AI For Students Copilot

AI For Students Copilot — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Algorithmic probability

In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the method together with Bayes' rule to obtain probabilities of prediction for an algorithm's future outputs. In the mathematical formalism used, the observations have the form of finite binary strings viewed as outputs of Turing machines, and the universal prior is a probability distribution over the set of finite binary strings calculated from a probability distribution over programs (that is, inputs to a universal Turing machine). The prior is universal in the Turing-computability sense, i.e. no string has zero probability. It is not computable, but it can be approximated. Formally, the probability P {\displaystyle P} is not a probability and it is not computable. It is only "lower semi-computable" and a "semi-measure". By "semi-measure", it means that 0 ≤ ∑ x P ( x ) < 1 {\displaystyle 0\leq \sum _{x}P(x)<1} . That is, the "probability" does not actually sum up to one, unlike actual probabilities. This is because some inputs to the Turing machine causes it to never halt, which means the probability mass allocated to those inputs is lost. By "lower semi-computable", it means there is a Turing machine that, given an input string x {\displaystyle x} , can print out a sequence y 1 < y 2 < ⋯ {\displaystyle y_{1} Read more →
Hildon

Hildon is an application framework originally developed for mobile devices (PDAs, mobile phones, etc.) running the Linux operating system as well as the Symbian operating system. The Symbian variant of Hildon was discontinued with the cancellation of Series 90. It was developed by Nokia for the Maemo operating system. It focuses on providing a finger-friendly interface. It is primarily a set of GTK extensions that provide mobile-device–oriented functionality, but also provides a desktop environment that includes a task navigator for opening and switching between programs, a control panel for user settings, and status bar, task bar and home applets. It is standard on the Maemo platform used by the Nokia Internet Tablets and the Nokia N900 smartphone. Hildon has also been selected as the framework for Ubuntu Mobile and Embedded Edition. Hildon was an early instance of a software platform for generic computing in a tablet device intended for internet consumption. But Nokia didn't commit to it as their only platform for their future mobile devices and the project competed against other in-house platforms. The strategic advantage of a modern platform was not exploited, being displaced by the Series 60, though its development is continued by the Maemo Leste project. == Components == The Hildon framework includes components that effectively provide a desktop environment. === Hildon Application Manager === Hildon Application Manager is the Hildon graphical package manager, it uses the Debian package management tools APT (Advanced Packaging Tool and dpkg) and provides a graphical interface for installing, updating and removing packages. It is a limited package manager, designed specifically for end-users, in that it doesn't directly offer the user access to system files and libraries. With the Diablo release of Maemo, Hildon Application Manager now supports "Seamless Software Update" (SSU), which implements a variety of features to allow system upgrades to be easily performed through it. === Hildon Control Panel === Hildon Control Panel is the user settings interface for Hildon. It provides simple access to control panels used to change system settings. === Hildon Desktop === Hildon Desktop is the primary UI component of Hildon, so makes up the bulk of what a user will see as "Hildon". It controls application launching and switching, general system control, and provides interfaces for task bar (application menu and task switcher), status bar (brightness and volume control), and home (internet radio and web search) applets. === Hildon Library === The Hildon library, originally developed by Nokia but since Maemo 5, developed by Igalia and Lanedo (who developed MaemoGTK+, the Maemo version of GTK+). It is a set of mobile specific GTK+ widgets for applications in Maemo. Up to Maemo 4, these widgets were designed for stylus usage. However, in Maemo 5, most widgets were deprecated and new widgets for direct finger manipulation were introduced, including a kinetic panning container.
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Observability (software)

In software engineering, more specifically in distributed computing, observability is the ability to collect data about programs' execution, modules' internal states, and the communication among components. To improve observability, software engineers use a wide range of logging and tracing techniques to gather telemetry information, and tools to analyze and use it. Observability is foundational to site reliability engineering, as it is the first step in triaging a service outage. One of the goals of observability is to minimize the amount of prior knowledge needed to debug an issue. == Etymology, terminology and definition == The term is borrowed from control theory, where the "observability" of a system measures how well its state can be determined from its outputs. Similarly, software observability measures how well a system's state can be understood from the obtained telemetry (metrics, logs, traces, profiling). The definition of observability varies by vendor: Observability is the process of making a system’s internal state more transparent. Systems are made observable by the data they produce, which in turn helps you to determine if your infrastructure or application is healthy and functioning normally. a measure of how well you can understand and explain any state your system can get into, no matter how novel or bizarre [...] without needing to ship new code software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs onobservability starts by shipping all your raw data to central service before you begin analysisthe ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces Observability is tooling or a technical solution that allows teams to actively debug their system. Observability is based on exploring properties and patterns not defined in advance. proactively collecting, visualizing, and applying intelligence to all of your metrics, events, logs, and traces—so you can understand the behavior of your complex digital system The term is frequently referred to as its numeronym o11y (where 11 stands for the number of letters between the first letter and the last letter of the word). This is similar to other computer science abbreviations such as i18n and l10n and k8s. === Observability vs. monitoring === Observability and monitoring are sometimes used interchangeably. As tooling, commercial offerings and practices evolved in complexity, "monitoring" was re-branded as observability in order to differentiate new tools from the old. The terms are commonly contrasted in that systems are monitored using predefined sets of telemetry, and monitored systems may be observable. Majors et al. suggest that engineering teams that only have monitoring tools end up relying on expert foreknowledge (seniority), whereas teams that have observability tools rely on exploratory analysis (curiosity). == Telemetry types == Observability relies on three main types of telemetry data: metrics, logs and traces. Those are often referred to as "pillars of observability". === Metrics === A metric is a point in time measurement (scalar) that represents some system state. Examples of common metrics include: number of HTTP requests per second; total number of query failures; database size in bytes; time in seconds since last garbage collection. Monitoring tools are typically configured to emit alerts when certain metric values exceed set thresholds. Thresholds are set based on knowledge about normal operating conditions and experience. Metrics are typically tagged to facilitate grouping and searchability. Application developers choose what kind of metrics to instrument their software with, before it is released. As a result, when a previously unknown issue is encountered, it is impossible to add new metrics without shipping new code. Furthermore, their cardinality can quickly make the storage size of telemetry data prohibitively expensive. Since metrics are cardinality-limited, they are often used to represent aggregate values (for example: average page load time, or 5-second average of the request rate). Without external context, it is impossible to correlate between events (such as user requests) and distinct metric values. === Logs === Logs, or log lines, are generally free-form, unstructured text blobs that are intended to be human readable. Modern logging is structured to enable machine parsability. As with metrics, an application developer must instrument the application upfront and ship new code if different logging information is required. Logs typically include a timestamp and severity level. An event (such as a user request) may be fragmented across multiple log lines and interweave with logs from concurrent events. === Traces === ==== Distributed traces ==== A cloud native application is typically made up of distributed services which together fulfill a single request. A distributed trace is an interrelated series of discrete events (also called spans) that track the progression of a single user request. A trace shows the causal and temporal relationships between the services that interoperate to fulfill a request. Instrumenting an application with traces means sending span information to a tracing backend. The tracing backend correlates the received spans to generate presentable traces. To be able to follow a request as it traverses multiple services, spans are labeled with unique identifiers that enable constructing a parent-child relationship between spans. Span information is typically shared in the HTTP headers of outbound requests. === Continuous profiling === Continuous profiling is another telemetry type used to precisely determine how an application consumes resources. === Instrumentation === To be able to observe an application, telemetry about the application's behavior needs to be collected or exported. Instrumentation means generating telemetry alongside the normal operation of the application. Telemetry is then collected by an independent backend for later analysis. In fast-changing systems, instrumentation itself is often the best possible documentation, since it combines intention (what are the dimensions that an engineer named and decided to collect?) with the real-time, up-to-date information of live status in production. Instrumentation can be automatic, or custom. Automatic instrumentation offers blanket coverage and immediate value; custom instrumentation brings higher value but requires more intimate involvement with the instrumented application. Instrumentation can be native - done in-code (modifying the code of the instrumented application) - or out-of-code (e.g. sidecar, eBPF). Verifying new features in production by shipping them together with custom instrumentation is a practice called "observability-driven development". == "Pillars of observability" == Metrics, logs and traces are most commonly listed as the pillars of observability. Majors et al. suggest that the pillars of observability are high cardinality, high-dimensionality, and explorability, arguing that runbooks and dashboards have little value because "modern systems rarely fail in precisely the same way twice." == Self monitoring == Self monitoring is a practice where observability stacks monitor each other, in order to reduce the risk of inconspicuous outages. Self monitoring may be put in place in addition to high availability and redundancy to further avoid correlated failures.
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StatCrunch

StatCrunch is a web-based statistical software application from Pearson Education. StatCrunch was originally created for use in college statistics courses. As a full-featured statistics package, it is now also used for research and for other statistical analysis purposes. == History == American statistics professor Webster West created StatCrunch in 1997. Over the next 19 years West assisted by others added many more statistical procedures and graphing capabilities, and made user interface improvements. In 2005, West received two awards for StatCrunch: the CAUSEweb Resource of the Year Award and the MERLOT Classics Award. In 2013, the StatCrunch Java code was rewritten in JavaScript in order to avoid Java browser security problems, and so that it would run on iOS and Android. In 2015, new ways of importing data were added, including importing multi-page data directly from Wikipedia tables and other Web sources, and also importing with drag-and-drop for various data formats. In 2016, StatCrunch was acquired by Pearson Education, which had already been serving as the primary distributor of StatCrunch for several years. == Software == A StatCrunch license is included with many of Pearson's statistical textbooks. Because StatCrunch is a web application, it works on multiple platforms, including Windows, macOS, iOS, and Android. Data in StatCrunch is represented in a "data table" view, which is similar to a spreadsheet view, but unlike spreadsheets, the cells in a data table can only contain numbers or text. Formulas cannot be stored in these cells. There are many ways to import data into StatCrunch. Data can be typed directly into cells in the data table. Entire blocks of data may be cut-and-pasted into the data table. Text files (.csv, .txt, etc.) and Microsoft Excel files (.xls and .xlsx) can be drag-and-dropped into the data table. Data can be pulled into StatCrunch directly from Wikipedia tables or other Web tables, including multi-page tables. Data can be loaded directly from Google Drive and Dropbox. Shared data sets saved by other StatCrunch community users can be searched for by title or keyword and opened in a data table. Graphs, results, and reports created by StatCrunch can be shared with other users, in addition to the sharing of data sets. StatCrunch has a library of data transformation functions. StatCrunch can also recode and reorganize data. All data is stored in memory, and all processing happens on the client, so response is fast, even with large data sets. StatCrunch can interact with multiple graphs simultaneously. If a user selects a data point on one graph, then that same data point is highlighted on all other displayed graphs. In addition to standard statistical and graphing procedures, StatCrunch has a collection of about forty "applets" which illustrate statistical concepts interactively.
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Neurocomputing (journal)

Neurocomputing is a peer-reviewed scientific journal covering research on artificial intelligence, machine learning, and neural computation. It was established in 1989 and is published by Elsevier. The editor-in-chief is Zidong Wang (Brunel University London). Independent scientometric studies noted that despite being one of the most productive journals in the field, it has kept its reputation across the years intact and plays an important role in leading the research in the area. The journal is abstracted and indexed in Scopus and Science Citation Index Expanded. According to the Journal Citation Reports, its 2023 impact factor is 5.5.
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Clarizen

Clarizen, Inc. is a project management software and collaborative work management company. Clarizen uses a software as a service business model. Clarizen's features include attaching CAD drawings to a project, moving between the project view and design view and an E-mail reporting feature. In May 2014 Clarizen raised $35 million in venture capital investment led by Goldman Sachs. The round brought investment to $90 million. Previous investors, including Benchmark Capital, Carmel Ventures, DAG Ventures, Opus Capital and Vintage Investment Partners participated. In April 2020, Clarizen appointed Matt Zilli as its new CEO, replacing Boaz Chalamish who is appointed as Executive Chairman. In January 2021 Clarizen was acquired by Planview.
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Protocol Builder

Protocol Builder is a tool in programming languages to generate code to build protocols in a fast and reliable way. Network programming for all kinds of protocols (such as TCP, UDP, and SNMP) includes converting data to be transferred to raw bytes in the sending side and parsing these bytes in the receiving side. Protocol builders facilitate this stage, usually by automatically generating the code. Protocol Programming has many components to be developed, these are: server listener, server connection, client connection, packets, and loggers. Most protocol builders implement these components automatically so developers save time and money. Currently, there are two Protocol Builders in the market, one for C++ from UpRedSun which is for TCP and UDP protocols. The second one is for .Net languages which generates the code in C# for TCP Protocols, this tool is called .Net Protocol Builder.
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Product-family engineering

Product-family engineering (PFE), also known as product-line engineering (PLE), is based on the ideas of "domain engineering" created by the Software Engineering Institute, a term coined by James Neighbors in his 1980 dissertation at University of California, Irvine. Software product lines are quite common in our daily lives, but before a product family can be successfully established, an extensive process has to be followed. This process is known as product-family engineering. Product-family engineering can be defined as a method that creates an underlying architecture of an organization's product platform. It provides an architecture that is based on commonality as well as planned variabilities. The various product variants can be derived from the basic product family, which creates the opportunity to reuse and differentiate on products in the family. Product-family engineering is conceptually similar to the widespread use of vehicle platforms in the automotive industry. Product-family engineering is a relatively new approach to the creation of new products, recently evolving to Model-Based Product Line Engineering (MBPLE), emphasizing the centrality of a model-centric approach in PLE. It focuses on the process of engineering new products in such a way that it is possible to reuse product components and apply variability with decreased costs and time. Product-family engineering is all about reusing components and structures as much as possible, according to the ISO/IEC 26550/2015 and the latest ISO/IEC 26580/2021 that introduced the concept of feature-based Product Line Engineering. Several studies have proven that using a product-family engineering approach for product development can have several benefits. Here is a list of some of them: Higher productivity Higher quality Faster time-to-market Lower labor needs The Nokia case mentioned below also illustrates these benefits. In 2025 the publishing of the book Model-Based Product Line Engineering (MBPLE): The feature-based path to product lines success by Marco Forlingieri, Tim Weilkiens and Hugo Guillermo Chalé-Gongora formalized the foundation of the discipline, including best practices and new industrial cases. == Overall process == The product family engineering process consists of several phases. The three main phases are: Phase 1: Product management Phase 2: Domain engineering Phase 3: Product engineering The process has been modeled on a higher abstraction level. This has the advantage that it can be applied to all kinds of product lines and families, not only software. The model can be applied to any product family. Figure 1 (below) shows a model of the entire process. Below, the process is described in detail. The process description contains elaborations of the activities and the important concepts being used. All concepts printed in italic are explained in Table 1. === Phase 1: product management === The first phase is the starting up of the whole process. In this phase some important aspects are defined especially with regard to economic aspects. This phase is responsible for outlining market strategies and defining a scope, which tells what should and should not be inside the product family. ==== Evaluate business visioning ==== During this first activity all context information relevant for defining the scope of the product line is collected and evaluated. It is important to define a clear market strategy and take external market information into account, such as consumer demands. The activity should deliver a context document that contains guidelines, constraints and the product strategy. ==== Define product line scope ==== Scoping techniques are applied to define which aspects are within the scope. This is based upon the previous step in the process, where external factors have been taken into account. The output is a product portfolio description, which includes a list of current and future products and also a product roadmap. It can be argued whether phase 1, product management, is part of the product-family-engineering process, because it could be seen as an individual business process that is more focused on the management aspects instead of the product aspect. However phase 2 needs some important input from this phase, as a large piece of the scope is defined in this phase. So from this point of view it is important to include the product-management phase (phase 1) into the entire process as a base for the domain-engineering process. === Phase 2: domain engineering === During the domain-engineering phases, the variable and common requirements are gathered for the whole product line. The goal is to establish a reusable platform. The output of this phase is a set of common and variable requirements for all products in the product line. ==== Analyze domain requirements ==== This activity includes all activities for analyzing the domain with regard to concept requirements. The requirements are categorized and split up into two new activities. The output is a document with the domain analysis. As can be seen in Figure 1 the process of defining common requirements is a parallel process with defining variable requirements. Both activities take place at the same time. ==== Define common requirements ==== Includes all activities for eliciting and documenting the common requirements of the product line, resulting in a document with reusable common requirements. ==== Define variable requirements ==== Includes all activities for eliciting and documenting the variable requirements of the product line, resulting in a document with variable requirements. ==== Design domain ==== This process step consists of activities for defining the reference architecture of the product line. This generates an abstract structure for all products in the product line. ==== Implement domain ==== During this step a detailed design of the reusable components and the implementation of these components are created. ==== Test domain ==== Validates and verifies the reusability of components. Components are tested against their specifications. After successful testing of all components in different use cases and scenarios, the domain engineering phase has been completed. === Phase 3: product engineering === In the final phase a product X is being engineered. This product X uses the commonalities and variability from the domain engineering phase, so product X is being derived from the platform established in the domain engineering phase. It basically takes all common requirements and similarities from the preceding phase plus its own variable requirements. Using the base from the domain engineering phase and the individual requirements of the product engineering phase a complete and new product can be built. After the product has been fully tested and approved, the product X can be delivered. ==== Define product requirements ==== Developing the product requirements specification for the individual product and reuse the requirements from the preceding phase. ==== Design product ==== All activities for producing the product architecture. Makes use of the reference architecture from the step "design domain", it selects and configures the required parts of the reference architecture and incorporates product specific adaptations. ==== Build product ==== During this process the product is built, using selections and configurations of the reusable components. ==== Test product ==== During this step the product is verified and validated against its specifications. A test report gives information about all tests that were carried out, this gives an overview of possible errors in the product. If the product in the next step is not accepted, the process will loop back to "build product", in Figure 1 this is indicated as "[unsatisfied]". ==== Deliver and support product ==== The final step is the acceptance of the final product. If it has been successfully tested and approved to be complete, it can be delivered. If the product does not satisfy to the specifications, it has to be rebuilt and tested again. The next figure shows the overall process of product-family engineering as described above. It is a full process overview with all concepts attached to the different steps. == Process data diagram == On the left side the entire process from the top to bottom has been drawn. All activities on the left side are linked to the concepts on the right side through dotted lines. Every concept has a number, which reflects the association with other concepts. == List of concepts == Below the list with concepts will be explained. Most concept definitions are extracted from Pohl, Bockle, & Linden (2005) and also some new definitions have been added. Table 1: List of concepts == Example == There are some good examples of the use of product family engineering, which were quite successful. The abstract model of product family engineering allows different kinds of uses, most of them are related to the consumer electronics m
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Automated dispensing cabinet

An automated dispensing cabinet (ADC), also called a unit-based cabinet (UBC), automated dispensing device (ADD), or automated dispensing machine (ADM)[1], is a computerized medicine cabinet for hospitals and healthcare settings. ADCs allow medications to be stored and dispensed near the point of care while controlling and tracking drug distribution. == Overview == Hospital pharmacies have provided medications for patients by filling patient-specific cassettes of unit-dose medications that were then delivered to the nursing unit and stored in medication cabinets or carts. ADCs, originally designed for hospital use, were introduced in hospitals in the 1980s and have facilitated the transition to alternative delivery models and more decentralized medication distribution systems.[2] Implementing automated dispensing cabinets as part of a decentralized or hybrid medication distribution system can improve patient safety and the accountability of the inventory, streamline certain billing processes. However, in the 2000s, the technology began to be deployed into other care settings where medication doses were stored onsite, and higher security methods were needed to control inventory, access, and dispensing of each patient dose. Settings that now deploy ADCs include long-term care facilities, hospice, critical access hospitals, surgery centers, group homes, residential care facilities, rehab and psych environments, animal health, dental clinics, and nursing education simulation. These diverse care settings share a common need to safely store, account for, and dispense individual doses of medications, especially narcotics and high-value medications, at the point of care.[3] ADCs track user access and dispensed medications, and their use can improve control over medication inventory. The real-time inventory reports generated by many cabinets can simplify the filling process and help the pharmacy track expired drugs. Furthermore, by restricting individual drugs – such as high-risk medications and controlled substances – to unique drawers within the cabinet, overall inventory management, patient safety, and medication security can be improved. Automated dispensing cabinets allow the pharmacy department to profile physician orders before they are dispensed.[4] ADCs can also enable providers to record medication charges upon dispensing, reducing the billing paperwork the pharmacy is responsible for. In addition, nurses can note returned medications using the cabinets' computers, enabling direct credits to patients' accounts. Since automated cabinets can be located on the nursing unit floor, nursing have speedier access to a patient's medications. Also, shorter waiting time ensures improved patient comfort and care.[5] == Role of automated dispensing in healthcare == Automated dispensing is a pharmacy practice in which a device dispenses medications and fills prescriptions. ADCs, which can handle many different medications, are available from a number of manufacturers such as BD, ARxIUM, and Omnicell. Though members of the pharmacy community have been utilizing automation technology since the 1980s, companies are constantly improving ADCs to meet changing needs and health standards in the industry. Several goals can be met by implementing an automated product in a healthcare facility. Patient safety can be ensured with the use of ADC technology such as barcoding. Anesthesia ADCs in operating rooms and perioperative areas may include label printing to prevent mix-ups such as errors between morphine and hydromorphone, two different opioid analgesics that frequently get confused. These systems also communicate with the pharmacy and its information management system to track medications removed and support inventory replenishment. == Key features == ADCs are like automated teller machines whose specific technologies such as barcode scanning and clinical decision support can improve medication safety. Some have metal locking drawers for added security and some have automated single-dose dispensing to prevent the need for a blind count each time a controlled substance is accessed. Over the years, ADCs have been adapted to facilitate compliance with emerging regulatory requirements such as pharmacy review of medication orders and safe practice recommendations. ADCs incorporate advanced software and electronic interfaces to synthesize high-risk steps in the medication use process. These unit-based medication repositories provide computer-controlled storage, dispensation, tracking, and documentation of medication distribution in the resident care unit. Since automated dispensing cabinets are not located in the pharmacy, they are considered "decentralized" medication distribution systems. Instead, they can be found at the point of care on the resident care unit. Tracking of the stocking and distribution process can occur by interfacing the unit with a central pharmacy computer. These cabinets can also be interfaced with other external databases such as resident profiles, the facility's admission/discharge/transfer system, and billing systems. Most ADC providers offer scalable systems since several important factors vary widely by facility such as budget, physical room size, patient population/demographics, type of healthcare facility, etc.
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IgHome

igHome is a customizable start page introduced in 2012 as an alternative to iGoogle, the personal web portal launched by Google in May 2005. Just like iGoogle, igHome offers users the possibility to build a start page containing a central search box and a number of gadgets. igHome mimics the user interface of iGoogle. Registered igHome users can create multiple tabs and import RSS feeds.
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Scroll (web service)

Scroll was a subscription-based web service developed by Scroll Labs Inc., offering ad-free access to websites in exchange for a fee. Scroll was not an ad blocker; instead, it partnered directly with internet publishers who voluntarily removed ads from their sites for Scroll users in exchange for a portion of the subscription fee. In May 2021, Scroll was acquired by Twitter. In October 2021, Scroll sent out an email announcing its integration into Twitter Blue within 30 days. == Functionality == Scroll enabled users to browse websites that partnered with Scroll without encountering online advertising, in exchange for a subscription fee. Unlike ad blocker, which disable advertisements without compensating the publisher, Scroll sent a browser cookie indicating that the user was a subscriber. The Scroll software integrated into the website detected this cookie and served an ad-free version of the site. In exchange for disabling advertisements, partner websites received a portion of the subscription fee. As of January 2020, Scroll retained 30% of the subscription fee, with the remaining 70% distributed among publisher sites. Payments to sites were made individually by users based on their 'engagement and loyalty,' rather than from a single pool of all subscription revenue. Scroll did not grant subscribers access to partner sites behind a paywall; it only removed ads from the site if the user also paid the publication's subscription fee. == History == Scroll was founded in 2016 by former Chartbeat Chief Executive Tony Haile. Scroll raised US$3 million in its first round of funding in 2016, including investments from The New York Times, Uncork Capital, and Axel Springer SE. By October 2018, Scroll had raised US$10 million in funding. In 2018, Scroll signed its first partner websites, which included The Atlantic, Fusion Media Group, Business Insider, Slate, MSNBC, The Philadelphia Inquirer, and Talking Points Memo. In February 2019, Scroll acquired the social media curation app Nuzzel. The same month, Mozilla and Scroll announced a partnership to run a "test pilot" together, but did not go into details. Scroll entered beta testing in 2019 and launched to the general public on January 28, 2020. In March 2020, Mozilla started offering Scroll as part of its "Firefox Better Web" service bundle. In May 2021, Scroll was acquired by Twitter, with the future of Scroll cited as being uncertain. An email to customers announcing the change said, "Later this year, Scroll will become part of a wider Twitter subscription that will expand on and adapt our services and functionality".
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Cloud load balancing

Cloud load balancing is a type of load balancing that is performed in cloud computing. Cloud load balancing is the process of distributing workloads across multiple computing resources. Cloud load balancing reduces costs associated with document management systems and maximizes availability of resources. It is a type of load balancing and not to be confused with Domain Name System (DNS) load balancing. While DNS load balancing uses software or hardware to perform the function, cloud load balancing uses services offered by various computer network companies. == Comparison With DNS load balancing == Cloud load balancing has an advantage over DNS load balancing as it can transfer loads to servers globally as opposed to distributing it across local servers. In the event of a local server outage, cloud load balancing delivers users to the closest regional server without interruption for the user. Cloud load balancing addresses issues relating to TTL reliance present during DNS load balancing. DNS directives can only be enforced once in every TTL cycle and can take several hours if switching between servers during a lag or server failure. Incoming server traffic will continue to route to the original server until the TTL expires and can create an uneven performance as different internet service providers may reach the new server before other internet service providers. Another advantage is that cloud load balancing improves response time by routing remote sessions to the best performing data centers. == Importance of Load Balancing == Cloud computing brings advantages in "cost, flexibility and availability of service users." Those advantages drive the demand for Cloud services. The demand raises technical issues in Service Oriented Architectures and Internet of Services (IoS)-style applications, such as high availability and scalability. As a major concern in these issues, load balancing allows cloud computing to "scale up to increasing demands" by efficiently allocating dynamic local workload evenly across all nodes. == Load Balancing Techniques == === Scheduling Algorithms === Opportunistic Load Balancing (OLB) is the algorithm that assigns workloads to nodes in free order. It is simple but does not consider the expected execution time of each node. Load balance Min-Min (LBMM) assigns sub-tasks to the node which requires minimum execution time. === Load Balancing Policies === Workload and Client Aware Policy (WCAP) specifies the unique and special property (USP) of requests and computing nodes. With the information of USP, the schedule can decide the most suitable node to complete a request. WCAP makes the most of computing nodes by reducing their idle time. Also, it reduces performance time through searches based on content information. === A Comparative Study of Algorithms === Biased Random Sampling bases its job allocation on the network represented by a directed graph. For each execution node in this graph, in-degree means available resources and out-degree means allocated jobs. In-degree will decrease during job execution while out-degree will increase after job allocation. Active Clustering is a self-aggregation algorithm to rewire the network. The experiment result is that"Active Clustering and Random Sampling Walk predictably perform better as the number of processing nodes is increased" while the Honeyhive algorithm does not show the increasing pattern. == Client-side Load Balancer Using Cloud Computing == Load balancer forwards packets to web servers according to different workloads on servers. However, it is hard to implement a scalable load balancer because of both the "cloud's commodity business model and the limited infrastructure control allowed by cloud providers." Client-side Load Balancer (CLB) solve this problem by using a scalable cloud storage service. CLB allows clients to choose back-end web servers for dynamic content although it delivers static content.
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Amália (LLM)

Amália is a Portuguese large language model (LLM) announced in November 2024 by the Portuguese Prime-Minister Luís Montenegro. Its final version is expected to be launched in 2026. It is being developed by Center for Responsible AI (Centro para a AI Responsável) and by the research centers of NOVA School of Science and Technology and Instituto Superior Técnico. == History == In 2024 it was announced that the Portuguese Agency for Administrative Modernization (Agência para a Modernização Administrativa) transpose this LLM to Portuguese Public Administration. According to Paulo Dimas (CEO of the Center for Responsible AI) the three fundamental points of this LLM project are the linguistic variant (European Portuguese), cultural representation and data protection. In April 2025 it was announced that Amália had entered beta phase with an improved version being expected to be launched in September 2025. The beta version released in September is available only to the Public Administration, but the website launched in October reiterates the final version will be an open model.
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Thinkfree Office

Thinkfree Office is a web-based commercial office productivity suite developed by South Korea-based Thinkfree Inc. It includes Word (a word processor), Spreadsheet (a spreadsheet) and Presentation (a presentation program). They are compatible with Microsoft Office's Word, PowerPoint, and Excel. It also features collaborative editing. The product is hosted on the client's server. == Supported file formats == Thinkfree Office supports ISO/IEC international standard ISO/IEC 26300 Open Document Format for Office Applications (odf, odt, odp, ods, odg). It also supports Microsoft's XML formats (docx, pptx, xlsx) and Microsoft's legacy binary formats (doc, ppt, xls). == Naming == The software was previously marketed under different names, such as Thinkfree Server, Thinkfree Online, Hancom Office Online, and Hancom Office Web. Eventually, the brand was consolidated under the name Thinkfree Office. == History == In June 2000, Thinkfree Inc. released Thinkfree Office, based in Silicon Valley, California. It is recognized as the world's first online office editor (predating Google Docs and Microsoft 365) and attracted significant media coverage, including reports on CNN. In 2001, Microsoft CEO Steve Ballmer highlighted Thinkfree as a significant competitor in a magazine interview, considering it a potential threat to his company, second only to Linux. In November 2003, Hancom, a South Korean office software company, signed a memorandum of understanding and subsequently acquired Thinkfree. In January 2004, Thinkfree expanded into other foreign markets. Subsidiary Haansoft USA, Inc. was created in San Jose, California to begin formal commercial operations in the US market. At the same time, a partnership was established with Riverdeep with the purpose of improving marketshare. In February 2004, expansion into the Japanese market began. A commercial agency agreement was signed with PSI in Shinjuku, Japan, which allowed for localized distribution. In addition, a global agreement was entered into with Yamada Denki, one of the three main computer distributors in Japan, for a total of 180,000 units. In May 2006, Thinkfree Office received the "Product of the Year" award at the Well-Connected Awards, USA. In January 2009, Thinkfree Mobile was launched at CES 2009 in Las Vegas. In April 2009, Thinkfree Live, Korea's first web office service, was launched. In June 2018, a partnership was formed with Amazon Web Services to integrate Thinkfree Office into WorkDocs, an in-house office suite. In October 2023, Hancom split its online office business unit as "Thinkfree Inc.".
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Gemini Enterprise Agent Platform

Gemini Enterprise Agent Platform (formerly known as Vertex AI) is a managed machine learning (ML) and artificial intelligence (AI) platform developed by Google Cloud. It provides a unified environment for building, training, deploying, and scaling ML models and generative AI applications. The platform integrates tools for the full ML lifecycle, including data preparation, model training, evaluation, deployment, and monitoring, under a single API and user interface. Vertex AI was announced at Google I/O and released as a generally available product on May 18, 2021. At launch, Google described Vertex AI as unifying its AutoML offerings with its prior Cloud AI Platform capabilities, and as adding operational features intended to help teams move models from experimentation into production use. On April 22, 2026, Google announced Gemini Enterprise Agent Platform as the replacement evolution of Vertex AI. == History == Google Cloud announced the general availability of Vertex AI on May 18, 2021, at the Google I/O developer conference. The platform was designed to consolidate Google Cloud's previously separate ML offerings, including AutoML and the legacy AI Platform, into a single system. At launch, Google claimed that Vertex AI required roughly 80% fewer lines of code to train a model compared to competing platforms. In June 2023, Google made generative AI support in Vertex AI generally available, giving developers access to foundation models including PaLM 2, Imagen, and Codey through the platform's Model Garden and the newly launched Generative AI Studio. At the time of this launch, Model Garden included over 60 models from Google and its partners. In August 2023, at the Google Cloud Next conference, Google announced further updates to Vertex AI, including the addition of third-party models such as Claude 2 from Anthropic and Llama 2 from Meta to the Model Garden, as well as new tools called Vertex AI Extensions for connecting models to APIs for real-time data retrieval. At the same event, Vertex AI Search and Conversation were made generally available, providing enterprise search and chatbot capabilities powered by foundation models. In April 2024, at Google Cloud Next, the company introduced Vertex AI Agent Builder, a no-code tool for creating AI-powered conversational agents built on top of Gemini large language models. This brought together the existing Vertex AI Search and Conversation products with new developer tools for building generative AI experiences. == Features == === Model training === Vertex AI supports both AutoML, which enables code-free model training on tabular, image, text, or video data, and custom training, which gives users full control over the ML framework, training code, and hyperparameter tuning. The platform provides serverless training as well as dedicated training clusters with GPU and TPU accelerators. Vertex AI Vizier handles automatic hyperparameter tuning, and Vertex AI Experiments allows comparison and tracking of training runs. === Model Garden === The Vertex AI Model Garden is a curated catalog of over 200 enterprise-ready models, including Google's own foundation models (such as Gemini, Imagen, and Veo), third-party models (such as Anthropic's Claude and Mistral AI models), and popular open-source models (such as Llama and Gemma). Models are accessible as fully managed model-as-a-service APIs. === Pipelines (workflow orchestration) === Vertex AI Pipelines provides managed orchestration of ML workflows and supports pipelines built with the Kubeflow Pipelines SDK, among other options described in Google Cloud documentation. === Vertex AI Studio === Vertex AI Studio provides tools for prompt design, testing, and model management, allowing developers to prototype and build generative AI applications using natural language, code, images, or video. === Agent Builder and Agent Engine === Vertex AI Agent Builder is a suite of products for building, deploying, and governing AI agents in production environments. It supports development with the open-source Agent Development Kit (ADK) and other frameworks. Vertex AI Agent Engine provides the underlying infrastructure for deploying and scaling agents, with support for enterprise security features including HIPAA compliance, customer-managed encryption keys (CMEK), and VPC Service Controls. === Generative AI tooling and model access === Google markets Vertex AI as providing access to Google foundation models (including the Gemini family) and developer tools such as Vertex AI Studio, along with a model catalog that includes Google and selected open source models (marketed as "Model Garden"). Google has also offered products within Vertex AI aimed at building generative search and conversational applications, including offerings named "Vertex AI Search" and "Vertex AI Conversation" as reported in 2023 coverage of platform updates. === MLOps tools === The platform includes a range of MLOps capabilities: Vertex AI Pipelines for orchestrating and automating ML workflows as reusable pipelines. Vertex AI Feature Store for serving, sharing, and reusing ML features across projects. Vertex AI Model Registry for storing, versioning, and managing trained models. Vertex AI Model Monitoring for detecting training-serving skew and inference drift in deployed models. Vertex Explainable AI for interpreting model predictions. Vertex AI Workbench for managed JupyterLab notebook environments integrated with Google Cloud Storage and BigQuery. == Industry recognition == Google was named a Leader for the fifth consecutive year in the 2024 Gartner Magic Quadrant for Cloud AI Developer Services, a recognition that encompasses Vertex AI and its related offerings. Google was also recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms and was named a Leader in the Forrester Wave for AI/ML Platforms, Q3 2024. In October 2025, Google was also named a Leader in the 2025 IDC (International Data Corporation) MarketScape for Worldwide GenAI Life-Cycle Foundation Model Software. == Pricing == Vertex AI uses a pay-as-you-go pricing model, with costs determined by the specific services consumed, including model training, prediction serving, and data storage. For generative AI tasks, pricing is based on a per-token model, with rates varying depending on the specific model used and whether tokens are input or output. Google offers a free tier for new users, which includes limited custom training hours and online prediction usage, along with an introductory US$300 in Google Cloud credits valid for 90 days. == Adoption == In the year following its 2021 launch, Google reported that usage of Vertex AI and BigQuery had driven 2.5 times more machine learning predictions compared to the prior year, and that active customers of Vertex AI Workbench had grown 25-fold over a six-month period. Early enterprise adopters included Ford, Wayfair, and Seagate, among others. Wayfair reported that it was able to run large model training jobs 5 to 10 times faster using the platform.
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