AI Email Write Up

AI Email Write Up — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Application Lifecycle Framework

The Application Lifecycle Framework (ALF) was a project by the Eclipse Foundation that aimed to create a standardized, open-source system to allow different application lifecycle management (ALM) tools to work together more easily. The goal was to provide common protocols and integration services that would let software development tools from different vendors communicate and share data. However, the project failed to gain sufficient support from major industry players and was terminated in 2008.
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Princh

Princh is a Danish software company, which is headquartered in Aarhus, Denmark. Founded in 2015, Princh develops cloud printing and electronic payment products. The company is headquartered in the city of Aarhus. While utilizing a smartphone or web app, users can locate a nearby printer to their current location, get directions to access said printer, and/or authorize a print and pay for the print job in question. The product is available as a native mobile apps for Android and iOS, as well as on web and desktop products for businesses and libraries. The app connects a network of printer owners and users around the world. Princh supports an array of printable files. == History == The company was founded in 2015. The company is currently based in the southern part of Aarhus. The Princh printing service was officially launched on June 23, 2015. Currently, Princh is available as a service in a multitude of locations such as print shops, libraries, hotels, or universities. Princh is a popular printing and payment product among libraries and can among other places be found in Denmark, Sweden, Norway, Germany, United Kingdom, United States, and Canada. == How it works == With the Princh app, users will be able to locate their nearest printer. Once the user is at the printer, the user chooses the document to be printed out and shares it with the Princh app. The user then selects the desired nearby printer entering the printer ID number or scanning the QR-code located on top of the printer, pays electronically and the print job is processed by the printer. Printer owners get access to a personal control panel where they can set printing prices and monitor all Princh activity for their business. == Notes and references ==
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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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Local-first software

Local-first software is a software engineering approach in which an application stores its data primarily on the user's own device rather than on remote servers. Users can read and write data without an Internet connection, and changes are synchronized across devices in the background when connectivity is available. The approach differs from conventional cloud-based applications, where the server holds the authoritative copy of user data and the client acts as a thin client. The term was coined in a 2019 paper published by researchers at Ink & Switch, an independent research lab, and presented at the Onward! conference at ACM SIGPLAN. The paper, sometimes referred to as a manifesto, was authored by Martin Kleppmann, Adam Wiggins, Peter van Hardenberg, and Mark McGranaghan. == Background == Before the widespread adoption of Internet-connected software in the 2000s, most desktop applications stored data as files on the user's local disk. Users had direct access to their files and could copy, back up, or delete them at will. The rise of software as a service (SaaS) and cloud-based applications like Google Docs shifted data storage to centralized servers. While cloud applications made real-time collaboration across devices straightforward, they introduced a dependency on the service provider: if the provider discontinued the service or experienced an outage, users could lose access to their data. A related concept, "offline-first," emerged in the early 2010s and focused on making web applications resilient to network interruptions. The local-first approach built on these earlier efforts while placing greater emphasis on long-term data ownership and end-to-end encryption. == Origins == === Ink & Switch manifesto === Ink & Switch is an industrial research lab co-founded by Adam Wiggins, who had earlier co-founded Heroku. Martin Kleppmann, an associate professor in the Department of Computer Science and Technology at the University of Cambridge, was a co-author of the 2019 paper. The manifesto proposed seven "ideals" for local-first software: Fast — Operations respond without network round-trips. Multi-device — Data synchronizes across a user's devices. Offline — Users can read and write data without a network connection. Collaboration — Multiple users can work on the same data concurrently. Longevity — Data remains accessible even if the software vendor ceases operation. Privacy — End-to-end encryption protects user data. User control — The vendor cannot restrict how users access or use their data. The paper surveyed existing approaches to data storage and collaboration — ranging from email attachments and Dropbox-style file synchronization to web applications and mobile backends — and argued that none of them satisfied all seven ideals simultaneously. === Role of CRDTs === The manifesto identified conflict-free replicated data types (CRDTs) as a promising technical foundation for local-first applications. CRDTs are data structures that allow multiple replicas to be edited independently and then merged without conflicts, a property first formalized in research by Marc Shapiro and colleagues around 2011. Kleppmann and collaborators at Ink & Switch developed Automerge, an open-source CRDT library for JSON documents, to make these algorithms available to application developers. == Adoption and community == Developer interest in the local-first approach grew after the 2019 paper spread on Hacker News and at developer conferences In August 2023, Wired published a feature article on the movement, describing it as an effort to reduce reliance on large cloud providers. The first Local-First Conf took place on 30 May 2024 in Berlin, with talks by Kleppmann and developers from companies including Linear and Anytype. The community has continued to expand, with regular "LoFi" meetups, a podcast (localfirst.fm), and a third edition of the conference planned for Berlin in July 2026. == Criticisms and limitations == Developers and commentators have pointed out practical difficulties with the local-first approach. Synchronizing data between multiple devices that may be offline for extended periods introduces complexity that cloud-based architectures avoid. Conflict resolution, even with CRDTs, can produce results that are technically consistent but semantically unexpected to users. Schema migrations across thousands of client devices running different application versions pose another difficulty that does not arise with server-side databases. Web browsers impose storage limits and may evict locally stored data. Safari, for instance, has been reported to clear IndexedDB data after seven days of inactivity on a given site, which undermines the assumption that local data is persistent. There is also disagreement within the local-first community about whether a fully decentralized architecture is required. The original manifesto described decentralization as the "logical end goal," but a number of products that identify as local-first still depend on centralized servers for authentication, backup, or synchronization. In a talk at Local-First Conf 2024, Kleppmann said the seven ideals are better understood as a "gradient" rather than a strict checklist.
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Adversarial stylometry

Adversarial stylometry is the practice of altering writing style to reduce the potential for stylometry to discover the author's identity or their characteristics. This task is also known as authorship obfuscation or authorship anonymisation. Stylometry poses a significant privacy challenge in its ability to unmask anonymous authors or to link pseudonyms to an author's other identities, which, for example, creates difficulties for whistleblowers, activists, and hoaxers and fraudsters. The privacy risk is expected to grow as machine learning techniques and text corpora develop. All adversarial stylometry shares the core idea of faithfully paraphrasing the source text so that the meaning is unchanged but the stylistic signals are obscured. Such a faithful paraphrase is an adversarial example for a stylometric classifier. Several broad approaches to this exist, with some overlap: imitation, substituting the author's own style for another's; translation, applying machine translation with the hope that this eliminates characteristic style in the source text; and obfuscation, deliberately modifying a text's style to make it not resemble the author's own. Manually obscuring style is possible, but laborious; in some circumstances, it is preferable or necessary. Automated tooling, either semi- or fully-automatic, could assist an author. How best to perform the task and the design of such tools is an open research question. While some approaches have been shown to be able to defeat particular stylometric analyses, particularly those that do not account for the potential of adversariality, establishing safety in the face of unknown analyses is an issue. Ensuring the faithfulness of the paraphrase is a critical challenge for automated tools. It is uncertain if the practice of adversarial stylometry is detectable in itself. Some studies have found that particular methods produced signals in the output text, but a stylometrist who is uncertain of what methods may have been used may not be able to reliably detect them. == History == Rao & Rohatgi (2000), an early work in adversarial stylometry, identified machine translation as a possibility, but noted that the quality of translators available at the time presented severe challenges. Kacmarcik & Gamon (2006) is another early work. Brennan, Afroz & Greenstadt (2012) performed the first evaluation of adversarial stylometric methods on actual texts. Brennan & Greenstadt (2009) introduced the first corpus of adversarially authored texts specifically for evaluating stylometric methods; other corpora include the International Imitation Hemingway Competition, the Faux Faulkner contest, and the hoax blog A Gay Girl in Damascus. == Motivations == Rao & Rohatgi (2000) suggest that short, unattributed documents (i.e., anonymous posts) are not at risk of stylometric identification, but pseudonymous authors who have not practiced adversarial stylometry in producing corpuses of thousands of words may be vulnerable. Narayanan et al. (2012) attempted large-scale deanonymisation of 100,000 blog authors with mixed results: the identifications were significantly better than chance, but only accurately matched the blog and author a fifth of the time; identification improved with the number of posts written by the author in the corpus. Even if an author is not identified, some of their characteristics may still be deduced stylometrically, or stylometry may narrow the anonymity set of potential authors sufficiently for other information to complete the identification. Detecting author characteristics (e.g., gender or age) is often simpler than identifying an author from a large, possibly open, set of candidates. Modern machine learning techniques offer powerful tools for identification; further development of corpora and computational stylometric techniques are likely to raise further privacy issues. Gröndahl & Asokan (2020a) say that the general validity of the hypothesis underlying stylometry—that authors have invariant, content-independent 'style fingerprints'—is uncertain, but "the deanonymisation attack is a real privacy concern". Those interested in practicing adversarial stylometry and stylistic deception include whistleblowers avoiding retribution; journalists and activists; perpetrators of frauds and hoaxes; authors of fake reviews; literary forgers; criminals disguising their identity from investigators; and, generally, anyone with a desire for anonymity or pseudonymity. Authors, or agents acting on behalf of authors, may also attempt to remove stylistic clues to author characteristics (e.g., race or gender) so that knowledge of those characteristics cannot be used for discrimination (e.g., through algorithmic bias). Another possible use for adversarial stylometry is in disguising automatically generated text as human-authored. == Methods == With imitation, the author attempts to mislead stylometry by matching their style to another author's. An incomplete imitation, where some of the true author's unique characteristics appear alongside the imitated author's, can be a detectable signal for the use of adversarial stylometry. Imitation can be performed automatically with style transfer systems, though this typically requires a large corpus in the target style for the system to learn from. Another approach is translation, which employs machine translation of a source text to eliminate characteristic style, often through multiple translators in sequence to produce a round-trip translation. Such chained translation can lead to texts being significantly altered, even to the point of incomprehensibility; improved translation tools reduce this risk. More simply-structured texts can be easier to machine translate without losing the original meaning. Machine translation blurs into direct stylistic imitation or obfuscation achieved through automated style transfer, which can be viewed as a "translation" with the same language as input and output. With low-quality translation tools, an author can be required to manually correct major translation errors while avoiding the hazard of re-introducing stylistic characteristics. Wang, Juola & Riddell (2022) found that gross errors introduced by Google Translate were rare, but more common with several intermediate translations—however, occasional simple or short sentences and misspellings in the source text appeared verbatim in the output, potentially providing an identifying signal. Chain translation can leave characteristic traces of its application in a document, which may allow reconstruction of the intermediate languages used and the number of translation steps performed. Obfuscation involves deliberately changing the style of a text to reduce its similarity to other texts by some metric; this may be performed at the time of writing by conscious modification, or as part of a revision process with feedback from the metric being targeted as an input to decide when the text has been sufficiently obfuscated. In contrast to translation, complex texts can offer more opportunities for effective obfuscation without altering meaning, and likewise genres with more permissible variation allow more obfuscation. However, longer texts are harder to thoroughly obfuscate. Obfuscation can blend into imitation if the author develops a novel target style, distinct from their original style. With respect to masking author characteristics, obfuscation may aim to achieve a union (adding signals for imitated characteristics) or an intersection (removing signals and normalising) of other authors' styles. Avoiding the author's own idiosyncrasies and producing a "normalised" text is a critical obfuscatory step: an author may have a unique tendency to misspell certain words, use particular variants, or to format a document in a characteristic way. Stylometric signals vary in how simply they can be adversarially masked; an author may easily change their vocabulary by conscious choice, but altering the pattern of grammar or the letter frequency in their text may be harder to achieve, though Juola & Vescovi (2011) report that imitation typically succeeds at masking more characteristics than obfuscation. Automated obfuscation may require large amounts of training data written by the author. Concerning automated implementations of adversarial stylometry, two possible implementations are rule-based systems for paraphrasing; and encoder–decoder architectures, where the text passes through an intermediate format that is (intended to be) style-neutral. Another division in automated methods is whether there is feedback from an identification system or not. With such feedback, finding paraphrases for author masking has been characterised as a heuristic search problem, exploring textual variants until the result is stylistically sufficiently far (in the case of obfuscation) or near (in the case of imitation), which then constitutes an adversarial example for that identification system. == Evaluation == How
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Traceability

Traceability is the capability to trace something. In some cases, it is interpreted as the ability to verify the history, location, or application of an item by means of documented recorded identification. Other common definitions include the capability (and implementation) of keeping track of a given set or type of information to a given degree, or the ability to chronologically interrelate uniquely identifiable entities in a way that is verifiable. Traceability is applicable to measurement, supply chain, software development, healthcare and security. == Measurement == The term measurement traceability or metrological traceability is used to refer to an unbroken chain of comparisons relating an instrument's measurements to a known standard. Calibration to a traceable standard can be used to determine an instrument's bias, precision, and accuracy. It may also be used to show a chain of custody—from current interpretation of evidence to the actual evidence in a legal context, or history of handling of any information. In many countries, national standards for weights and measures are maintained by a National Metrological Institute (NMI) which provides the highest level of standards for the calibration / measurement traceability infrastructure in that country. Examples of government agencies include the National Physical Laboratory, UK (NPL) the National Institute of Standards and Technology (NIST) in the USA, the Physikalisch-Technische Bundesanstalt (PTB) in Germany, the Instituto Nazionale di Ricerca Metrologica (INRiM) in Italy, and the National Research Council of Canada (NRC). As defined by NIST, "Traceability of measurement requires the establishment of an unbroken chain of comparisons to stated references each with a stated uncertainty." A clock providing traceable time is traceable to a time standard such as Coordinated Universal Time or International Atomic Time. The Global Positioning System is a source of traceable time. === Food processing === In food processing (meat processing, fresh produce processing), the term traceability refers to the recording through means of barcodes or RFID tags and other tracking media, all movement of product and steps within the production process. One of the key reasons this is such a critical point is in instances where an issue of contamination arises, and a recall is required. Where traceability has been closely adhered to, it is possible to identify, by precise date/time and exact location which goods must be recalled, and which are safe, potentially saving millions of dollars in the recall process. Traceability within the food processing industry is also utilised to identify key high production and quality areas of a business, versus those of low return, and where points in the production process may be improved. In food processing software, traceability systems imply the use of a unique piece of data (e.g., order date/time or a serialized sequence number, generally through the use of a barcode / RFID) which can be traced through the entire production flow, linking all sections of the business, including suppliers and future sales through the supply chain. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability software to find the particular transaction and/or product within the supply chain. In food systems, ISO 22005, as part of the ISO 22000 family of standards, has been developed to define the principles for food traceability and specifies the basic requirements for the design and implementation of a feed and food traceability system. It can be applied by an organization operating at any step in the feed and food chain. The European Union's General Food Law came into force in 2002, making traceability compulsory for food and feed operators and requiring those businesses to implement traceability systems. The EU introduced its Trade Control and Expert System, or TRACES, in April 2004. The system provides a central database to track movement of animals within the EU and from third countries. Australia has its National Livestock Identification System to keep track of livestock from birth to slaughterhouse. India has started taking initiatives for setting up traceability systems at Government and Corporate levels. Grapenet, an initiative by Agriculture and Processed Food Products Export Development Authority (APEDA), Ministry of Commerce, Government of India is an example in this direction. GrapeNet is an internet based traceability software system for monitoring fresh grapes exported from India to the European Union. GrapeNet is a first of its kind initiative in India that has put in place an end-to-end system for monitoring pesticide residue, achieve product standardization and facilitate tracing back from pallets to the farm of the Indian grower, through the various stages of sampling, testing, certification and packing. Grapenet won the National Award (Gold), in the winners announced for the best e-Governance initiatives undertaken in India in 2007. The Directorate Generate Foreign Trade (DGFT), Government of India, through its notification dated 04.02.2009 relating to Amendment in Foreign Trade Policy (RE2008)has mandated that Export to the European Union is permitted subject to registration with APEDA, thereby making Grapenet mandatory for all exports of fresh grapes from India to Europe. Uruguay has also designed a system called "Traceability & Electronic Information System of the Beef Industry". Traceability in food supply can also refer to practices employed by individual companies, including Ritual and Amway's Nutrilite. In the case of Nutrilite's supplements, ingredients are documented and tested throughout farming, processing, and manufacturing to ensure traceability at each stage of production. == Systems and software development == In systems and software development, the term traceability (or requirements traceability) refers to the ability to link product requirements back to stakeholders' rationales and forward to corresponding design artifacts, code, and test cases. Traceability supports numerous software engineering activities such as change impact analysis, compliance verification or traceback of code, regression test selection, and requirements validation. It is usually accomplished in the form of a matrix created for the verification and validation of the project. Unfortunately, the practice of constructing and maintaining a requirements trace matrix (RTM) can be very arduous and over time the traces tend to erode into an inaccurate state unless date/time stamped. Alternate automated approaches for generating traces using information retrieval methods have been developed. The IEEE defines traceability as "(1)The degree to which a relationship can be established between two or more products of the development process, especially products having a predecessor, successor or master-subordinate relationship to one another. For example, the degree to which the requirements and design of a given software component match. See also: consistency. " and "(2) The degree to which each element in a software development product establishes its reason for existing; for example, the degree to which each element in a bubble chart references the requirement that it satisfies." In transaction processing software, traceability implies use of a unique piece of data (e.g., order date/time or a serialized sequence number) which can be traced through the entire software flow of all relevant application programs. Messages and files at any point in the system can then be audited for correctness and completeness, using the traceability key to find the particular transaction. This is also sometimes referred to as the transaction footprint. == Health care == Patient safety during healthcare service plays an important role in preventing delayed recovery or even mortality, by increasing and improving the quality of life of citizens, and is considered an indicator of the quality status of health services Maintaining patient safety is a complex task and involves factors inherent to the environment and human actions. New technologies facilitate the traceability tools of patients and medications. This is particularly relevant for drugs that are considered high risk and cost. Recent research in the healthcare industry emphasizes the significant impact of Blockchain Technology (BCT) on improving the performance of healthcare supply chain management. It highlights BCT's role in enhancing transparency, data immutability, and efficient management, leading to better cooperation among stakeholders and effective risk mitigation in healthcare services. The World Health Organization has recognized the importance of traceability for medical products of human origin (MPHO) and urged member states "to encourage the implementation of globally consistent coding systems to facilitate national and international traceability". == Security and cri
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Ampere Computing

Ampere Computing LLC is an American fabless semiconductor company that designs ARM-based central processing units (CPUs) with high core counts for use in cloud computing and data center environments. Founded in 2017 by former Intel president Renée James, the company is headquartered in Santa Clara, California, and operates as an independent subsidiary of SoftBank Group since November 2025. == History == Ampere Computing was founded in fall 2017 by Renée James, ex-President of Intel, with funding from The Carlyle Group. James acquired a team from MACOM Technology Solutions (formerly AppliedMicro) in addition to several industry hires to start the company. Ampere Computing is an ARM architecture licensee and develops its own server microprocessors. Ampere fabricates its products at TSMC. In April 2019, Ampere announced its second major investment round, including investment from Arm Holdings and Oracle Corporation. In June 2019, Nvidia announced a partnership with Ampere to bring support for Compute Unified Device Architecture (CUDA). In November 2019, Nvidia announced a reference design platform for graphics processing unit (GPU)-accelerated ARM-based servers including Ampere. In the first half of 2020, Ampere announced Ampere Altra, an 80-core processor, and Ampere Altra Max, a 128-core processor, without the use of simultaneous multithreading. In March 2020, the company announced a partnership with Oracle. In September 2020, Oracle said it would launch bare-metal and virtual machine instances in early 2021 based on Ampere Altra. In November 2020, Ampere was named one of the top 10 hottest semiconductor startups by CRN. In May 2021, the company announced a partnership with Microsoft. In April 2022, Ampere said that it had filed a confidential prospectus with the U.S. Securities and Exchange Commission, signaling its intent to go public. In June 2022, HPE announced their Gen11 ProLiant system would use Ampere Altra and Ampere Altra Max Cloud Native Processors. In July 2022, Google announced T2A instances using Ampere Altra in the Google cloud and in August 2022 Microsoft announced their instances of Ampere running in Azure. On March 19, 2025, investment holding company SoftBank Group announced it will acquire Ampere Computing for $6.5 billion. The deal finalized in November 2025, with Ampere remaining as an independent subsidiary with its headquarters in Santa Clara, California. == Products == Ampere develops ARM-based computer processors and CPU cores under their Altra brands. These are used in databases, media encoding, web services, network acceleration, mobile gaming, AI inference processing, and other applications and programs that need to scale. On February 5, 2018, Ampere announced the eMAG 8180 featuring 32x Skylark cores fabricated on TSMC's 16FF+ process. It supports a turbo of up to 3.3 GHz with a TDP of 125 W, 8ch 64-bit DDR4, up to 1 TB DDR4 per socket, and 42x PCIe 3.0 Lanes. The Skylark cores were based on AppliedMicro's X-Gene 3. Packet offers servers with the eMAG 8180 and 128 GB DRAM, 480 GB SSD, and 2x 10 Gbit/s networking. On September 19, 2018, Ampere announced the availability of a version featuring 16x Skylark cores. === 2020 === On March 3, 2020, Ampere announced the Ampere Altra featuring 80 cores fabricated on TSMC's N7 process for hyperscale computing. It was the first server-grade processor to include 80 cores and the Q80-30 conserves power by running at 161 W in use. The cores are semi-custom Arm Neoverse N1 cores with Ampere modifications. It supports a frequency of up to 3.3 GHz with TDP of 250 W, 8ch 72-bit DDR4, up to 4 TB DDR4-3200 per socket, 128x PCIe 4.0 Lanes, 1 MB L2 per core and 32 MB SLC. Ampere also announced their roadmap with Ampere Altra Max (2021) in development and AmpereOne (2022) defined. === 2021 === The 128-core Altra Max was released in 2021 and targeted hyperscale cloud providers. It uses the same server socket and platforms as Ampere Altra, and both products have one thread per core. The Altra Max CPUs provide 128 Arm v8.2+ cores per chip and run up to 3.0 GHz. They also support eight channels of DDR4-3200 memory and 128 lanes of PCIe Gen4. Also in 2021, Oracle launched its Oracle Cloud Infrastructure (OCI) using Ampere Altra processors. === 2022 === In February 2022, Ampere and Rigetti Computing announced a strategic partnership to create hybrid quantum-classical computers. The companies will combine Ampere's Altra Max CPUs with Rigetti's Quantum Processing Units (QPU) in cloud-based High-Performance Computing (HPC) environments. In April, Microsoft previewed its Azure Virtual Machines running on the Ampere Altra. The VMs run scale-out workloads, web servers, application servers, open source databases, cloud native .NET applications, Java applications, gaming servers, media servers, and other processes. In May, Ampere announced the sampling of AmpereOne CPUs, 5 nanometer chips based on its in-house Ampere-developed core. AmpereOne will add support for DDR5 main memory and PCIe Gen5 peripherals. On June 28, 2022, HPE became first tier-one server provider to offer compute with optimized cloud-native silicon for service providers and enterprises embracing cloud-native development with new line of HPE ProLiant RL Gen11 servers, using Ampere® Altra® and Ampere® Altra® Max processors, delivering high performance and power efficiency. === 2023 === During April 2023, Ampere released the Altra developer's kit, an IoT Prototype Kit based on Ampere Altra, aimed at cloud developers, available in 32-core, 64-core, and 80-core formats. === 2024 === In May 2024, Ampere updated its AmpereOne roadmap to 256 cores and announced a joint effort with Qualcomm on CPUs and accelerators. == Customers == Ampere's customers include Microsoft Azure, Tencent Cloud, Oracle, ByteDance, Hewlett Packard Enterprise (HPE), Cloudflare, Equinix, Kingsoft Cloud, Meituan, Scaleway, UCloud, Foxconn Industrial Internet, Gigabyte, Inspur, Cruise, Hetzner, Project Ronin, Wiwynn and Google Cloud Platform Cruise uses an Ampere Altra variant for its autonomous driving unit. The CPU was selected because of its throughput and low power consumption. In 2021, Oracle, Microsoft, Tencent, and ByteDance committed to using Ampere's customized chips, first announced in May. In April 2022, Microsoft previewed Ampere Altra processors in its new Azure D-and E- series virtual machines. The Dpsv5 series is built for Linux enterprise application types, and the Epsv5 series is for memory-intensive Linux workloads. They provide up to 64 vCPUs, include VM sizes with 2GiB, 4GiB, and 8GiB per vCPU memory configurations, up to 40 Gbit/s networking, and high-performance local SSD storage. In 2022, Microsoft's Ampere Altra-based Azure servers became the first cloud solution provider server to be Arm SystemReady SR certified. The Azure VMs, powered by Altra processors, were also the first to be SystemReady Virtual Environment standard certified. SystemReady defines a set of firmware and hardware standards as a baseline for system development for software developers, original equipment vendors, and chipmakers.
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Enonic XP

Enonic XP is a free and open-source content platform. Developed by the Norwegian software company Enonic, the platform can be used to build websites, progressive web applications, or web-based APIs. Enonic XP uses an application framework for coding server logic with JavaScript, and has no need for SQL as it ships with an integrated content repository. The CMS is fully decoupled, meaning developers can create traditional websites and landing pages, or use XP in headless mode, that is without the presentation layer, for loading editorial content onto any device or client. Enonic is used by major organizations in Norway, including the national postal service Norway Post, the insurance company Gjensidige, the Norwegian Labour and Welfare Administration, and all the top football clubs in the national football league for men, Eliteserien. == Overview == Enonic XP ships with the content management system (CMS) Content Studio. This includes a visual drag and drop editor, a landing page editor, support for multi-site and multi-language, media and structured content, advanced image editing, responsive user interface, permissions and roles management, revision and version control, and bulk publishing. Integrations and applications can be directly installed via the "Applications" section in XP, where the platform finds apps approved in the official Enonic Market. There are no third-party databases in Enonic XP. Instead, the developers have built a distributed storage repository, avoiding the need to index content. The system brings together capabilities from Filesystem, NoSQL, document stores, and search in the storage technology, which automatically indexes everything put into the storage. Enonic XP supports deployment of server side JavaScript. The open-source framework runs on top of a JVM (Java virtual machine), and allows developers to run the same code in the browser and on the server, thus enabling them to employ JavaScript. While running on the Java virtual machine, Enonic XP can be deployed on most infrastructures. The dependency on a third-party application server to deploy code has been removed, as the platform is an application server by default. A developer can for instance insert his own modules and code straight into the system while it is running. JavaScript unifies all the technical elements, and Enonic XP features a MVC framework where everything on the back-end can be coded with server-side JavaScript. The Enonic platform can use any template engine. === Progressive web apps === Another feature of Enonic XP is the possibility for developers to create progressive web apps (PWA). A PWA is a web application that is a regular web page or website, but can appear to the user like a mobile application. === Headless CMS and integrations === Enonic XP is headless, which means it separates content and presentation. The platform supports GraphQL, provides several default APIs, and allows for building custom APIs through the Guillotine starter kit. Consequently, Enonic supports modern front-end frameworks, and offers integrations with e.g. Next.js and React. == History == Enonic AS was founded in 2000 by Morten Øien Eriksen and Thomas Sigdestad. The software company specialized in building services and solutions, including a content management system known as "Vertical Site", then "Enonic CMS". Being aware that they had application, database, and website teams working on separate silos toward the same goal, Enonic sought to combine the different elements into a single software. The resulting application platform Enonic XP, first released in 2015, includes a CMS as an optional surface layer. In March 2020, Enonic XP was ranked by SoftwareReviews, a division of Info-Tech Research Group, a Canadian IT research and analyst firm, as the "Leader" in Web Experience Management. The ranking is based on user reviews, and is featured in SoftwareReviews‘ Digital Experience Data Quadrant Report, a comprehensive evaluation and ranking of leading Web Experience Management vendors. Enonic was also ranked first in 2021 and 2022. === Release history === Enonic XP assumed the mantle from the previous content management system Enonic CMS, and thus began with "version 5.0.0." The following list only contains major releases. == Development and support == Enonic offers a user and developer community consisting of a forum, support system with tickets, documentation, codex, learning and training center with certifications, and various community groups. Writing about the support system, Mike Johnston of CMS Critic notes that "enterprise customers obviously get access to a higher level of personalized support, where the Enonic support team can respond as fast as two hours." The support system is divided in three levels: silver, gold and platinum—from next day business support to 24/7 support. As Enonic XP is open-source, known vulnerabilities, bugs and issues are listed on GitHub.
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Pixlr

Pixlr is a group of SaaS creative tools including Pixlr.com, Designs.ai and Vectr.com. Pixlr.com is a cloud-based set of image editing tools and utilities, including AI image generation and enhancements. The Pixlr suite targets users who require subjectively simple, or more advanced, photo editing as well as graphic design. It features a freemium business model with subscription plans—Plus, Premium and Teams. The platform can be used on desktop and also smartphones and tablets. Pixlr is compatible with various image formats such as JPEG, PNG, WEBP, GIF, PSD (Photoshop Document) and PXZ (native Pixlr document format). Designs.ai lets users create content using AI, with a goal of being within two minutes, across different media types including videos, text, banners and audio. Vectr.com was acquired in 2017 before being spun out into Pixlr Group in 2023. == History == Pixlr was founded in 2008 and built on Macromedia Flash. On 19 July 2011, Autodesk announced that they had acquired the Pixlr suite. In 2013, Time listed Pixlr as one of the top 50 websites of the year. In 2017, Pixlr was acquired from Autodesk. It was subsequently rebuilt and relaunched in HTML5 in 2019. In September 2023, Pixlr was awarded as the Top 13 GenAi Web Product by the world's top venture firm Andreessen Horowitz. In November 2023, Pixlr, Designs.ai and Vectr were combined as a new business group named Pixlr Group focusing on generative AI and creative software solutions. In May 2024, Pixlr was featured as one of the top 18 progressive web applications highlighted on Google I/O. == Versions == Pixlr.com rebranded itself as a full creative suite in 2019 by introducing Pixlr X, Pixlr E and Pixlr M. The platform introduced more features in December 2021 with a new logo and added tools which included: Brushes, the 'Heal tool', Animation, and Batch upload. The brush feature enables the creation of hand-drawn effects. The Heal tool allows users to remove unwanted objects from their images whereas the Animation feature can be used to include movements into their edits. Users can also utilize Batch upload to edit up to 50 images simultaneously. In November 2022, Pixlr 2023 was launched, adding more tools such as "AI smart resize", colorization, text wrapping and other additional effects. In November 2023, Pixlr 2024 was launched with Pixlr Designer and new AI-powered updates which includes AI image generation, AI infill, AI inpainting and more.
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IOS SDK

The iOS SDK (iOS Software Development Kit), formerly the iPhone SDK, is a software development kit (SDK) developed by Apple Inc. The kit allows for the development of mobile apps on Apple's iOS 17 and iPadOS operating systems. The iOS SDK is a free download for users of Macintosh (or Mac) personal computers. It is not available for Microsoft Windows PCs. The SDK contains sets giving developers access to various functions and services of iOS devices, such as hardware and software attributes. It also contains an iPhone simulator to mimic the look and feel of the device on the computer while developing. New versions of the SDK accompany new versions of iOS. In order to test applications, get technical support, and distribute apps through App Store, developers are required to subscribe to the Apple Developer Program. Combined with Xcode, the iOS SDK helps developers write iOS apps using officially supported programming languages, including Swift and Objective-C. Other companies have also created tools that allow for the development of native iOS apps using their respective programming languages. == History == While originally developing iPhone prior to its unveiling in 2007, Apple's then-CEO Steve Jobs did not intend to let third-party developers build native apps for the iOS operating system, instead directing them to make web applications for the Safari web browser. However, backlash from developers prompted the company to reconsider, with Jobs announcing on October 17, 2007, that Apple would have a software development kit (SDK) available for developers by February 2008. The SDK was released on March 6, 2008. == Features == The iOS SDK is a free download for Mac users. It is not available for Microsoft Windows. To test the application, get technical support, and distribute applications through App Store, developers are required to subscribe to the Apple Developer Program. The SDK contents are separated into the following sets: UIKit Multi-touch events and controls Accelerometer support View hierarchy Localization (i18n) Camera support Media OpenAL audio mixing and recording Video playback Image file formats Quartz Core Animation OpenGL ES Core Services Networking Embedded SQLite database Core Location Threads CoreMotion Mac OS X Kernel TCP/IP Sockets Power management File system Security The SDK also contains an iPhone simulator, a program used to simulate the look and feel of iPhone on the developer's computer. New SDK versions accompany new iOS versions. == Programming languages == The iOS SDK, combined with Xcode, helps developers write iOS applications using officially supported programming languages, including Swift and Objective-C. An .ipa (iOS App Store Package) file is an iOS application archive file which stores an iOS app. === Java === In 2008, Sun Microsystems announced plans to release a Java Virtual Machine (JVM) for iOS, based on the Java Platform, Micro Edition version of Java. This would enable Java applications to run on iPhone and iPod Touch. Soon after the announcement, developers familiar with the SDK's terms of agreement believed that by not allowing third-party applications to run in the background (answer a phone call and still run the application, for example), and not allowing an application to download code from another source, nor allowing an application to interact with a third-party application, Sun's development efforts could be hindered without Apple's cooperation. Sun also worked with a third-party company called Innaworks in attempts to get Java on iPhone. Despite the apparent lack of interest from Apple, a firmware leak of the 2007 iPhone release revealed an ARM chip with a processor with Jazelle support for embedded Java execution. === .NET === Novell announced in September 2009 that they had successfully developed MonoTouch, a software framework that let developers write native iPhone applications in the C# and .NET programming languages, while still maintaining compatibility with Apple's requirements. === Flash === iOS does not support Adobe Flash, and although Adobe has two versions of its software: Flash and Flash Lite, Apple views neither as suitable for the iPhone, claiming that full Flash is "too slow to be useful", and Flash Lite to be "not capable of being used with the Web". In October 2009, Adobe announced that an upcoming update to its Creative Suite would feature a component to let developers build native iPhone apps using the company's Flash development tools. The software was officially released as part of the company's Creative Suite 5 collection of professional applications. === 2010 policy on development tools === In April 2010, Apple made controversial changes to its iPhone Developer Agreement, requiring developers to use only "approved" programming languages in order to publish apps on App Store, and banning applications that used third-party development tools; the ban affected Adobe's Packager tool, which converted Flash apps into iOS apps. After developer backlash and news of a potential anti-trust investigation, Apple again revised its agreement in September, allowing the use of third-party development tools. === Mac Catalyst === Originally called "Project Marzipan", Mac Catalyst helps developers bring iPadOS app experiences to macOS, and make it easier to take apps developed for iPadOS devices to Macs by avoiding the need to write the underlying software code twice.
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Personal cloud

A personal cloud is a collection of digital content and services that are accessible from any device through the Internet. It is not a tangible entity, but a place that gives users the ability to store, synchronize, stream and share content on a relative core, moving from one platform, screen and location to another. Created on connected services and applications, it reflects and sets consumer expectations for how next-generation computing services will work. The four primary types of personal cloud in use today are: Online cloud, NAS device cloud, server device cloud, and home-made clouds. == Online cloud == The online cloud is sometimes referred to as the public cloud. It is the cloud computing model where online resources like software and data storage are made available over the Internet. Typically, an individual or organization has little control over the ecosystem in which the online cloud is hosted, and the core infrastructure is shared between many individuals and organizations. The data and applications provided by the service provider are logically segregated so that only those authorized are allowed access. == NAS device cloud == A network-attached storage (NAS) device is a computer connected to a network that provides only file-based data storage services to other devices on the network. Although it may technically be possible to run other software on a NAS device, it is not designed to be a general purpose server. Cloud NAS is remote storage that is accessed over the Internet as if it were local. A cloud NAS is often used for backups and archiving. One of the benefits of NAS Cloud is that data in the cloud can be accessed at any time from anywhere. The main drawback, however, is that the speed of the transfer rate is only as fast as the network connection the data is accessed over and can therefore be fairly slow. == Server device cloud == In many ways cloud servers work in the same way as physical servers but the functions they perform can be very different. Typically, the cloud server is an on-premises device that is connected to the Internet and gives users the functions available on the online cloud but with the added benefit and security of the files being in their control on their premises. The server cloud has been historically enterprise-based deployed by businesses needing an in-house cloud. However, there are also in-house options available for individual users. == Home-made clouds == For the more technologically proficient user a common solution for using a personal cloud is to create a home-made cloud system by connecting an external USB hard drive to a Wi-Fi router. This enables both wired and wireless computers to access the USB hard drive and use it for storage or for retrieving files a user needs to share on the network thereby acting like a cloud. Setting up a personal cloud requires a user to have particular skills in technology and network setup. One of the risks associated with improper setup is security, and leaving the files accessible to anyone with technical knowledge. Not every router supports this type of access and modification.
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ShowScoop

ShowScoop is a website and mobile app platform on which users can rate and review artists, concerts, and music festivals that they have seen/attended. The reviews and ratings are designed to be informative of how well such performances are live. This helps concert-goers decide which live music events they want to attend. == History == ShowScoop was founded in August 2012 by Micah Smurthwaite and is based out of San Diego, CA. In February 2013, ShowScoop launched its mobile app at the SF Music Tech Summit. The application is currently available on the iPhone, with plans to expand into the Android market in the future. == Services == ShowScoop uses crowdsourcing to provide accurate ratings of live concert experiences. In addition to viewing ratings, users are encouraged to rate and review concerts they have attended. The ShowScoop database includes nearly one million artists and over 2.5 million live music events. ShowScoop users can rate artists on four aspects of the performance: stage presence, crowd interaction, sound quality, and visual effects. The rating system uses an ascending scale from one to five in each of the aspects, with five being the highest score. In addition to the quantitative ratings, ShowScoop users are also free to write qualitative reviews in a provided comment section. This allows users to explain their ratings and add further insight or opinion. ShowScoop incorporates several facets of social media into its services. Users can create a user profile to share limited personal information and store their ratings and reviews. Users are also given the option of sharing their evaluations with their social networks on Facebook and Twitter. Users can "like" reviews, follow artists, and follow other ShowScoop users. The mobile app allows users to take photos, apply filters, and share the final image in conjunction with reviews and through Instagram. == Road Crew == ShowScoop's "Road Crew" is a group made up of top contributors within the ShowScoop community. The Road Crew assists in curating artist pages, assuring information quality and accuracy. In return, members of the Road Crew are given incentives, including free tickets to concerts and personal invitations to exclusive shows. Applicants to the Road Crew are judged on the number and quality of their reviews, the photos and videos they have posted, and their general engagement with the ShowScoop community in following and liking users and reviews.
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Group of Governmental Experts on Lethal Autonomous Weapons Systems

The Group of Governmental Experts on Lethal Autonomous Weapons Systems, commonly known as the GGE on LAWS, refers to a group of governmental experts established under the framework of the Convention on Certain Conventional Weapons (CCW), a United Nations arms control framework. The group examines legal, ethical, societal and moral questions that arise from the increased use of autonomous robots to carry weapons and to be programmed to engage in combat in various situations that might arise, including battles between countries, or in patrolling border areas or sensitive areas, or other similar roles. As of 18 March 2025, the Convention on Certain Conventional Weapons had 128 High Contracting Parties. In the Geneva Conventions, the term "High Contracting Parties" refers to the states that have joined the conventions and are therefore bound to uphold them. Among the countries that have joined are states with tense relations or ongoing armed conflict with one another, including Russia and Ukraine, Israel and the State of Palestine, and Pakistan and Afghanistan. == Background == In 2013, the Meeting of State Parties to the Convention on Certain Conventional Weapons agreed on a mandate on lethal autonomous weapon systems and tasked its chairperson with convening an informal Meeting of Experts to discuss issues related to emerging technologies in the area of LAWS. Those informal Meetings of Experts were then held in 2014, 2015 and 2016, and their reports fed into subsequent meetings of the High Contracting Parties. At the Fifth CCW Review Conference in 2016, the High Contracting Parties decided to establish an open-ended Group of Governmental Experts on emerging technologies in the area of LAWS, building on the earlier expert meetings. Since then, the group has been reconvened annually. In 2023, the Meeting of the High Contracting Parties to the CCW decided that the GGE on LAWS would continue its work in 2024 and 2025. The group was tasked with developing, by consensus, elements of a possible instrument, without predetermining its form, as well as other measures addressing lethal autonomous weapon systems, drawing on existing CCW protocols, earlier recommendations, state proposals, and legal, military, and technological expertise. == 2024 == In 2024, the GGE met twice, and the group was chaired by Robert in den Bosch, the Netherlands' disarmament ambassador. The 2024 Meeting of the High Contracting Parties decided that the group would meet for 10 days in 2025, in two five-day sessions, and reaffirmed its mandate to continue work by consensus on possible elements of an instrument and other measures addressing lethal autonomous weapon systems. == 2025 == At its first 2025 session, held in Geneva from 3 to 7 March 2025, the Group of Governmental Experts on Lethal Autonomous Weapon Systems discussed revisions to the chair's rolling text. The text was structured into five sections, or "boxes", though delegates held differing views on whether headings were useful or appropriate. Broadly, the discussions covered the characterization of lethal autonomous weapon systems, the application of international humanitarian law, possible prohibitions and regulations, legal review, and questions of accountability and responsibility. At its second session, held from 1 to 5 September 2025, delegations continued work on the chair's rolling text, which set out elements of a possible instrument and was organized into five thematic "boxes". == 2026 == === Developments before the 2026 session === A few weeks before the meeting, autonomous weapons drew renewed attention when the United States pressured Anthropic to revise the terms of use for its AI model Claude. Anthropic prohibited the model's use for mass domestic surveillance and for fully autonomous weapons operating without human oversight, while reports also emerged that OpenAI had reached an agreement with the U.S. Department of War for the use of its AI models, reportedly stipulating that they would not independently direct autonomous weapons where human control was required. The U.S. military nevertheless continued to use Claude during its war on Iran, and there was increasing alarm about the use of AI-assisted semi-autonomous weapons in conflicts including those in Ukraine, Sudan, Gaza, and Iran. Before the start of the sessions, Robert in den Bosch, as chair, warned that progress was urgent because technological developments were moving quickly. At the same time, although states agreed that international humanitarian law applied to LAWS, specific internationally binding standards governing such systems remained largely absent. A key divide before the session was that Russia and the United States opposed new legally binding instruments, while other states argued that new rules were necessary. According to Robert in den Bosch, the talks could lead to new rules, amendments to an existing convention, or a new treaty. === First session === From 2 to 6 March 2026, the group held its penultimate session under the group's three-year mandate. Delegations discussed the chair's rolling draft text, circulated in December 2025, on elements of a possible instrument or other measures concerning lethal autonomous weapon systems. In revised text circulated by the chair on 5 March 2026, a lethal autonomous weapon system was characterized as "a functionally integrated combination of one or more weapons and technological components, that can identify, select, and engage a target, without intervention by a human operator in the execution of these tasks". The text was divided into five boxes to structure discussion. During the session, delegates conducted a first reading of the draft text, and the chair later circulated revised language for several sections. Informal consultations were also held. According to campaign groups and participating observers, support grew during the week for moving to negotiations on the basis of the rolling text, with more than 70 states said to support that step by the end of the session, though some participants warned that attempts to bridge differences risked blurring the group's core purpose. The International Committee of the Red Cross argued that the text should not only restate existing international humanitarian law, but also clarify how those rules apply to autonomous weapons and set out additional measures tailored to the specific challenges such systems raise. Stop Killer Robots likewise emphasized the need to preserve meaningful human judgment and control over increasingly autonomous systems. During the discussions, the U.S. delegation opposed the term "human control" and reportedly proposed the alternative phrase "good faith human judgment and care". Other delegations rejected that wording as too weak, while many states continued to insist that meaningful human control over weapon systems remained essential.
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Mobile simulator

A mobile simulator is a software application for a personal computer which creates a virtual machine version of a mobile device, such as a mobile phone, iPhone, other smartphone, or calculator, on the computer. This may sometimes also be termed an emulator. The mobile simulator allows the user to use features and run applications on the virtual mobile on their computer as though it was the actual mobile device. A mobile simulator lets you test a website and determine how well it performs on various types of mobile devices. A good simulator tests mobile content quickly on multiple browsers and emulates several device profiles simultaneously. This allows analysis of mobile content in real-time, locate errors in code, view rendering in an environment that simulates the mobile browser, and optimize the site for performance. Mobile simulators may be developed using programming languages such as Java, .NET and JavaScript.
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Gerrit (software)

Gerrit ( GERR-it) is a free, web-based team code collaboration tool. Software developers in a team can review each other's modifications on their source code using a Web browser and approve or reject those changes. It integrates closely with Git, a distributed version control system. Gerrit is a fork of Rietveld, a code review tool for Subversion. Both are named after Dutch designer Gerrit Rietveld. == History == Originally written in Python like Rietveld, it is now written in Java (Java EE Servlet) with SQL since version 2 and a custom-made Git-based database (NoteDb) since version 3. In versions 2.0–2.16 Gerrit used Google Web Toolkit for its browser-based front-end. After being developed and used in parallel with GWT for versions 2.14–2.16, a new Polymer web UI replaced the GWT UI in version 3.0.
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