AI Assistant Intellij

AI Assistant Intellij — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

SeaTable

SeaTable is a no-code platform that allows users to develop and implement business processes. The cloud collaboration service SeaTable is marketed by the GmbH of the same name with headquarters in Mainz and additional offices in Berlin and Beijing, and developed by the same company as Seafile. == History == SeaTable is a collaborative database and low-code application platform developed as part of a joint venture between Seafile Ltd., a software company based in Guangzhou, China, and SeaTable GmbH, a German firm headquartered in Mainz. Founded in 2020, the project represents the international expansion of Seafile, a Chinese developer originally known for its file synchronization and sharing software. While SeaTable's cloud services and European client operations are managed by the German entity, the platform itself is developed in China by Seafile's engineering team. This cross-border structure, described by TechCrunch as an “unconventional path” for a Chinese startup expanding abroad, reflects Seafile's effort to maintain its product development in China while addressing growing scrutiny in Western markets over data governance and corporate control. In 2021, an innovation project led by the Cyber Innovation Hub at the IT School of the German Armed Forces started to evaluate the possibilities of a large-scale deployment at the German Armed Forces. The evaluation project is currently still ongoing. In 2022, SeaTable is optimizing its database backend to allow millions of records within one base in the future. The focus of development is increasingly on automation and visualization. In 2025, SeaTable introduced AI-powered automations with version 6. The update enabled the integration of large language models (LLMs) for text analysis and automated decision-making. SeaTable operates a self-hosted LLM on servers provided by Hetzner (Germany), while self-hosted deployments can connect to any compatible model. == Features == SeaTable combines the traditional capabilities of a spreadsheet such as Excel and supplements them with a wide range of functions for process automation and visualization as well as a fully comprehensive API. SeaTable is not a pure cloud solution, but can alternatively be installed on a private server and operated completely autonomously. In this way, the owner retains full control over their own data. The installation is done via Docker on a Linux server. == Security and privacy == While most no-code platforms exist only as SaaS solutions, SeaTable describes itself as a data-sparse European solution. While initially the SeaTable Cloud was hosted on Amazon AWS, the move to the German data centers of Swiss provider Exoscale then took place in May 2021. This was followed by the replacement of the Freshdesk cloud ticketing system with a self-hosted Zammad instance, and since April 2022 SeaTable has completely dispensed with all tracking cookies on its website.
Read more →
Acousto-electronics

Acousto-electronics (also spelled 'Acoustoelectronics') is a branch of physics, acoustics and electronics that studies interactions of ultrasonic and hypersonic waves in solids with electrons and with electro-magnetic fields. Typical phenomena studied in acousto-electronics are acousto-electric effect and also amplification of acoustic waves by flows of electrons in piezoelectric semiconductors, when the drift velocity of the electrons exceeds the velocity of sound. The term 'acousto-electronics' is often understood in a wider sense to include numerous practical applications of the interactions of electro-magnetic fields with acoustic waves in solids. In particular, these are signal processing devices using surface acoustic waves (SAW), different sensors of temperature, pressure, humidity, acceleration, etc.
Read more →
Modulation error ratio

The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ⁡ ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.
Read more →
Matt Mullenweg

Matthew Charles Mullenweg (born January 11, 1984) is an American web developer and entrepreneur. He is known as a co-founder of the free and open-source web publishing software WordPress, and the founder of Automattic. == Early life and education == Mullenweg was born January 11, 1984, in Houston, Texas, to Chuck and Kathleen Mullenweg and grew up in the Willowbend neighborhood. His older sister was born in 1974. His father, who died in 2016, was a computer programmer who worked for Brown & Root, and encouraged his children to start using home computers at an early age. His mother was a stay-at-home mother. The Mullenwegs were raised Catholic. He attended Kinder High School for the Performing and Visual Arts, studying jazz and playing the saxophone. Mullenweg suffered from migraines as a child that forced him to miss extended periods of school. He attended the University of Houston for two years, studying philosophy and political science. He dropped out after his sophomore year in 2004 to work for CNET, which promised him that he could allocate time to the development of WordPress. == Career == Mullenweg began blogging in 2002 on the open source platform b2. B2 developer Michael Valdrighi abandoned the project and Mullenweg took it over in 2003. He and Mike Little created a b2 fork that year they called WordPress and published it under the GNU General Public License. In March 2003, he co-founded the Global Multimedia Protocols Group (GMPG) with Eric A. Meyer and Tantek Çelik. In April 2004, he helped launch Ping-O-Matic, a mechanism for notifying search engines about blog updates. In October 2004, he was hired by CNET who would allow him to develop WordPress part-time as part of his job. He dropped out of college and moved to San Francisco for the position. === Automattic === After leaving CNET in 2005, Mullenweg founded Automattic as a fully distributed company. Toni Schneider was hired as CEO so Mullenweg could learn how to manage a large organization. During this period, Mullenweg focused on product development while Schneider managed the company. In January 2014, Mullenweg resumed the role of CEO, replacing Schneider. He led Automattic's expansion and a series of acquisitions, including WooCommerce in 2015, The Atavist Magazine in 2018, Tumblr in 2019, Pocket Casts in 2021, and Beeper in 2024. Mullenweg received the Heinz Award for Technology, the Economy and Employment in 2016, for "helping to democratize online publishing". Automattic's valuation reached $7.5 billion in 2021. At the time, WordPress hosted 28 million websites, or 40 percent of all websites on the Internet. == Public disputes == On several occasions, Mullenweg has publicly challenged competitors to WordPress and WordPress.com. He has stated that he prefers to settle disputes in the court of public opinion and described his approach as "brinksmanship", noting that the potential cost of legal action could put Automattic in a "tough spot". In 2008, shortly before WordPress 2.5's release, Six Apart's Movable Type published "A WordPress 2.5 Upgrade Guide"—a comparison of their CMS with their rival, WordPress—as a company blog article that Mullenweg characterized as "desperate and dirty". In 2013, developers on the digital marketplace Envato were banned from speaking at WordPress events after he criticized the platform for selling WordPress themes with the graphics and CSS components under a proprietary license instead of the GPL. In 2016, Mullenweg accused Wix.com, a competitor to WordPress.com, of reusing WordPress's mobile text editor code in Wix's own mobile app without adhering to the terms of the GPL. Despite the license's requirement to publish anything built with GPL code under the GPL, Wix's CEO claimed that the company open-sourced their forked version of the component and satisfied the license's terms before the app switched to its own fork of the MIT-licensed text editor that the WordPress editor was based upon. The new fork added a clause to the MIT license that forbids redistribution under any other license. In 2022, Mullenweg criticized GoDaddy for not reinvesting in the WordPress project sufficiently. On January 9, 2025, the representative of the WordPress Sustainability team, Thijs Buijs, resigned via WordPress.org’s Slack channel, citing dissatisfaction with Matt Mullenweg’s December 24, 2024, Reddit post titled “What drama should I create in 2025?” highlighting concerns about what he described as “unsustainable leadership”. In response, Matt Mullenweg thanked Thijs Buijs for reminding him of the existence of a sustainability team, announced its disbanding, and subsequently closed Wordpress.org's #sustainability Slack channel. === Tumblr === Mullenweg began a three-month sabbatical from his role as CEO at the beginning of February 2024. During that time, Mullenweg engaged in a public feud with a transgender Tumblr user who, frustrated with the failure of Tumblr (owned by Automattic) to address transphobic harassment, posted that she wished Mullenweg would die in a comedic way. The user was subsequently banned. Responding to user uproar, Mullenweg addressed the ban in posts on his personal Tumblr blog, in which he characterized the post as a death threat, and shared private account information about the user. Mullenweg also responded to individual commenters on Tumblr in posts and direct messages, and went to Twitter to respond to the banned user's tweets about the situation. A few days later, transgender employees of Tumblr and Automattic made a post on the official Tumblr staff blog characterizing his response as "unwarranted and harmful" and stating that he did not speak on their behalf. They also said that the user's post was not a realistic threat of violence and not the reason for her ban. === WP Engine dispute === == Audrey Capital == Mullenweg is a principal at angel investment firm Audrey Capital, which he co-founded in 2008 alongside Naveen Selvadurai and Audrey Kim. As of 2024, the company lists investments in companies such as CoinDesk, MakerBot, Sonos, SpaceX, Ring, as well as software companies including Calm, Chartbeat, DailyBurn, Memrise, Genius, Nord Security and Telegram. It has also funded startups that provide services to web developers including Creative Market, GitLab, NPM, SendGrid, Stripe and Typekit. From 2017 to 2019, Mullenweg also served as a board member for GitLab. Mullenweg has employed a team of contributors to WordPress through Audrey Capital since 2010, who work separately from Automattic. On the 20th anniversary of WordPress' initial release, Mullenweg announced a scholarship program aimed at the children of significant contributors to open-source projects.
Read more →
Machine-learned interatomic potential

Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed using machine learning. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mapping atomic structures to their potential energies. These potentials are referred to as MLIPs or MLPs. Such machine learning potentials promised to fill the gap between density functional theory, a highly accurate but computationally intensive modelling method, and empirically derived or intuitively-approximated potentials, which were far lighter computationally but substantially less accurate. Improvements in artificial intelligence technology heightened the accuracy of MLPs while lowering their computational cost, increasing the role of machine learning in fitting potentials. Machine learning potentials began by using neural networks to tackle low-dimensional systems. While promising, these models could not systematically account for interatomic energy interactions; they could be applied to small molecules in a vacuum, or molecules interacting with frozen surfaces, but not much else – and even in these applications, the models often relied on force fields or potentials derived empirically or with simulations. These models thus remained confined to academia. Modern neural networks construct highly accurate and computationally light potentials, as theoretical understanding of materials science was increasingly built into their architectures and preprocessing. Almost all are local, accounting for all interactions between an atom and its neighbor up to some cutoff radius. There exist some nonlocal models, but these have been experimental for almost a decade. For most systems, reasonable cutoff radii enable highly accurate results. Almost all neural networks intake atomic coordinates and output potential energies. For some, these atomic coordinates are converted into atom-centered symmetry functions. From this data, a separate atomic neural network is trained for each element; each atomic network is evaluated whenever that element occurs in the given structure, and then the results are pooled together at the end. This process – in particular, the atom-centered symmetry functions which convey translational, rotational, and permutational invariances – has greatly improved machine learning potentials by significantly constraining the neural network search space. Other models use a similar process but emphasize bonds over atoms, using pair symmetry functions and training one network per atom pair. Other models to learn their own descriptors rather than using predetermined symmetry-dictating functions. These models, called message-passing neural networks (MPNNs), are graph neural networks. Treating molecules as three-dimensional graphs (where atoms are nodes and bonds are edges), the model takes feature vectors describing the atoms as input, and iteratively updates these vectors as information about neighboring atoms is processed through message functions and convolutions. These feature vectors are then used to predict the final potentials. The flexibility of this method often results in stronger, more generalizable models. In 2017, the first-ever MPNN model (a deep tensor neural network) was used to calculate the properties of small organic molecules. == Gaussian Approximation Potential (GAP) == One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP), which combines compact descriptors of local atomic environments with Gaussian process regression to machine learn the potential energy surface of a given system. To date, the GAP framework has been used to successfully develop a number of MLIPs for various systems, including for elemental systems such as carbon, silicon, phosphorus, and tungsten, as well as for multicomponent systems such as Ge2Sb2Te5 and austenitic stainless steel, Fe7Cr2Ni. == Equivariant graph neural networks == A significant limitation of early MPNNs was that they were not inherently equivariant to rotations and reflections of atomic structures — meaning predictions could change depending on how a molecule was oriented in space. Beginning around 2021, a new class of models addressed this by incorporating equivariance directly into the message-passing layers using spherical harmonics and irreducible representations. Notable examples include NequIP (2021), MACE (2022), and GemNet-OC (2022). These equivariant architectures proved substantially more data-efficient and accurate than their predecessors, and became the dominant paradigm for high-accuracy MLIPs. == Universal MLIPs and large-scale datasets == Early MLIPs were system-specific, trained on a few thousand structures of a single material. A major shift occurred with the creation of large, chemically diverse datasets enabling models that generalize across many elements, bonding environments, and application domains — so-called universal MLIPs. A key driver was the Open Catalyst Project (OC20, OC22), a collaboration between Meta AI (FAIR) and Carnegie Mellon University launched in 2020. OC20 comprises approximately 1.3 million DFT relaxations across 82 elements, designed to accelerate the discovery of catalysts for renewable energy applications. It was among the first datasets large enough to train GNNs that generalize across diverse chemical systems, and established a widely-used benchmark for the field. A subsequent dataset, Open Direct Air Capture (OpenDAC 2023 and OpenDAC 2025), applied the same approach to carbon capture, providing a large computational database of metal-organic frameworks and sorbent candidates evaluated for CO₂ capture, generated using nearly 400 million CPU hours of quantum chemistry calculations in collaboration with Georgia Tech. These datasets revealed a new challenge: the GNN architectures most effective for atomic simulations were memory-intensive, as they model higher-order interactions between triplets or quadruplets of atoms, making it difficult to scale model size. Graph Parallelism, introduced by Sriram et al. (ICLR 2022), addressed this by distributing a single input graph across multiple GPUs — a distinct strategy from data parallelism (which distributes training examples) or model parallelism (which distributes layers). This enabled training GNNs with hundreds of millions to billions of parameters for the first time. Building on these foundations, Meta FAIR released the Universal Model for Atoms (UMA) in 2025, trained on approximately 500 million unique 3D atomic structures spanning molecules, materials, and catalysts — the largest training run to date for an MLIP. UMA introduced a Mixture of Linear Experts (MoLE) architecture, enabling one model to learn from datasets generated by different DFT codes and settings without significant inference overhead. It matches or surpasses specialized models across catalysis, materials, and molecular benchmarks without task-specific fine-tuning, and has been described as marking a "pre/post-UMA" divide in the field. == Applications == Catalyst discovery: MLIPs have significantly accelerated the computational screening of heterogeneous catalysts by replacing expensive DFT relaxations with fast neural network surrogates. The Open Catalyst Project explicitly targets this application, aiming to identify new catalysts for green hydrogen production and other renewable energy reactions. Carbon capture: The OpenDAC project applies universal MLIPs to screening sorbent materials for direct air capture of CO₂, a key technology for climate change mitigation. AI-accelerated screening allows evaluation of orders of magnitude more candidate materials than traditional DFT workflows. Drug discovery and molecular design: MLIPs are increasingly used in pharmaceutical research to model molecular conformations and binding energies. The Open Molecules 2025 (OMol25) dataset, released by Meta FAIR in 2025, provides high-accuracy calculations for a large set of molecular systems to support this use case. Materials discovery: Universal MLIPs enable high-throughput screening of novel inorganic materials, including battery electrolytes, semiconductors, and superconductors, by rapidly estimating stability and properties across large chemical spaces.
Read more →
Atomtronics

Atomtronics is an emerging field concerning the quantum technology of matter-wave circuits which coherently guide propagating ultra-cold atoms. The systems typically include components analogous to those found in electronics, quantum electronics or optical systems, such as beam splitters, transistors, and atomic counterparts of superconducting quantum interference devices (SQUIDs). Applications range from studies of fundamental physics to the development of practical devices such as quantum superfluids for the computation of large models for artificial general intelligence. == Etymology == Atomtronics is a portmanteau of "atom" and "electronics", in reference to the creation of atomic analogues of electronic components, such as transistors and diodes, and also electronic materials such as semiconductors. The field itself has considerable overlap with atom optics and quantum simulation, and is not strictly limited to the development of electronic-like components. However, this field develops into the research of ultra-cold atoms for the applied research implications of computations in quantum science. == Methodology == Three major elements are required for an atomtronic circuit. The first is a Bose-Einstein condensate, which is needed for its coherent and superfluid properties, although an ultracold Fermi gas may also be used for certain applications. The second is a tailored trapping potential, which can be generated optically, magnetically, or using a combination of both. The final element is a method to induce the movement of atoms within the potential, which can be achieved in several ways, for various research advancements around fields not limited to distributed computing, supercomputing, and quantum computing. For example, a transistor-like atomtronic circuit may be realized by a ring-shaped trap divided into two by two moveable weak barriers, with the two separate parts of the ring acting as the drain and the source and the barriers acting as the gate. As the barriers move, atoms flow from the source to the drain. It is now possible to coherently guide matterwaves over distances of up to 40 cm in ring-shaped atomtronic matterwave guide measurement. == Applications == The field of atomtronics is still very nascent and any schemes realized thus far are proof-of-concept. Applications include: gravimetry rotational sensing via the Sagnac effect quantum computing Obstacles to the development of practical sensing devices are largely due to the technical challenges of creating Bose-Einstein condensates. They require bulky lab-based setups not easily suitable for transportation. However, creating portable experimental setups is an active area of research.
Read more →
Haul video

A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.
Read more →
Asymmetric follow

An asymmetric follow social network is one which allows many people to follow an individual or account without having to follow them back. It is also known as asynchronous follow or sometimes asymmetric friendship. Asymmetric follow is a common pattern on Twitter, where someone may have thousands of followers, but themselves follow few (or no) accounts. In September 2010 Facebook started experimenting with a similar feature, which Facebook calls "Subscribe To."
Read more →
Ware report

Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.
Read more →
Electronic submission

Electronic submission refers to the submission of a document by electronic means: that is, via e-mail or a web form on the Internet, or on an electronic medium such as a compact disc, a hard disk or a USB flash drive. Traditionally, the term "manuscript" referred to anything that was explicitly "written by hand". However, in popular usage and especially in the context of computers and the internet, the term "manuscript" may even refer to documents (text or otherwise) typed out or prepared on typewriters and computers and can be extended to digital photographs and videos, and online surveys too. In other words, any manuscript prepared and submitted online can be considered to be an electronic submission. == History and early usage == There is no concrete data indicating when and by whom were electronic submissions used for the first time. However, research based universities in several countries have been encouraging the collection of course assignments and projects in the form of electronic submissions for almost a decade now. Several governments and organizations are also switching to electronic submissions for the collection of research papers, grant applications and government application forms. == Types of electronic submissions == Since modern computers can store and process information and data in virtually any format and with the Internet allowing easy transfer of this data, the number of scenarios in which submissions can be collected electronically has increased exponentially in the last few years. Some of these scenarios are described below. In most of these scenarios, submissions were collected on hard paper until the Information Technology revolution occurred. === Academic Submissions === Teachers, professors and teaching assistants often collect course assignments and projects electronically. Electronic submissions are usually collected using a web-based system which more often than not also helps in the management of submissions collected and stored on it. (Explained By Henny L, University of Lethbridge, AB, Canada) === Research Papers === In call-for-paper or academic conferences, prospective presenters are usually asked to submit a short abstract or a full paper on their presentation or research work electronically, which is reviewed before being accepted for the conference. === Proposals for Grants === Several grant-giving organizations like the NSA, W3C, NIA, NIH etc. require grant seekers to submit a proposal which if accepted result in the desired grants. A majority of these proposals are now submitted electronically on systems that also help in the managing and tracking the proposals submitted. === Articles for Publication === Magazines, newspapers and other publishing houses have begun accepting electronic submissions for articles from various sources - both internal (by journalists and writers hired by them) as well as external (by users and popular readers). The submitted articles are stored on a server hosted by the publication house or by a third-party Archived 2019-10-13 at the Wayback Machine vendor and are usually evaluated before being given a green signal. === Contests and Competition Entries === Almost every kind of contest or competition requires participants to submit an entry in a format described by the organizers of the contest. If the contest is an Internet-based one, then the entries or nominations for the contest are collected electronically using e-mail or other electronic means depending on feasibility and the choice of the organizers. === Government Applications === The governments of several countries are turning to electronic submission of applications and forms for various government procedures. Electronic submissions allow easier management of the applications and forms submitted. === Legal documents === Many legal documents may be submitted to the courts electronically. In England and Wales, the Civil Procedure Rules include a suitable "document exchange" as an acceptable "method of service". Case law in employment law cases has established that where a claim is submitted electronically, a prudent legal adviser should "check that it has been received and there must be systems in place for doing that". === Resumés and CVs === It has become commonplace for job-seekers to submit soft copies (electronic versions) of their resumés and CVs to recruiting agencies and online job portals. This is usually done over the Internet using e-mail or a pre-hosted web-based system. == Submission management systems == The art and science of collecting and managing electronic submissions is called Submission Management. Certain software vendors have begun developing submission management systems to assist in the collection, tracking and management of complex submission processes realized electronically. Most of these systems are web based and accessible from any device with a browser and an Internet connection. However, a majority of these systems are application specific and cannot be applied to all submission management scenarios. == Resistance to electronic submissions == Despite the easier management and tracking of electronic submissions compared to their paper-based counterparts, widespread adoption and use of electronic submissions and systems for managing them has been hampered by several facts, which include but are not limited to: Inconvenience while drawing figures, diagrams and equations on a computer Resistance to change and adoption of new technologies Lack of or limited access to the Internet.
Read more →
Zero-overhead looping

In computer architecture, zero-overhead looping is a hardware feature found in some processors that enables loops to execute without the performance cost of traditional loop control instructions. Instead of software managing loop iterations, the processor's hardware handles repetition automatically, saving clock cycles and improving efficiency. This technique is commonly employed in digital signal processors (DSPs) and certain complex instruction set computer (CISC) architectures. == Background == In many instruction sets, a loop must be implemented by using instructions to increment or decrement a counter, check whether the end of the loop has been reached, and if not jump to the beginning of the loop so it can be repeated. Although this typically only represents around 3–16 bytes of space for each loop, even that small amount could be significant depending on the size of the CPU caches. More significant is that those instructions each take time to execute, time which is not spent doing useful work. The overhead of such a loop is apparent compared to a completely unrolled loop, in which the body of the loop is duplicated exactly as many times as it will execute. In that case, no space or execution time is wasted on instructions to repeat the body of the loop. However, the duplication caused by loop unrolling can significantly increase code size, and the larger size can even impact execution time due to cache misses. (For this reason, it's common to only partially unroll loops, such as transforming it into a loop which performs the work of four iterations in one step before repeating. This balances the advantages of unrolling with the overhead of repeating the loop.) Moreover, completely unrolling a loop is only possible for a limited number of loops: those whose number of iterations is known at compile time. For example, the following C code could be compiled and optimized into the following x86 assembly code: == Implementation == Processors with zero-overhead looping have machine instructions and registers to automatically repeat one or more instructions. Depending on the instructions available, these may only be suitable for count-controlled loops ("for loops") in which the number of iterations can be calculated in advance, or only for condition-controlled loops ("while loops") such as operations on null-terminated strings. === Examples === ==== PIC ==== In the PIC instruction set, the REPEAT and DO instructions implement zero-overhead loops. REPEAT only repeats a single instruction, while DO repeats a specified number of following instructions. ==== Blackfin ==== Blackfin offers two zero-overhead loops. The loops can be nested; if both hardware loops are configured with the same "loop end" address, loop 1 will behave as the inner loop and repeat, and loop 0 will behave as the outer loop and repeat only if loop 1 would not repeat. Loops are controlled using the LTx and LBx registers (x either 0 to 1) to set the top and bottom of the loop — that is, the first and last instructions to be executed, which can be the same for a loop with only one instruction — and LCx for the loop count. The loop repeats if LCx is nonzero at the end of the loop, in which case LCx is decremented. The loop registers can be set manually, but this would typically consume 6 bytes to load the registers, and 8–16 bytes to set up the values to be loaded. More common is to use the loop setup instruction (represented in assembly as either LOOP with pseudo-instruction LOOP_BEGIN and LOOP_END, or in a single line as LSETUP), which optionally initializes LCx and sets LTx and LBx to the desired values. This only requires 4–6 bytes, but can only set LTx and LBx within a limited range relative to where the loop setup instruction is located. ==== x86 ==== The x86 assembly language REP prefixes implement zero-overhead loops for a few instructions (namely MOVS/STOS/CMPS/LODS/SCAS). Depending on the prefix and the instruction, the instruction will be repeated a number of times with (E)CX holding the repeat count, or until a match (or non-match) is found with AL/AX/EAX or with DS:[(E)SI]. This can be used to implement some types of searches and operations on null-terminated strings.
Read more →
Haul video

A haul video is a video recording posted to the Internet in which a person discusses items that they recently purchased, sometimes going into detail about their experiences during the purchase and the cost of the items they bought. The posting of haul videos (or hauls) was a growing trend between 2008 and 2016. Often the items bought are books, clothing, groceries, household goods, makeup, or jewellery. == Details == The posting of haul videos grew as a trend between 2008 and 2016. By late 2010, nearly a quarter of a million haul videos had been shared on the website YouTube alone. Certain videos have each received tens of millions of views. Many young adults (mostly women) have displayed their shopping hauls, while including their beauty and design commentary in the narration. The videos are often grouped by store name or by the type of product (cosmetics, accessories, shoes, postage stamps, etc.). Before haul videos became an online trend, millions of people spent time watching other people, in technical product videos unbox their latest new gadgets and technology. The trend of "unboxing videos" had emerged during 2006. Haul videos have led to celebrity status for some people. Other haul video bloggers have entered sponsorship deals and advertising programs from major brands. The videos are rarely negative about the products being reviewed. This aspect of the genre of haul videos makes sponsorship by brand advertisers particularly appealing. Brands including J.C. Penney contacted haulers as part of their marketing efforts for Back to School 2010. Haul videos also convinced three San Francisco Bay Area area natives to launch HaulBlog–a parody site that creates fake haul videos which poke fun at the phenomenon. The site is also home to the original monthly web series "The Haul Monitor" a humorous commentary show that features haul videos from around the community. == Fashion media == Sarah Sykes and John Zimmerman of Carnegie Mellon University, HCII and School of Design wrote an article "Making Sense of Haul Videos: Self-created Celebrities Fill a Fashion Media Gap". They discuss their analysis and research project examining what makes video bloggers so popular on YouTube, as well as how it affects fashion media through the production of haul videos. == Federal Trade Commission == The United States Federal Trade Commission recently enacted laws to regulate many types of online publishers and content creators. The posted information includes blogging and podcasting in text, images, audio, and video. While any publishers (including the haul-video creators) are allowed to accept free merchandise and advertising, the gifts or payments must be fully (and clearly) disclosed to reveal being paid by a brand name, as a sponsor, to review a product. The Canadian Radio-television and Telecommunications Commission is also closely monitoring such Internet activities.
Read more →
Nolot

Nolot is a chess test suite with 11 positions from real games. They were compiled by Pierre Nolot (French: [nɔ.lo]) for the French chess magazine Gambisco and posted on the rec.games.chess Usenet group in 1994. They were designed to be particularly hard to solve for chess engines to solve at the time, although modern engines can find a solution near-instantaneously. == Problem 1 == FEN: r3qb1k/1b4p1/p2pr2p/3n4/Pnp1N1N1/6RP/1B3PP1/1B1QR1K1 w - - 0 1 26.Nxh6!! c3 (26... Rxh6 27.Nxd6 Qh5 (best) 28.Rg5! Qxd1 29.Nf7+ Kg8 30.Nxh6+ Kh8 31.Rxd1 c3 32.Nf7+ Kg8 33.Bg6! Nf4 34.Bxc3 Nxg6 35.Bxb4 Kxf7 36.Rd7+ Kf6 37.Rxg6+ Kxg6 38.Rxb7 ±) 27.Nf5! cxb2 28.Qg4 Bc8 (28... g6!? 29.Kh2! 29.Qd7 30.Nh4 Bc6 31.Nc5! dxc 32.Rxe6 Nf6 33.Nxg6+ Kg7 34.Qg5 Nbd5 35.Ne5 Kh8 36.Nxd7 ±) 29.Qh4+ Rh6 30.Nxh6 gxh6 31.Kh2! Qe5 32.Ng5 Qf6 33.Re8 Bf5 34.Qxh6 (missing a mate in 6: 34.Nf7+ Qxf7 35.Qxh6+ Bh7 36.Rxa8 Nf6 37.Rxf8 Qxf8 38.Qxf8+ Ng8 39.Qg7#) 34...Qxh6 35.Nf7+ Kh7 36.Bxf5+ Qg6 37.Bxg6+ Kg7 38.Rxa8 Be7 39.Rb8 a5 40.Be4+ Kxf7 41.Bxd5+ 1–0 The best Novag computer, the Diablo 68000, finds 26. Nxh6 after seven and a half months (Pierre Nolot has let it run on the position for 14 months and one day, until a power failure stopped an analysis of over 80,000,000,000 nodes.) but for wrong reasons: it evaluates white's position as inferior and thinks this move would enable it to draw. Today Gambit Tiger 2.0 for example can find it quite quickly: Most free engines running on 64-bit processors in 2010 could solve this problem and the others in a few seconds. 1.Qd4 c3 2.Bxc3 Nxc3 3.Qxb4 Nxe4 4.Qxb7 Rb8 5.Qxb8 Qxb8 6.Bxe4 d5 7.Rb1 μ (-1.20) Depth: 12 00:00:09 6055 kN 1.Nxh6 c3 2.Nf5 cxb2 3.Qg4 Rb8 4.Nxg7 Rg6 5.Qxg6 Qxg6 6.Rxg6 Bxg7 7.Nxd6 ³ (-0.48) Depth: 12 00:00:21 14368 kN 1.Nxh6 c3 2.Nf5 cxb2 3.Qg4 Rc8 4.Nxg7 Rg6 5.Nxe8 Rxg4 6.Rxg4 Rxe8 7.Rg6 μ (-0.74) Depth: 13 00:00:55 38455 kN 1.Ne3 Rxe4 2.Bxe4 Qxe4 3.Nxd5 Qxd5 4.Qc1 Qf5 5.Qxh6+ Qh7 6.Qe6 Nd3 7.Re2 Nxb2 8.Rxb2 ³ (-0.58) Depth: 13 00:01:30 62979 kN 1.Ne3 Rxe4 ³ (-0.58) Depth: 14 00:02:02 84941 kN 1.Ne3 Nxe3 2.Rexe3 Bxe4 3.Qg4 Rg6 4.Qxe4 Qxe4 5.Bxe4 Rxg3 6.Rxg3 d5 7.Bf5 Re8 8.Bc3 ³ (-0.30) Depth: 15 00:03:05 128968 kN 1.Nxh6 ² (0.32) Depth: 15 00:07:58 350813 kN With the next ply showing a clear advantage. Stockfish 14dev 64bit 4CPU running on 2020 hardware recognises the significance of Nxh6!! in 1 second. Stockfish_21092606_x64_avx2: NNUE evaluation using nn-13406b1dcbe0.nnue enabled. 19/32 00:01 7708k 4882k +3,00 Nxh6 Rxh6 Nxd6 Qh5 Bg6 Qxd1 Nf7+ Kg8 Nxh6+ gxh6 Bh5+ Kh7 Rxd1 c3 Bxc3 Nxc3 Rd7+ Kh8 Rxb7 Ne4 Re3 Nxf2 Kxf2 Bc5 Ke2 Bxe3 Kxe3 Nd5+ Kf2 49/73 15:02 5118270k 5673k +6,15 Nxh6 Rxh6 Nxd6 Qh5 Rg5 Qxd1 Nf7+ Kg8 Nxh6+ Kh8 Rxd1 c3 Nf7+ Kg8 Bg6 Nf4 Bxc3 Nbd5 Rb1 Bc6 Bd2 Nxg6 Rxg6 Ne7 Rxc6 Nxc6 Rb6 Rc8 Ng5 a5 Ra6 Bb4 Be3 Ne5 Bd4 Nc6 Bb6 Bd2 h4 Kf8 Bc5+ Kg8 Be3 Bxe3 fxe3 Kf8 Kf2 Ke7 Nf3 Kd7 Rb6 Ne7 Rb5 Kd6 Rxa5 Rc2+ Kg3 Re2 Nd4 Rxe3+ Kf4 Rd3 Nf5+ Kc7 Nxe7 == Problem 2 == FEN: r4rk1/pp1n1p1p/1nqP2p1/2b1P1B1/4NQ2/1B3P2/PP2K2P/2R5 w - - 0 1 22.Rxc5!! Nxc5 23.Nf6+ Kh8 24.Qh4 Qb5+ (computers think there is perpetual check here, but...) 25.Ke3! 25... h5 26.Nxh5 Qxb3+ (26... d5+ 27.Bxd5 Qd3 28.Kf2 Ne4+ 29.Bxe4 Qd4+ 30.Kg2 Qxb2+ 31.Kh3 ±) and White won in 41 moves. Today Deep Junior 8.ZX for example finds it very quickly (around 1 minute): 1.Kd1 Rac8 2.Bh6 Qb5 3.Rc3 Qf1+ 4.Kc2 Rc6 5.Bxf8 −+ (-2.11) Depth: 12 00:00:04 10422 kN 1.Nxc5 Nxc5 2.Rxc5 Qxc5 3.e6 Rae8 4.e7 Nc8 5.Kf1 Nxd6 6.Bf6 b5 −+ (-2.10) Depth: 12 00:00:14 25054 kN 1.Bf6! μ (-1.35) Depth: 12 00:00:17 34601 kN 1.Bf6 Qb5+ 2.Ke1 Bb4+ 3.Kf2 Bc5+ = (0.00) Depth: 12 00:00:20 34601 kN 1.Bf6 Qb5+ 2.Ke1 Nxf6 3.Nxf6+ Kg7 4.Nh5+ gxh5 5.Qf6+ Kg8 6.Qg5+ Kh8 7.Qf6+ = (0.00) Depth: 15 00:01:01 130544 kN 1.Rxc5! = (0.15) Depth: 15 00:01:12 145875 kN 1.Rxc5 Nxc5 2.Nf6+ Kh8 3.Qh4 Qb5+ 4.Ke3 h5 5.Nxh5 Qd3+ 6.Kf2 Ne4+ 7.fxe4 Qd4+ 8.Kf1 Qd3+ 9.Ke1 Qb1+ 10.Bd1 ± (2.18) Depth: 15 00:01:18 145875 kN Stockfish 14dev 64bit 4CPU running on 2020 hardware recognises the significance of Rxc5!! in 1 second. Stockfish_21092606_x64_avx2: NNUE evaluation using nn-13406b1dcbe0.nnue enabled. 21/25 00:01 5822k 5545k +6,61 Rxc5 Qxc5 Nxc5 Nxc5 Bh6 Nbd7 Bxf8 Rxf8 Qe3 Rc8 f4 Nxe5 Qxe5 Ne6 Bxe6 Rc2+ Kd3 Rxh2 46/86 11:27 5057055k 7355k +7,61 Rxc5 Qxc5 Nxc5 Nxc5 Bf6 Ne6 Qh6 Nd4+ Kf2 Nf5 Qg5 Nd7 h4 Nxf6 Qxf6 Ng7 d7 b5 Bd5 Rab8 b4 Nh5 Bxf7+ Rxf7 d8R+ Rxd8 Qxd8+ Rf8 Qd5+ Kg7 e6 Kf6 Qd7 Ng7 Qd4+ Kxe6 Qxg7 Rf7 Qc3 Ke7 Qc5+ Ke8 Qc8+ Ke7 h5 gxh5 Kg3 h4+ Kh2 h6 Qc5+ Kf6 Qxb5 Kg7 f4 Rxf4 Qe5+ Rf6 b5 h3 Qd4 Kg8 Qxf6 h5 Blacks 22. .. Nxc5 is suboptimal and leads faster mate 77/44 09:18 6987714k 12518k +M22 Nf6+ Kh8 Qh4 Qb5+ Ke3 Qxb3+ axb3 h5 Nxh5 Nd5+ Kd4 Ne6+ Kxd5 Nxg5 Qxg5 gxh5 f4 Rad8 f5 f6 Qxh5+ Kg7 Qg6+ Kh8 e6 b6 e7 Rb8 exf8Q+ Rxf8 Ke6 b5 Ke7 Rb8 Qh5+ Kg7 Qf7+ Kh8 Kxf6 Rf8 Qxf8+ Kh7 Qg7+ == Problem 3 == FEN: r2qk2r/ppp1b1pp/2n1p3/3pP1n1/3P2b1/2PB1NN1/PP4PP/R1BQK2R w KQkq - 0 1 12.Nxg5!! Bxd1 13.Nxe6 Qb8 14.Nxg7+!! Kf8 15.Bh6! Bg4 16.0-0+ Kg8 17.Rf4 ± White wins with a queen sac but black has defensive resources. Stockfish 8 64bit 3CPU running on 2016 hardware recognizes the significance of Nxg5!! in 55 seconds. Stockfish 14 dev (Stockfish_21092606_x64_avx2) 64bit 4CPU running on 2020 hardware recognizes the significance of Nxg5!! in 1 second. NNUE evaluation using nn-13406b1dcbe0.nnue enabled. 21/34 00:01 8291k 4530k +2,78 Nxg5 Bxd1 Nxe6 Qb8 Nxg7+ Kd8 Kxd1 b5 N3f5 Bf8 Rf1 Kc8 Nh5 Kb7 Bxb5 Ne7 g4 a6 Ba4 Nxf5 gxf5 Ka7 Nf4 c5 47/59 37:49 10390430k 4578k +3,16 Nxg5 Bxd1 Nxe6 Qb8 Nxg7+ Kd8 Kxd1 b5 Rf1 Kc8 N3f5 Bf8 Ne6 Kd7 Nf4 Ne7 g4 a5 Ke2 Qb7 h4 Ra6 a3 Kc8 Be3 Kb8 Kf3 Rb6 Bd2 Qc8 Kg3 c5 Be3 c4 Nxe7 Bxe7 Bf5 Qd8 h5 Qg8 Kh3 Bg5 Rf3 Ra6 Raf1 b4 Nxd5 Qxd5 Bxg5 bxc3 bxc3 Rb6 Be3 Rb3 Blacks 14 .. Kf8 is suboptimal and leads loss fast 41/68 06:31 3269727k 8350k +9,28 Bh6 Kg8 Rxd1 Bf8 N3h5 Bxg7 Nxg7 Qf8 Nf5 Ne7 Bxf8 Nxf5 Bxf5 Rxf8 Be6+ Kg7 Rd3 Rf4 Bxd5 c6 Rg3+ Kf8 Rf3 Rxf3 Bxf3 Kg7 Rf1 Re8 Be4 Re6 Ke2 a5 Ke3 Rh6 h3 a4 Kf4 Re6 h4 Re8 Ke3 h6 h5 Rf8 Rxf8 Kxf8 == Problem 4 == FEN: r1b1kb1r/1p1n1ppp/p2ppn2/6BB/2qNP3/2N5/PPP2PPP/R2Q1RK1 w kq - 0 1 10.Nxe6!! Qxe6 11.Nd5 Kd8 12.Bg4 Qe5 13.f4 Qxe4 (13...Qxb2 stronger but not sufficient: 14.Bxd7 Bxd7 15.Rb1 Qa3 16.Nxf6 Bb5 17.Qd4 Qc5 18.Rfd1 ±) 14.Bxd7 Bxd7 15.Nxf6 gxf6 16.Bxf6+ Kc7 17.Bxh8 and Black resigned on move 27. Stockfish 14dev 64bit 4CPU running on 2020 hardware recognises the significance of 10.Nxe6 in 1 second. Stockfish_21092606_x64_avx2: NNUE evaluation using nn-13406b1dcbe0.nnue enabled. 22/37 00:01 6955k 5367k +4,00 Nxe6 Qxe6 Nd5 Kd8 Bg4 Qe5 f4 Qxb2 Rb1 Qa3 Bxd7 Bxd7 Nxf6 Bb5 Rf3 Qxa2 c4 Bxc4 Rf2 Qa5 Nd5+ f6 Nxf6 Kc7 Rc1 b5 Qd5 gxf6 Bxf6 Kb8 Rxc4 Qe1+ Rf1 51/70 47:10 14538911k 5137k +5,76 Nxe6 Qxe6 Nd5 Kd8 Bg4 Qe5 f4 Qxe4 Bxd7 Bxd7 Nxf6 Qf5 Qd4 Kc8 Nd5 Bc6 c4 f6 Nb6+ Kb8 Bh4 Be7 Rae1 Bd8 Nxa8 Kxa8 Bf2 Kb8 Qxd6+ Bc7 Ba7+ Kc8 Qe6+ Qxe6 Rxe6 h5 h4 Rd8 Re7 g6 Be3 Ba5 Kf2 Rd6 Rc1 Bd8 Rg7 Be4 Rg8 Kd7 c5 Rd3 Rc4 Bd5 Rg7+ Ke6 Rd4 Rxd4 Bxd4 Kf5 Rd7 Bc6 Rxd8 Kxf4 Bxf6 == Problem 5 == FEN: r2qrb1k/1p1b2p1/p2ppn1p/8/3NP3/1BN5/PPP3QP/1K3RR1 w - - 0 1 21.e5!! dxe5 22.Ne4! Nh5 23.Qg6!? (stronger is 23.Qg4!! Nf4 24.Nf3 Qc7 25.Nh4 ± ) 23...exd4? (23...Nf4 24.Rxf4! exf4 25.Nf3! Qb6 26.Rg5!! covering b5 and threatening Nf6 or Ne5-f7+) 24.Ng5 1−0 Stockfish 8 64bit 3CPU running on 2016 hardware recognises the significance of 21.e5 in 5 seconds. Stockfish 12 dev (Stockfish_20062212_x64_modern) 64bit 1CPU running on 2016 hardware recognizes the significance of 21.e5 in 11 seconds. 25/42 00:06 7 963k 1309k +6,93 e5 Nh5 Ne4 dxe5 Nf3 Nf4 Qg4 Qc7 Nh4 Bc6 Nf6 g5 Rxf4 exf4 Qh5 Qe7 Ng6+ Kg7 Nxe7 Rxe7 Ng4 37/62 03:12 298 083k 1545k +10,70 e5 Ng4 Qxg4 Qg5 Qh3 Qxe5 Nde2 g5 Rxf8+ Kg7 Rff1 Rf8 Re1 Qf5 Qg3 Rad8 Nd4 Qf4 Nxe6+ Bxe6 Rxe6 Qxg3 == Problem 6 == FEN: rnbqk2r/1p3ppp/p7/1NpPp3/QPP1P1n1/P4N2/4KbPP/R1B2B1R b kq - 0 1 13... axb5!! offers an exchange to keep the white queen out of play. 14.Qxa8 Bd4 15.Nxd4 cxd4 16.Qxb8 0-0! 17.Ke1 Qh4 18.g3 Qf6 19.Bf4 g5? (Ivanchuk found 19...d3! during post-game analysis.) 20.Rc1 exf4 21.Qxf4 Qd4 22.Rd1 bxc4 23.e5 Qc3+ 24.Rd2 Re8 25.Bxd3 cxd3 −+ Tasc R30 finds 19... d3! in 2 1/2 hours. 19... Bf5!! is even stronger than 19... d3. Position is already lost at 19... d3 +8.00 for black, ... Bf5 not much better Stockfish 14dev 64bit 4CPU running on 2020 hardware recognises the significance of axb5!! in 1 second. Stockfish_21092606_x64_avx2: NNUE evaluation using nn-13406b1dcbe0.nnue enabled. 21/28 00:01 9264k 4714k -1,22 axb5 Qxa8 Bd4 Nxd4 cxd4 h3 Nf6 Bg5 0-0 cxb5 h6 Bxf6 Qxf6 Re1 Nd7 Kd1 Qg6 Qa4 Qg3 Qc2 Qxa3 Bd3 Qxb4 Qb1 46/67 1:05:00 18113493k 4644k -2,40 axb5 Qxa8 Bd4 h3 Nf6 Nxd4 exd4 Kf2 Nxe4+ Kg1 Nd7 Bg5 Qxg5 Qxc8+ Ke7 Qc7 Qe5 d6+ Qxd6 Qxd6+ Kxd6 bxc5+ Ndxc5 cxb5 d3 h4 d2 Rh3 Ke5 Be2 f5 Ra2 Rd8 Bd1 Rd4 Re3 f4 Re2 b6 a4 Kd6 Rc2 Kd5 Ra2 h6 Rb2 Nxa4 Bxa4 Rxa4 Rexd2+ Nxd2 Rxd2+ Kc4 Rd7 g6 == Problem 7 == FEN 1r1bk2r/2R2ppp/p3p3/1b2P2q/4QP2/4N3/1B4PP/3R2K1 w k - 0 1 1.Rxd8+!! Rxd8 (1...Kxd8 2.Ra7! Qe2 3.Qd4+ Ke8 4.h3 Qe1+ 5.Kh2 Rd8 6.Qc5 Qh4 7.Ba3 Rd7 8.Ra8+ Rd8 9.g3 1−0)
Read more →
Daylight Computer Co.

Daylight Computer Co. is a Public Benefit Company that designs and manufactures devices that do not emit blue light or flicker. Anjan Katta, the company's founder and CEO, stated that he started the company to reduce his personal eyestrain and the distraction that came with conventional devices. The first device that the company released is the Daylight DC-1, a tablet using a monochrome transflective liquid-crystal display designed for outdoor use, while also being usable indoors with an amber backlight. The company's goal is to create a "healthy computer." == History == In June 2018, Anjan Katta began the process of designing a device that did not emit blue light or flicker. He was inspired by the Kindle stating that he wanted to create a device that was, "an analog object that happens to have digital magical capabilities.” By 2020, he created his first scientific prototype and created the first proof-of-concept prototype in 2021. In the early research and development stages of the device, Katta had spent $300,000 of his own money. Eventually, Katta obtained a $12 million investment from current and former executives of companies such as Oculus, Pinterest, and Dropbox. In 2024, the company held a launch party at the Conservatory of Flowers in Golden Gate Park for the Daylight DC1, the company's first device. The event had roughly 200 attendees. Later that year, Daylight sold out its first run of 5,000 devices. The Daylight DC1 is a 1.2 pound tablet that runs its own operating system, SolOS, based on Android 13. It has a refresh rate of 60 Hz, fast enough to process video. In 2025, the product was demonstrated by Danny Jones on the Joe Rogan Experience. The company has been described by outlets such as Wired and VentureBeat as a "returning computing to hippie ideals" and being a product for "techno-hippies." The company is headquartered in San Francisco, California.
Read more →
RR Media

RR Media was a NASDAQ listed provider of global digital media services to the broadcast industry and content owners. Its services can be divided into four main groups: global content distribution network (satellite, fiber and the internet); content management & playout; sports, news & live events; and online video services. The company was rebranded to RR Media from RRsat in September 2014. In February 2016, it was announced that, subject to regulatory approvals, RR Media was to be acquired by SES, based in Betzdorf, Luxembourg, and merged with SES subsidiary company, SES Platform Services a media services provider for television broadcasters, production companies and platform operators, based in Unterföhring near Munich, Germany. In July 2016, the merged company was named MX1. == Digital media services == Global content distribution services RR Media's global distribution network uses a combination of satellite, fiber and the internet. The network includes satellite downlink and uplink; fiber connectivity to digital media hubs; connectivity to TV service providers; and internet-based content delivery. RR Media's network delivers live television channels, streaming media and Video on demand (VOD) content in all formats including Standard-definition television (SD), High-definition television (HD), 4K resolution (4K) & 3D television (3D). End-to-end content management & playout services RR Media manages, prepares and plays out content from its media centers. Services include: content preparation (digitization, localization, conversion, ingest, multiple formatting, editing, restoration); content management (digital asset management, media ingest and library, streamlined workflows, metadata curation, Video on demand (VOD) delivery) and playout, channel creation, playlist management, advertising insertion/management, graphics, titles & overlay, live events operations). RR Media also creates branded or white label product television channels using live and archived materials. Sports, news & live events RR Media delivers live sports and event content for sports rights holders, broadcasters and news channels. Services include: live production (Outside broadcasting vans, Satellite news gathering (SNG), studios), global live distribution, sports content preparation and content management, playout and origination.RR Media provides downlink, uplink, simultaneous translation, turnaround and live production services for sports events like football, basketball, tennis and golf, news and entertainment channels. Online video services RR Media converts existing and archive content into programs, channels and other digital assets, and converges broadcast and internet delivery. Services include converged media (preparing content for broadcast or online use) Content Management Systems (CMS), VOD services, branded platforms, multi-screen delivery, web video portals and viewer measurement tools (using digital analytics). == Media centers == RR Media's media centers are based in Hawley, PA (USA), Emeq Ha’Ela (Israel) Bucharest (Romania), with another facility opened in London, (UK) in June 2015. An additional facility in Miami, FL United States was announced in April 2016. The centers provide RR Media's services, including content preparation, management, online video, live content and distribution, and 24/7 service and support. == Awards == In November 2014, RR Media won the award for Achievement in Legacy Content at the 2014 TVB Europe awards in London, in recognition for its work with British Pathe and the restoration for YouTube. In February 2014, the World Teleport Association named Avi Cohen, CEO of RR Media (formerly RRsat), as its 2014 Teleport Executive of the Year. In 2009, the World Teleport Association awarded RR Media (then RRsat) the Independent Teleport Operator of the Year award for excellence. == History == RR Media (as RRsat) was established in 1981 as a communications provider. The company was founded by David Rivel, an electronics, computers and communications engineer. Rivel is CEO of the company for 31 years and from 2012 a Member of RR Media's board of directors. Under management of Rivel RRsat Communications Network Ltd. went public on 2006-11-01 - NASDAQ:RRST In 2014, the Company rebranded from RRsat Global Communications Network to RR Media. The rebrand was launched at the International Broadcasting Convention (IBC) Show in Amsterdam. In 2015, RR Media announced its NASDAQ stock ticker symbol change to RRM. == Acquisitions == In April 2015, RR Media acquired Eastern Space Systems (ESS) in Romania, a privately held provider of content management and content distribution services and related consulting services. In June 2015, RR Media acquired Satlink Communications as part of strategy to increase scale and expand its global content distribution network and content management footprint, strengthening its customer mix and leverage media industry expertise.
Read more →