up to 3.5.11 with only minor issues. === Pinta === In 2010, developer Jonathan Pobst started a project called Pinta, describing it as a clone of Paint.NET for Mono and Gtk#. Pinta reused the adjustments and effects code from Paint.NET but otherwise is original code.

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  • ViEWER

    ViEWER

    ViEWER, the Virtual Environment Workbench for Education and Research, is a proprietary, freeware computer program for Microsoft Windows written by researchers at the University of Idaho for the study of visual perception and complex immersive three-dimensional environments. It was created using C++ and OpenGL, and has been used by Dr. Brian Dyre, Dr. Steffen Werner, Dr. Ernesto Bustamante, Dr. Ben Barton, and their undergraduate and graduate researchers in visual perception, signal detection, and child-safety experiments.

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  • Cybernetics

    Cybernetics

    Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent actions. It is concerned with general principles that are relevant across multiple contexts, including engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character means that it intersects with a number of other fields, resulting in a wide influence and diverse interpretations. The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide. Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture. == Definitions == Cybernetics has been defined in a variety of ways, reflecting "the richness of its conceptual base". One of the best known definitions is that of the American scientist Norbert Wiener, who characterised cybernetics as concerned with "control and communication in the animal and the machine". Another early definition is that of the Macy cybernetics conferences, where cybernetics was understood as the study of "circular causal and feedback mechanisms in biological and social systems". Margaret Mead emphasised the role of cybernetics as "a form of cross-disciplinary thought which made it possible for members of many disciplines to communicate with each other easily in a language which all could understand". Other definitions include: "the art of governing or the science of government" (André-Marie Ampère); "the art of steersmanship" (Ross Ashby); "the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control" (Andrey Kolmogorov); and "a branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect" (Gregory Bateson). == Etymology == The Ancient Greek term κυβερνητικός (kubernētikos, '(good at) steering') appears in Plato's Republic and Alcibiades, where the metaphor of a steersman is used to signify the governance of people. The French word cybernétique was also used in 1834 by the physicist André-Marie Ampère to denote the sciences of government in his classification system of human knowledge. According to Norbert Wiener, the word cybernetics was coined by a research group involving himself and Arturo Rosenblueth in the summer of 1947. It has been attested in print since at least 1948 through Wiener's book Cybernetics: Or Control and Communication in the Animal and the Machine. In the book, Wiener states: After much consideration, we have come to the conclusion that all the existing terminology has too heavy a bias to one side or another to serve the future development of the field as well as it should; and as happens so often to scientists, we have been forced to coin at least one artificial neo-Greek expression to fill the gap. We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman. Moreover, Wiener explains, the term was chosen to recognize James Clerk Maxwell's 1868 publication on feedback mechanisms involving governors, noting that the term governor is also derived from κυβερνήτης (kubernḗtēs) via a Latin corruption gubernator. Finally, Wiener motivates the choice by steering engines of a ship being "one of the earliest and best-developed forms of feedback mechanisms". == History == === First wave === The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems. Two foundational articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow – based on the research on living organisms that Rosenblueth did in Mexico – and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts. The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants that included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener. In the UK, similar focuses were explored by the Ratio Club, an informal dining club of young psychiatrists, psychologists, physiologists, mathematicians and engineers that met between 1949 and 1958. Wiener introduced the neologism cybernetics to denote the study of "teleological mechanisms" and popularized it through the book Cybernetics: Or Control and Communication in the Animal and the Machine. During the 1950s, cybernetics was developed as a primarily technical discipline, such as in Qian Xuesen's 1954 "Engineering Cybernetics". The text was quickly translated into multiple languages and became a foundational text on automation. In the Soviet Union, Cybernetics was initially considered with suspicion but became accepted from the mid to late 1950s. By the 1960s and 1970s, however, cybernetics' transdisciplinarity fragmented, with technical focuses separating into separate fields. Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop in 1956, differentiating itself from the broader cybernetics field. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed. Similarly, computer science became defined as a distinct academic discipline in the 1950s and early 1960s. === Second wave === The second wave of cybernetics came to prominence from the 1960s onwards, with its focus shifting away from technology toward social, ecological, and philosophical concerns. It was still grounded in biology, notably Maturana and Varela's autopoiesis, and built on earlier work on self-organising systems and the presence of anthropologists Mead and Bateson in the Macy meetings. The Biological Computer Laboratory, founded in 1958 and active until the mid-1970s under the direction of Heinz von Foerster at the University of Illinois at Urbana–Champaign, was a major incubator of this trend in cybernetics research. Focuses of the second wave of cybernetics included management cybernetics, such as Stafford Beer's biologically inspired viable system model; work in family therapy, drawing on Bateson; social systems, such as in the work of Niklas Luhmann; epistemology and pedagogy, such as in the development of radical constructivism. Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion, notably in Mead's invocation at the inaugural meeting of the American Society for Cybernetics (ASC) to apply cybernetics to the activities of the ASC itself. This focus on reflexivity was especially prominent in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition, epistemology, and ethics. The 1960s onwards also saw cybernetics begin to develop exchanges with the creative arts, design, and architecture, notably with the Cybernetic Serendipity exhibition (ICA, London, 1968), curated by Jasia Reichardt, and the unrealised Fun Palace project (London, unrealised, 1964 onwards), where Gordon Pask was consultant to architect Cedric Price and theatre director Joan Littlewood. In 1962, Qian Xuesen recruited Song Jian and Guan Zhaozhi to establish China's first cybernetics laboratory with him. Following the Sino-Soviet split, cybernetics was deemed disreputable in China. The field was again favored in the 1970s and 1980s following Deng Xiaoping's emphasis on modernisation. === Third wave === From the 1990s onwards, there has been a renewed interest in cybernetics from a number of directions. Early cybernetic work on artificial neural networks has been returned to as a paradigm in machine learning and artifi

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  • Tango (platform)

    Tango (platform)

    Tango (named Project Tango while in testing) was an augmented reality computing platform, developed and authored by the Advanced Technology and Projects (ATAP), a skunkworks division of Google. It used computer vision to enable mobile devices, such as smartphones and tablets, to detect their position relative to the world around them without using GPS or other external signals. This allowed application developers to create user experiences that include indoor navigation, 3D mapping, physical space measurement, environmental recognition, augmented reality, and windows into a virtual world. The first product to emerge from ATAP, Tango was developed by a team led by computer scientist Johnny Lee, a core contributor to Microsoft's Kinect. In an interview in June 2015, Lee said, "We're developing the hardware and software technologies to help everything and everyone understand precisely where they are, anywhere." Google produced two devices to demonstrate the Tango technology: the Peanut phone and the Yellowstone 7-inch tablet. More than 3,000 of these devices had been sold as of June 2015, chiefly to researchers and software developers interested in building applications for the platform. In the summer of 2015, Qualcomm and Intel both announced that they were developing Tango reference devices as models for device manufacturers who use their mobile chipsets. At CES, in January 2016, Google announced a partnership with Lenovo to release a consumer smartphone during the summer of 2016 to feature Tango technology marketed at consumers, noting a less than $500 price-point and a small form factor below 6.5 inches. At the same time, both companies also announced an application incubator to get applications developed to be on the device on launch. On 15 December 2017, Google announced that they would be ending support for Tango on March 1, 2018, in favor of ARCore. == Overview == Tango was different from other contemporary 3D-sensing computer vision products, in that it was designed to run on a standalone mobile phone or tablet and was chiefly concerned with determining the device's position and orientation within the environment. The software worked by integrating three types of functionality: Motion-tracking: using visual features of the environment, in combination with accelerometer and gyroscope data, to closely track the device's movements in space Area learning: storing environment data in a map that can be re-used later, shared with other Tango devices, and enhanced with metadata such as notes, instructions, or points of interest Depth perception: detecting distances, sizes, and surfaces in the environment Together, these generate data about the device in "six degrees of freedom" (3 axes of orientation plus 3 axes of position) and detailed three-dimensional information about the environment. Project Tango was also the first project to graduate from Google X in 2012 Applications on mobile devices use Tango's C and Java APIs to access this data in real time. In addition, an API was also provided for integrating Tango with the Unity game engine; this enabled the conversion or creation of games that allow the user to interact and navigate in the game space by moving and rotating a Tango device in real space. These APIs were documented on the Google developer website. == Applications == Tango enabled apps to track a device's position and orientation within a detailed 3D environment, and to recognize known environments. This allowed the creations of applications such as in-store navigation, visual measurement and mapping utilities, presentation and design tools, and a variety of immersive games. At Augmented World Expo 2015, Johnny Lee demonstrated a construction game that builds a virtual structure in real space, an AR showroom app that allows users to view a full-size virtual automobile and customize its features, a hybrid Nerf gun with mounted Tango screen for dodging and shooting AR monsters superimposed on reality, and a multiplayer VR app that lets multiple players converse in a virtual space where their avatar movements match their real-life movements. Tango apps are distributed through Play. Google has encouraged the development of more apps with hackathons, an app contest, and promotional discounts on the development tablet. == Devices == As a platform for software developers and a model for device manufacturers, Google created two Tango devices. === The Peanut phone === "Peanut" was the first production Tango device, released in the first quarter of 2014. It was a small Android phone with a Qualcomm MSM8974 quad-core processor and additional special hardware including a fisheye motion camera, "RGB-IR" camera for color image and infrared depth detection, and Movidius Vision processing units. A high-performance accelerometer and gyroscope were added after testing several competing models in the MARS lab at the University of Minnesota. Several hundred Peanut devices were distributed to early-access partners including university researchers in computer vision and robotics, as well as application developers and technology startups. Google stopped supporting the Peanut device in September 2015, as by then the Tango software stack had evolved beyond the versions of Android that run on the device. === The Yellowstone tablet === "Yellowstone" was a 7-inch tablet with full Tango functionality, released in June 2014, and sold as the Project Tango Tablet Development Kit. It featured a 2.3 GHz quad-core Nvidia Tegra K1 processor, 128GB flash memory, 1920x1200-pixel touchscreen, 4MP color camera, fisheye-lens (motion-tracking) camera, an IR projector with RGB-IR camera for integrated depth sensing, and 4G LTE connectivity. As of May 27, 2017, the Tango tablet is considered officially unsupported by Google. ==== Testing by NASA ==== In May 2014, two Peanut phones were delivered to the International Space Station to be part of a NASA project to develop autonomous robots that navigate in a variety of environments, including outer space. The soccer-ball-sized, 18-sided polyhedral SPHERES robots were developed at the NASA Ames Research Center, adjacent to the Google campus in Mountain View, California. Andres Martinez, SPHERES manager at NASA, said "We are researching how effective [Tango's] vision-based navigation abilities are for performing localization and navigation of a mobile free flyer on ISS. === Intel RealSense smartphone === Announced at Intel's Developer Forum in August 2015, and offered to public through a Developer Kit since January 2016. It incorporated a RealSense ZR300 camera which had optical features required for Tango, such as the fisheye camera. === Lenovo Phab 2 Pro === Lenovo Phab 2 Pro was the first commercial smartphone with the Tango Technology, the device was announced at the beginning of 2016, launched in August, and available for purchase in the US in November. The Phab 2 Pro had a 6.4 inch screen, a Snapdragon 652 processor, and 64 GB of internal storage, with a rear facing 16 Megapixels camera and 8 MP front camera. === Asus Zenfone AR === Asus Zenfone AR, announced at CES 2017, was the second commercial smartphone with the Tango Technology. It ran Tango AR & Daydream VR on Snapdragon 821, with 6GB or 8GB of RAM and 128 or 256GB of internal memory depending on the configuration.

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  • Arattai

    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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  • Georges Giralt PhD Award

    Georges Giralt PhD Award

    The Georges Giralt PhD Award is a European scientific prize for extraordinary contributions to robotics. It is awarded yearly at the European Robotics Forum by euRobotics AISBL, a non-profit organisation based in Brussels with the objective of turning robotics beneficial for Europe’s economy and society. Georges Giralt received his PhD in 1958, from Paul Sabatier University, in the domain of electrical machines, and soon afterwards became a pioneer in robotics, in Europe and worldwide. He was especially instrumental in bringing in scientific foundations and methodology when the domain was still young, and a loose coupling of mechanical and electrical engineering, adopting the early results of automatic control. The high reputation of the Georges Giralt PhD Award is based on the prominent role of the awarding institution euRobotics. With more than 250 member organisations, euRobotics represents the academic and industrial robotics community in Europe. Moreover, it provides the European robotics community with a legal entity to engage in a public/private partnership with the European Commission. The award is covered by various media. Entitled for participation in the Georges Giralt PhD Award are all robotics-related dissertations which have been successfully defended at a European university. The US-American counterpart is the Dick Volz Award. == Award winners == 2026: Antonio González Morgado 2025: Erfan Shahriari 2024: Manuel Keppler 2023: Antonio Andriella, Ribin Balachandran 2022: Antonio Loquercio, Michael Lutter 2021: Giuseppe Averta, Bernd Henze 2020: Cosimo Della Santina 2019: Grazioso Stanislao, Teodor Tomic 2018: Frank Bonnet, Daniel Leidner 2017: Johannes Englsberger 2016: Alexander Dietrich, Mark Müller 2015: Jörg Stückler 2014: Manuel Catalano, Fabien Expert, Rainer Jaekel 2013: Jens Kober 2012: Sami Haddadin 2011: Mario Pratts 2010: Ludovic Righetti 2009: Alejandro-Dizan Vasquez-Govea 2008: Cyrill Stachniss, Eduardo Rocon 2007: Pierre Lamon 2006: Martijn Wisse 2005: Juan Andrade Cetto 2004: Gilles Duchemin 2003: Ralf Koeppe 2002: Gianluca Antonelli, Jens-Steffen Gutmann

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  • Read Along

    Read Along

    Read Along, formerly known as Bolo, is an Android language-learning app for children developed by Google for the Android operating system. The application was released on the Play Store on March 7, 2019. It features a character named Diya helping children learn to read through illustrated stories. It has the facility to learn English and Indian major languages i.e. Hindi, Bengali, Tamil, Telugu, Marathi and Urdu, as well as Spanish, Portuguese and Arabic. == Technology == The app uses text-to-speech technology, through which the character named Dia reads the story, as well as speech-to-text technology, which mechanically identifies the matches between the text and the reading of the user. The story of Chhota Bheem and Katha Kids was added in September 2019. In April 2020, a new version of the application was released. In September 2020, it added Arabic language to its language option. A web version was launched in August 2022.

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  • Bright Computing

    Bright Computing

    Bright Computing, Inc. was a developer of software for deploying and managing high-performance (HPC) clusters, Kubernetes clusters, and OpenStack private clouds in on-premises data centers as well as in the public cloud. In 2022, it was acquired by Nvidia. == History == Bright Computing was founded by Matthijs van Leeuwen in 2009, who spun the company out of ClusterVision, which he had co-founded with Alex Ninaber and Arijan Sauer. Alex and Matthijs had worked together at UK’s Compusys, which was one of the first companies to commercially build HPC clusters. They left Compusys in 2002 to start ClusterVision in the Netherlands, after determining there was a growing market for building and managing supercomputer clusters using off-the-shelf hardware components and open source software, tied together with their own customized scripts. ClusterVision also provided delivery and installation support services for HPC clusters at universities and government entities. In 2004, Martijn de Vries joined ClusterVision and began development of cluster management software. The software was made available to customers in 2008, under the name ClusterVisionOS v4. In 2009, Bright Computing was spun out of ClusterVision. ClusterVisionOS was renamed Bright Cluster Manager, and van Leeuwen was named Bright Computing’s CEO. In February 2016, Bright appointed Bill Wagner as chief executive officer. Matthijs van Leeuwen became chief strategy officer, and then left the company and board of directors in 2018. In January 2022 Bright was acquired by Nvidia. Nvidia cited using Bright's Amsterdam facility as a development center. The acquisition occurred after several layoffs under Bill Wagner. == Customers == Early customers included Boeing, Sandia National Laboratories, Virginia Tech, Hewlett Packard, NSA, and Drexel University. Many early customers were introduced through resellers, including SICORP, Cray, Dell, and Advanced HPC. As of 2019, the company had more than 700 customers, including more than fifty Fortune 500 Companies. == Products and services == Bright Cluster Manager for HPC lets customers deploy and manage complete clusters. It provides management for the hardware, the operating system, the HPC software, and users. In 2014, the company announced Bright OpenStack, software to deploy, provision, and manage OpenStack-based private cloud infrastructures. In 2016, Bright started bundling several machine learning frameworks and associated tools and libraries with the product, to make it very easy to get machine learning workload up and running on a Bright cluster. In December 2018, version 8.2 was released, which introduced support for the ARM64 architecture, edge capabilities to build clusters spread out over many different geographical locations, improved workload accounting & reporting features, as well as many improvements to Bright's integration with Kubernetes. Bright Cluster Manager software was frequently sold through original equipment manufacturer (OEM) resellers, including Dell and HPE. In version 10, Bright Cluster Manager was merged into the NVIDIA Base Command Manager. Bright Computing was covered by Software Magazine and Yahoo! Finance, among other publications. == Awards == In 2016, Bright Computing was awarded a €1.5M Horizon 2020 SME Instrument grant from the European Commission. Bright Computing was one of only 33 grant recipients from 960 submitted proposals. In its category only 5 out of 260 grants were awarded. 2015 HPCwire Editor’s Choice Award for “Best HPC Cluster Solution or Technology" Main Software 50 “Highest Growth” award winner, 2013 Deloitte Technology Fast50 “Rising Star 2013” award winner Bio-IT World Conference & Expo ‘13, Boston, MA, winner of “IT Hardware & Infrastructure” category of the “Best of Show Award” program Red Herring Top 100 Global Award, 2013

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  • DAVI

    DAVI

    The Dutch Automated Vehicle Initiative (DAVI) is a research and demonstration initiative developing automated vehicles for use on public roads. The project is unique in that, besides simply making driverless cars, it also focuses on having automated vehicles share information among each other. The aim is to have the cars help to avoid traffic congestion by reducing the safety distance between the cars (from 2 seconds to 0.5 seconds) and avoiding sudden traffic slow-downs due to maneuvers undertaken by drivers.

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  • Parasolid

    Parasolid

    Parasolid is a geometric modeling kernel originally developed by Shape Data Limited, now owned and developed by Siemens Digital Industries Software. It can be licensed by other companies for use in their 3D computer graphics software products. Parasolid's abilities include model creation and editing utilities such as Boolean modeling operators, feature modeling support, advanced surfacing, thickening and hollowing, blending and filleting, and sheet modeling. It also incorporates modeling with mesh surfaces and lattices. Parasolid also includes tools for direct model editing, including tapering, offsetting, geometry replacement and removing feature details with automated regeneration of surrounding data. Parasolid also provides wide-ranging graphical and rendering support, including hidden-line, wireframe and drafting, tessellation, and model data inquiries. To use Parasolid effectively, software developers need knowledge of CAD in general, computational geometry, and topology. Parasolid is available for Windows (32-bit, 64-bit and AArch64), Linux (64-bit and AArch64), macOS (Apple silicon and Intel), iOS, and Android. == Parasolid XT format == Parasolid parts are normally saved in XT format, which usually has the file extension .X_T. The format is documented and open. There is also a binary version of the format, usually with an .X_B extension, which is somewhat more compact. Both .X_T and .X_B are used for parts files. == Applications == It is used in many computer-aided design (CAD), computer-aided manufacturing (CAM), computer-aided engineering (CAE), product visualization, and CAD data exchange packages. Notable uses include:

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