Excalidraw is an open-source, web-based virtual whiteboard and diagramming application. It is used to create diagrams, wireframes, and sketches within a web browser without requiring account registration. The software features a characteristic hand-drawn visual style and supports real-time multi-user collaboration using client-side end-to-end encryption. Excalidraw is released under the MIT License and is maintained by Excalidraw s.r.o., a company based in Brno, Czech Republic. == History == Excalidraw was created on 1 January 2020 by Christopher Chedeau, a software engineer at Meta Platforms. Chedeau, who previously co-created React Native and Prettier, initially developed the application as a personal project before registering the domain on 3 January 2020. Within its first months, the project attracted open-source contributors who assisted in expanding its features and rewriting the codebase into TypeScript and React. By early 2021, day-to-day operations moved to Czech developers David Luzar and Milos Vetesnik. In May 2021, the team incorporated Excalidraw s.r.o. in Brno and launched a commercial cloud-based version named Excalidraw+ to fund the open-source project's development. By May 2026, the main open-source repository on GitHub had accumulated over 123,000 stars. == Features and architecture == The application provides an infinite canvas for geometric shapes, lines, arrows, text, and freehand drawing. Its visual presentation relies on Rough.js, a JavaScript graphics library that alters standard vector paths to mimic irregular, hand-drawn lines. Excalidraw operates as a Progressive web application (PWA), allowing local installation and offline usage, saving data natively to local browser storage. Files use a native, JSON-based extension format (.excalidraw), and canvases can be exported to PNG or SVG formats. Real-time collaboration sessions are executed using Socket.IO via a relay server. Data transmission uses the browser's native Web Cryptography API to achieve end-to-end encryption. A symmetric AES key is generated on the client side and appended to the sharing URL as a fragment identifier (following the # character). Because web browsers do not transmit URL fragments to HTTP servers, the data remains unreadable to the distribution server. == Ecosystem == Excalidraw is distributed as an npm package, allowing third-party developers to embed the whiteboard component directly into external React web applications. Community-developed extensions integrate the application's file format into text editors and note-taking systems, including Visual Studio Code and Obsidian. The platform also has native integrations in commercial platforms such as Notion and HackerRank. == Reception == Google's developer relations team published a technical case study on Excalidraw as a reference implementation for Progressive Web Apps. The analysis highlighted the software's adoption of advanced web platform capabilities, specifically its utilization of the File System Access API and native Clipboard API to replicate desktop software behavior within a web browser environment.
Semantic analysis (machine learning)
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans. Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning. For the restricted domain of spatial analysis, a computer-based language understanding system was demonstrated. Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA). Latent Dirichlet allocation, which involves attributing document terms to topics. n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms. == Stochastic semantic analysis ==
Artificial intuition
Artificial intuition is a theoretical capacity of an artificial software to function similarly to human consciousness, specifically in the capacity of human consciousness known as intuition. == Comparison of human and the theoretically artificial == Intuition is the function of the mind, the experience of which, is described as knowledge based on "a hunch", resulting (as the word itself does) from "contemplation" or "insight". Psychologist Jean Piaget showed that intuitive functioning within the normally developing human child at the Intuitive Thought Substage of the preoperational stage occurred at from four to seven years of age. In Carl Jung's concept of synchronicity, the concept of "intuitive intelligence" is described as something like a capacity that transcends ordinary-level functioning to a point where information is understood with a greater depth than is available in more simple rationally-thinking entities. Artificial intuition is theoretically (or otherwise) a sophisticated function of an artifice that is able to interpret data with depth and locate hidden factors functioning in Gestalt psychology, and that intuition in the artificial mind would, in the context described here, be a bottom-up process upon a macroscopic scale identifying something like the archetypal (see τύπος). To create artificial intuition supposes the possibility of the re-creation of a higher functioning of the human mind, with capabilities such as what might be found in semantic memory and learning. The transferral of the functioning of a biological system to synthetic functioning is based upon modeling of functioning from knowledge of cognition and the brain, for instance as applications of models of artificial neural networks from the research done within the discipline of computational neuroscience. == Application software contributing to its development == The notion of a process of a data-interpretative synthesis has already been found in a computational-linguistic software application that has been created for use in an internal security context. The software integrates computed data based specifically on objectives incorporating a paradigm described as "religious intuitive" (hermeneutic), functional to a degree that represents advances upon the performance of generic lexical data mining.
Run-time algorithm specialization
In computer science, run-time algorithm specialization is a methodology for creating efficient algorithms for costly computation tasks of certain kinds. The methodology originates in the field of automated theorem proving and, more specifically, in the Vampire theorem prover project. The idea is inspired by the use of partial evaluation in optimising program translation. Many core operations in theorem provers exhibit the following pattern. Suppose that we need to execute some algorithm a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} in a situation where a value of A {\displaystyle A} is fixed for potentially many different values of B {\displaystyle B} . In order to do this efficiently, we can try to find a specialization of a l g {\displaystyle {\mathit {alg}}} for every fixed A {\displaystyle A} , i.e., such an algorithm a l g A {\displaystyle {\mathit {alg}}_{A}} , that executing a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} is equivalent to executing a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} . The specialized algorithm may be more efficient than the generic one, since it can exploit some particular properties of the fixed value A {\displaystyle A} . Typically, a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} can avoid some operations that a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} would have to perform, if they are known to be redundant for this particular parameter A {\displaystyle A} . In particular, we can often identify some tests that are true or false for A {\displaystyle A} , unroll loops and recursion, etc. == Difference from partial evaluation == The key difference between run-time specialization and partial evaluation is that the values of A {\displaystyle A} on which a l g {\displaystyle {\mathit {alg}}} is specialised are not known statically, so the specialization takes place at run-time. There is also an important technical difference. Partial evaluation is applied to algorithms explicitly represented as codes in some programming language. At run-time, we do not need any concrete representation of a l g {\displaystyle {\mathit {alg}}} . We only have to imagine a l g {\displaystyle {\mathit {alg}}} when we program the specialization procedure. All we need is a concrete representation of the specialized version a l g A {\displaystyle {\mathit {alg}}_{A}} . This also means that we cannot use any universal methods for specializing algorithms, which is usually the case with partial evaluation. Instead, we have to program a specialization procedure for every particular algorithm a l g {\displaystyle {\mathit {alg}}} . An important advantage of doing so is that we can use some powerful ad hoc tricks exploiting peculiarities of a l g {\displaystyle {\mathit {alg}}} and the representation of A {\displaystyle A} and B {\displaystyle B} , which are beyond the reach of any universal specialization methods. == Specialization with compilation == The specialized algorithm has to be represented in a form that can be interpreted. In many situations, usually when a l g A ( B ) {\displaystyle {\mathit {alg}}_{A}(B)} is to be computed on many values of B {\displaystyle B} in a row, a l g A {\displaystyle {\mathit {alg}}_{A}} can be written as machine code instructions for a special abstract machine, and it is typically said that A {\displaystyle A} is compiled. The code itself can then be additionally optimized by answer-preserving transformations that rely only on the semantics of instructions of the abstract machine. The instructions of the abstract machine can usually be represented as records. One field of such a record, an instruction identifier (or instruction tag), would identify the instruction type, e.g. an integer field may be used, with particular integer values corresponding to particular instructions. Other fields may be used for storing additional parameters of the instruction, e.g. a pointer field may point to another instruction representing a label, if the semantics of the instruction require a jump. All instructions of the code can be stored in a traversable data structure such as an array, linked list, or tree. Interpretation (or execution) proceeds by fetching instructions in some order, identifying their type, and executing the actions associated with said type. In many programming languages, such as C and C++, a simple switch statement may be used to associate actions with different instruction identifiers. Modern compilers usually compile a switch statement with constant (e.g. integer) labels from a narrow range by storing the address of the statement corresponding to a value i {\displaystyle i} in the i {\displaystyle i} -th cell of a special array, as a means of efficient optimisation. This can be exploited by taking values for instruction identifiers from a small interval of values. == Data-and-algorithm specialization == There are situations when many instances of A {\displaystyle A} are intended for long-term storage and the calls of a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} occur with different B {\displaystyle B} in an unpredictable order. For example, we may have to check a l g ( A 1 , B 1 ) {\displaystyle {\mathit {alg}}(A_{1},B_{1})} first, then a l g ( A 2 , B 2 ) {\displaystyle {\mathit {alg}}(A_{2},B_{2})} , then a l g ( A 1 , B 3 ) {\displaystyle {\mathit {alg}}(A_{1},B_{3})} , and so on. In such circumstances, full-scale specialization with compilation may not be suitable due to excessive memory usage. However, we can sometimes find a compact specialized representation A ′ {\displaystyle A^{\prime }} for every A {\displaystyle A} , that can be stored with, or instead of, A {\displaystyle A} . We also define a variant a l g ′ {\displaystyle {\mathit {alg}}^{\prime }} that works on this representation and any call to a l g ( A , B ) {\displaystyle {\mathit {alg}}(A,B)} is replaced by a l g ′ ( A ′ , B ) {\displaystyle {\mathit {alg}}^{\prime }(A^{\prime },B)} , intended to do the same job faster.
MarkLogic Server
MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data. == History == MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors. MarkLogic's customers are mostly Global 2000 companies. == Technology == MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. == Features == Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows. Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document). == Applications == Banking Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines == Licensing == MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. == Releases == 2001 – Cerisent XQE 1: ACID transactions, Full-text search, XML Storage, XQuery, Role-based security 2004 – Cerisent XQE 2: Scale-out architecture, Enhanced search (stemming, thesaurus, wildcard), Backup and restore 2005 – MarkLogic Server 3: Continuing search improvements, Content Processing Framework (including PDF, Word, Excel, PPT), Failover 2008 – MarkLogic Server 4: Geospatial search, entity extraction, advanced XQuery, performance, scalability enhancements, auditing 2011 – MarkLogic Server 5: Flexible replication / DDIL, real-time indexing, advanced search, improved analytics, concurrency enhancements 2012 – MarkLogic Server 6: REST and Java APIs, App Builder, enhanced UI, improved search 2013 – MarkLogic Server 7: Semantic graph, bitemporal data, tiered storage, improved search, better management 2015 – MarkLogic Server 8: A Native JSON storage, Server-side JavaScript, Bitemporal, Node.js client API, Incremental backup, Flexible replication[16] 2017 – MarkLogic Server 9: Data integration across Relational and Non-Relational data, Advanced Encryption, Element Level Security, Redaction 2019 – MarkLogic Server 10: Enhanced Data Hub, improved SQL, security, analytics performance, cloud support 2022 – MarkLogic Server 11: MarkLogic Ops Director (Monitoring and Administration Improvements), expanded PKI 2025 – MarkLogic Server 12: Generative AI and Native Vector Search, Graph Algorithm Support, Virtual TDEs (relational views on the fly)
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.
Artificial intelligence in architecture
Artificial intelligence in architecture is the use of artificial intelligence in automation, design, and planning in the architectural process or in assisting human skills in the field of architecture. AI has been used by some architects for design, and has been proposed as a way to automate planning and routine tasks in the field. == Implications == === Benefits === Artificial intelligence, according to ArchDaily, is said to potentially significantly augment the architectural profession through its ability to improve the design and planning process as well as increasing productivity. Through its ability to handle a large amount of data, AI is said to potentially allow architects a range of design choices with criteria considerations such as budget, requirements adjusted to space, and sustainability goals calculated as part of the design process. ArchDaily said this may allow the design of optimized alternatives that can then undergo human review. AI tools are also said to potentially allow architects to assimilate urban and environmental data to inform their designs, streamlining initial stages of project planning and increasing efficiency and productivity. The advances in generative design through the input of specific prompts allow architects to produce visual designs, including photorealistic images, and thus render and explore various material choices and spatial configurations. ArchDaily noted this could speed the creative process as well as allow for experimentation and sophistication in the design. Additionally, AI's capacity for pattern recognition and coding could aid architects in organizing design resources and developing custom applications, thus enhancing the efficiency and collaboration between both architects and AI. AI is thought to also be able to contribute to the sustainability of buildings by analyzing various factors and following recommended energy-efficient modifications, thus pushing the industry towards greener practices. The use of AI in building maintenance, project management, and the creation of immersive virtual reality experiences are also thought of as potentially augmenting the architectural design process and workflow. Examples include the use of text-to-image systems such as Midjourney to create detailed architectural images, and the use of AI optimization systems from companies such as Finch3D and Autodesk to automatically generate floor plans from simple programmatic inputs. In contrast to digital-only creative practices, the high materiality of architectural outputs requires transitions from ephemeral digital files to permanent physical structures that are subject to strict safety regulations, material constraints, sensory intuition, and site-specific cultural contexts, making full automation difficult. Early adopters such as architect Stephen Coorlas have actively challenged the boundaries of architectural practice through AI. His early experimental initiative, Speculations on AI and Architecture, confronts the discipline's traditional workflows by training text-to-image AI tools such as Midjourney, Luma AI, and PromeAI to generate more nuanced architectural illustrations including construction documents, architectural details, and assembly sequences for various structures. Coorlas inputs precise terminology and architectural language to provoke the AI into producing axonometric drawings that resemble conventional documentation, then experiments with animating the outputs using AI generated depth maps and other AI image-to-3D wireframe tools. Stephen's inventive process invites architects and designers to reconsider authorship, automation, and the future of visual communication in the built environment. Rather than treating AI as a peripheral tool, Stephen has advocated for AI to be a speculative collaborator capable of engaging with discipline-specific challenges. His work contributes to the growing discourse on generative design, parametric optimization, and the philosophical implications of machine-assisted creativity raising urgent questions about how such technologies will reshape architectural agency, precision, and pedagogy. Another prominent advocate is Architect Andrew Kudless, who in an interview to Dezeen recounted that he uses AI to innovate in architectural design by incorporating materials and scenes not usually present in initial plans, which he believes can significantly alter client presentations. He told Dezeen he believes one should show clients renderings from the onset, with AI assisting in this work, arguing that changes in design should be a positive aspect of the client-designer relationship by actively involving clients in the process. Additionally, Kudless highlighted the AI's potential to facilitate labor in architectural firms, particularly in automating rendering tasks, thus reducing the workload on junior staff while maintaining control over the creative output. === Emergent aesthetics === In an interview for the AItopia series to Dezeen, designer Tim Fu discussed the transformative potential of AI in architecture, and proposed a future where AI could herald a "neoclassical futurist" style, blending the grandeur of classical aesthetics with futuristic design. Through his collaborative project, The AI Stone Carver, Fu showcased how AI can innovate traditional practices by generating design concepts that are then realized through human craftsmanship, such as stone carving by mason Till Apfel. This approach, he believed, celebrated the fusion of diverse architectural styles and also emphasized the unique capabilities of AI in enhancing creative design processes. Fu told Dezeen he envisions the integration of AI in design as a means to revive the ornamentation and detailed aesthetics characteristic of classical architecture, moving away from minimalism, which he said dominates contemporary architecture. He argued that AI's involvement in the ideation phase of design allows for a reversal in the roles of machine and human, enabling architects and designers to focus on creating more intricate and ornamental structures. Fu's optimistic outlook extended to the broader impact of AI on the architectural field, seeing it as an indispensable tool that will shift rather than replace human roles, enriching the field with innovative designs that pay homage to the beauty and qualities of classical architecture not present in contemporary architecture while embracing new technologies. This perspective resonates with designers like Manas Bhatia, whose explorations similarly embrace generative AI as a co-creator and a medium to express ideas, blend architectural traditions, and speculate spatial futures. === Concerns === As AI continues to expand its presence across various industries, its impact on the architectural profession has become a topic of growing discussion. These discussions focus on how AI processes may influence traditional architectural practices, potentially altering job roles, and shaping the nature of creativity. While AI-driven processes may increase efficiency in some aspects of the profession, they also raise questions about the potential loss of unique design perspectives. These thoughts have been countered by many prominent creative figures in the realm of AI architecture, such as Stephen Coorlas, Tim Fu, Hassan Ragab, and Manas Bhatia who have showcased the amplification of creativity in design and potential benefits in terms of restoring creative power to the designer. A key concern is that AI-powered tools could diminish the need for human involvement in specific tasks traditionally performed by architects. This has led to speculation that the profession may increasingly shift toward roles focused on oversight, coordination, and strategic decision-making rather than hands-on design work. In some design scenarios, algorithmically generated solutions can be adjusted to prioritize efficiency and cost-effectiveness, which some argue may overshadow the creative and contextual nuances that define individual architectural styles. As with any discipline though, it has been determined that AI can be configured to provide beneficial results based on inputs and end goals the architect or designer assigns it. There are also concerns about the potential for AI to exacerbate inequalities within the architectural profession. For instance, larger firms with greater resources to invest in advanced AI technologies may gain a competitive edge over smaller firms and independent architects. This dynamic could contribute to industry consolidation, potentially limiting the diversity of architectural practice and stifling innovation. Ethical considerations in regard to cultural sensitivity have also been raised due to the datasets used to train AI. Without proper vetting of data or implementing failsafe overrides, AI generated outcomes can trend toward overly documented and prioritized content.