AI Code Meme

AI Code Meme — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Neurocomputing (journal)

    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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  • How to Choose an AI Virtual Assistant

    How to Choose an AI Virtual Assistant

    In search of the best AI virtual assistant? An AI virtual assistant is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI virtual assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • How to Choose an AI Text-to-video Tool

    How to Choose an AI Text-to-video Tool

    Comparing the best AI text-to-video tool? An AI text-to-video tool is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI text-to-video tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • AI Text-to-image Tools Reviews: What Actually Works in 2026

    AI Text-to-image Tools Reviews: What Actually Works in 2026

    In search of the best AI text-to-image tool? An AI text-to-image tool is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI text-to-image tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • PNGOUT

    PNGOUT

    PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.

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  • Finite-state transducer

    Finite-state transducer

    A finite-state transducer (FST) is a finite-state machine with two memory tapes, following the terminology for Turing machines: an input tape and an output tape. This contrasts with an ordinary finite-state automaton, which has a single tape. An FST is a type of finite-state automaton (FSA) that maps between two sets of symbols. An FST is more general than an FSA. An FSA defines a formal language by defining a set of accepted strings, while an FST defines a relation between sets of strings. An FST will read a set of strings on the input tape and generate a set of relations on the output tape. An FST can be thought of as a translator or relater between strings in a set. In morphological parsing, an example would be inputting a string of letters into the FST, the FST would then output a string of morphemes. == Overview == An automaton can be said to recognize a string if we view the content of its tape as input. In other words, the automaton computes a function that maps strings into the set {0,1}. Alternatively, we can say that an automaton generates strings, which means viewing its tape as an output tape. On this view, the automaton generates a formal language, which is a set of strings. The two views of automata are equivalent: the function that the automaton computes is precisely the indicator function of the set of strings it generates. The class of languages generated by finite automata is known as the class of regular languages. The two tapes of a transducer are typically viewed as an input tape and an output tape. On this view, a transducer is said to transduce (i.e., translate) the contents of its input tape to its output tape, by accepting a string on its input tape and generating another string on its output tape. It may do so nondeterministically and it may produce more than one output for each input string. A transducer may also produce no output for a given input string, in which case it is said to reject the input. In general, a transducer computes a relation between two formal languages. Each string-to-string finite-state transducer relates the input alphabet Σ to the output alphabet Γ. Relations R on Σ×Γ that can be implemented as finite-state transducers are called rational relations. Rational relations that are partial functions, i.e. that relate every input string from Σ to at most one Γ, are called rational functions. Finite-state transducers are often used for phonological and morphological analysis in natural language processing research and applications. Pioneers in this field include Ronald Kaplan, Lauri Karttunen, Martin Kay and Kimmo Koskenniemi. A common way of using transducers is in a so-called "cascade", where transducers for various operations are combined into a single transducer by repeated application of the composition operator (defined below). == Formal construction == Formally, a finite transducer T is a 6-tuple (Q, Σ, Γ, I, F, δ) such that: Q is a finite set, the set of states; Σ is a finite set, called the input alphabet; Γ is a finite set, called the output alphabet; I is a subset of Q, the set of initial states; F is a subset of Q, the set of final states; and δ ⊆ Q × ( Σ ∪ { ϵ } ) × ( Γ ∪ { ϵ } ) × Q {\displaystyle \delta \subseteq Q\times (\Sigma \cup \{\epsilon \})\times (\Gamma \cup \{\epsilon \})\times Q} (where ε is the empty string) is the transition relation. We can view (Q, δ) as a labeled directed graph, known as the transition graph of T: the set of vertices is Q, and ( q , a , b , r ) ∈ δ {\displaystyle (q,a,b,r)\in \delta } means that there is a labeled edge going from vertex q to vertex r. We also say that a is the input label and b the output label of that edge. NOTE: This definition of finite transducer is also called letter transducer (Roche and Schabes 1997); alternative definitions are possible, but can all be converted into transducers following this one. Define the extended transition relation δ ∗ {\displaystyle \delta ^{}} as the smallest set such that: δ ⊆ δ ∗ {\displaystyle \delta \subseteq \delta ^{}} ; ( q , ϵ , ϵ , q ) ∈ δ ∗ {\displaystyle (q,\epsilon ,\epsilon ,q)\in \delta ^{}} for all q ∈ Q {\displaystyle q\in Q} ; and whenever ( q , x , y , r ) ∈ δ ∗ {\displaystyle (q,x,y,r)\in \delta ^{}} and ( r , a , b , s ) ∈ δ {\displaystyle (r,a,b,s)\in \delta } then ( q , x a , y b , s ) ∈ δ ∗ {\displaystyle (q,xa,yb,s)\in \delta ^{}} . The extended transition relation is essentially the reflexive transitive closure of the transition graph that has been augmented to take edge labels into account. The elements of δ ∗ {\displaystyle \delta ^{}} are known as paths. The edge labels of a path are obtained by concatenating the edge labels of its constituent transitions in order. The behavior of the transducer T is the rational relation [T] defined as follows: x [ T ] y {\displaystyle x[T]y} if and only if there exists i ∈ I {\displaystyle i\in I} and f ∈ F {\displaystyle f\in F} such that ( i , x , y , f ) ∈ δ ∗ {\displaystyle (i,x,y,f)\in \delta ^{}} . This is to say that T transduces a string x ∈ Σ ∗ {\displaystyle x\in \Sigma ^{}} into a string y ∈ Γ ∗ {\displaystyle y\in \Gamma ^{}} if there exists a path from an initial state to a final state whose input label is x and whose output label is y. === Weighted automata === Finite State Transducers can be weighted, where each transition is labelled with a weight in addition to the input and output labels. A Weighted Finite State Transducer (WFST) over a set K of weights can be defined similarly to an unweighted one as an 8-tuple T=(Q, Σ, Γ, I, F, E, λ, ρ), where: Q, Σ, Γ, I, F are defined as above; E ⊆ Q × ( Σ ∪ { ϵ } ) × ( Γ ∪ { ϵ } ) × Q × K {\displaystyle E\subseteq Q\times (\Sigma \cup \{\epsilon \})\times (\Gamma \cup \{\epsilon \})\times Q\times K} (where ε is the empty string) is the finite set of transitions; λ : I → K {\displaystyle \lambda :I\rightarrow K} maps initial states to weights; ρ : F → K {\displaystyle \rho :F\rightarrow K} maps final states to weights. In order to make certain operations on WFSTs well-defined, it is convenient to require the set of weights to form a semiring. Two typical semirings used in practice are the log semiring and tropical semiring: nondeterministic automata may be regarded as having weights in the Boolean semiring. Two weighted FST can be composed. == Operations on finite-state transducers == The following operations defined on finite automata also apply to finite transducers: Union. Given transducers T and S, there exists a transducer T ∪ S {\displaystyle T\cup S} such that x [ T ∪ S ] y {\displaystyle x[T\cup S]y} if and only if x [ T ] y {\displaystyle x[T]y} or x [ S ] y {\displaystyle x[S]y} . Concatenation. Given transducers T and S, there exists a transducer T ⋅ S {\displaystyle T\cdot S} such that x [ T ⋅ S ] y {\displaystyle x[T\cdot S]y} if and only if there exist x 1 , x 2 , y 1 , y 2 {\displaystyle x_{1},x_{2},y_{1},y_{2}} with x = x 1 x 2 , y = y 1 y 2 , x 1 [ T ] y 1 {\displaystyle x=x_{1}x_{2},y=y_{1}y_{2},x_{1}[T]y_{1}} and x 2 [ S ] y 2 . {\displaystyle x_{2}[S]y_{2}.} Kleene closure. Given a transducer T, there might exist a transducer T ∗ {\displaystyle T^{}} with the following properties: and x [ T ∗ ] y {\displaystyle x[T^{}]y} does not hold unless mandated by (k1) or (k2). Composition. Given a transducer T on alphabets Σ and Γ and a transducer S on alphabets Γ and Δ, there exists a transducer T ∘ S {\displaystyle T\circ S} on Σ and Δ such that x [ T ∘ S ] z {\displaystyle x[T\circ S]z} if and only if there exists a string y ∈ Γ ∗ {\displaystyle y\in \Gamma ^{}} such that x [ T ] y {\displaystyle x[T]y} and y [ S ] z {\displaystyle y[S]z} . This operation extends to the weighted case. This definition uses the same notation used in mathematics for relation composition. However, the conventional reading for relation composition is the other way around: given two relations T and S, ( x , z ) ∈ T ∘ S {\displaystyle (x,z)\in T\circ S} when there exist some y such that ( x , y ) ∈ S {\displaystyle (x,y)\in S} and ( y , z ) ∈ T . {\displaystyle (y,z)\in T.} Projection to an automaton. There are two projection functions: π 1 {\displaystyle \pi _{1}} preserves the input tape, and π 2 {\displaystyle \pi _{2}} preserves the output tape. The first projection, π 1 {\displaystyle \pi _{1}} is defined as follows: Given a transducer T, there exists a finite automaton π 1 T {\displaystyle \pi _{1}T} such that π 1 T {\displaystyle \pi _{1}T} accepts x if and only if there exists a string y for which x [ T ] y . {\displaystyle x[T]y.} :The second projection, π 2 {\displaystyle \pi _{2}} is defined similarly. Determinization. Given a transducer T, we want to build an equivalent transducer that has a unique initial state and such that no two transitions leaving any state share the same input label. The powerset construction can be extended to transducers, or even weighted transducers, but sometimes fails to halt; indeed, some non-deterministic transducers do not admit equivalent

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  • AI Chatbots Reviews: What Actually Works in 2026

    AI Chatbots Reviews: What Actually Works in 2026

    Comparing the best AI chatbot? An AI chatbot is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI chatbot slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Thomas G. Dietterich

    Thomas G. Dietterich

    Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal. Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models. == Biography and education == Thomas Dietterich was born in South Weymouth, Massachusetts, in 1954. His family later moved to New Jersey and then again to Illinois, where Tom graduated from Naperville Central High School. Dietterich then entered Oberlin College and began his undergraduate studies. In 1977, Dietterich graduated from Oberlin with a degree in mathematics, focusing on probability and statistics. Dietterich spent the following two years at the University of Illinois, Urbana-Champaign. After those two years, he began his doctoral studies in the Department of Computer Science at Stanford University. Dietterich received his Ph.D. in 1984 and moved to Corvallis, Oregon, where he was hired as an assistant professor in computer science. in 2013, he was named "Distinguished Professor". In 2016, Dietterich retired from his position at Oregon State University. Throughout his career, Dietterich has worked to promote scientific publication and conference presentations. For many years, he was the editor of the MIT Press series on Adaptive Computation and Machine Learning. He also held the position of co-editor of the Morgan Claypool Synthesis Series on Artificial Intelligence and Machine Learning. He has organized several conferences and workshops including serving as Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90), Technical Program Chair of the Neural Information Processing Systems (NIPS-2000) and General Chair of NIPS-2001. He served as founding President of the International Machine Learning Society and he has been a member of the IMLS Board since its founding. He is currently also a member of the Steering Committee of the Asian Conference on Machine Learning. == Research interests == Professor Dietterich is interested in all aspects of machine learning. There are three major strands of his research. First, he is interested in the fundamental questions of artificial intelligence and how machine learning can provide the basis for building integrated intelligent systems. Second, he is interested in ways that people and computers can collaborate to solve challenging problems. And third, he is interested in applying machine learning to problems in the ecological sciences and ecosystem management as part of the emerging field of computational sustainability. Over his career, he has worked on a wide variety of problems ranging from drug design to user interfaces to computer security. His current focus is on ways that computer science methods can help advance ecological science and improve our management of the Earth's ecosystems. This passion has led to several projects including research in wildfire management, invasive vegetation and understanding the distribution and migration of birds. For example, Dietterich's research is helping scientists at the Cornell Lab of Ornithology answer questions like: How do birds decide to migrate north? How do they know when to land and stopover for a few days? How do they choose where to make a nest? Tens of thousands of volunteer birdwatchers (citizen scientists) all over the world contribute data to the study by submitting their bird sightings to the eBird website. The amount of data is overwhelming – in March 2012 they had over 3.1 million bird observations. Machine learning can uncover patterns in data to model the migration of species. But there are many other applications for the same techniques which will allow organizations to better manage our forests, oceans, and endangered species, as well as improve traffic flow, water systems, the electrical power grid, and more. I realized I wanted to have an impact on something that really mattered – and certainly the whole Earth's ecosystem, of which we are a part, is under threat in so many ways. And so if there's some way that I can use my technical skills to improve both the science base and the tools needed for policy and management decisions, then I would like to do that. I am passionate about that. == Dangers of AI: an academic perspective == Dietterich has argued that the most realistic risks about the dangers of artificial intelligence are basic mistakes, breakdowns and cyberattacks, and the fact that it simply may not always work, rather than machines that become super powerful or destroy the human race. Dietterich considers machines becoming self-aware and trying to exterminate humans to be more science fiction than scientific fact. But to the extent that computer systems are given increasingly dangerous tasks, and asked to learn from and interpret their experiences, he said they may simply make mistakes. Instead, much of the work done in the AI safety community does indeed focus around accidents and design flaws. == Positions held == 2014–2016: President, Association for the Advancement of Artificial Intelligence (AAAI). 2013–present: Distinguished Professor of computer science, Oregon State University. 2011–present: Chief Scientist, BigML, Corvallis, OR. 2005–present: Director of Intelligent Systems Research, School of Electrical Engineering and Computer Science, Oregon State University. 2006–2008: Chief Scientist, Smart Desktop, Inc., Seattle, WA. 2004–2005: Chief Scientist, MyStrands, Inc., Corvallis, OR. 1995-2013: Professor of computer science, Oregon State University. 1998–1999: Visiting Senior Scientist, Institute for the Investigation of Artificial Intelligence, Barcelona, Spain. (Sabbatical leave position) 1988–1995: Associate Professor of computer science, Oregon State University. 1991–1993: Senior Scientist, Arris Pharmaceutical Corporation, S. San Francisco, CA. 1985–1988: Assistant Professor of computer science, Oregon State University. 1979–1984: Research Assistant, Heuristic Programming Project, Department of Computer Science, Stanford University. 1979 (Summer): Member of Technical Staff, Bell Telephone Laboratories, Naperville, Illinois. Computer-to-computer file transfer and micro-code distribution to remote switching systems. 1977 (Summer): Assistant to the Director of Planning and Research, Oberlin College, Oberlin, Ohio. Developed institutional planning database. == Awards and honors == Thomas Dietterich was honored by Oregon State University in the spring of 2013 as a "Distinguished Professor" for his work as a pioneer in the field of machine learning and being one of the mostly highly cited scientists in his field. He has also earned exclusive "Fellow" status in the Association for the Advancement of Artificial Intelligence, the American Association for the Advancement of Science and the Association for Computing Machinery. Over his career, he obtained more than $30 million in research grants, helped build a world-class research group at Oregon State, and created three software companies. He also co-founded two of the field's leading journals and was elected first president of the International Machine Learning Society. His other awards and honors include: ACM Distinguished Lecturer, 2012-2013 Fellow, American Association for the Advancement of Science, 2007 Oregon State University, College of Engineering Collaboration Award, 2004 Winner, JAIR Award for Best Paper in Previous Five Years, 2003 Fellow, Association for Computing Machinery, elected 2003 Oregon State University, College of Engineering Research Award, 1998 Fellow, Association for the Advancement of Artificial Intelligence, elected 1994 NSF Presidential Young Investigator, 1987-92 Nominated for Carter Award for Graduate Teaching, 1987, 1988 IBM Graduate Fellow, 1982, 1983 Upsilon Pi Epsilon, 1996 Sigma Xi, 1979–present State Farm Companies Foundation Fellowship, 1978 Member, Board of Trustees, Oberlin College, 1977-1980 Graduation with Honors in Mathematics, Oberlin College, 1977 Phi Beta Kappa, 1977 National Merit Scholar, 1973 == Selected publications == Liping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou (2016). Transductive Optimization of Top k Precision. International Joint Conference on Artificial Intelligence (IJCAI-2016). pp. 1781–1787. New York, NY Md. Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Da

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  • Gonioreflectometer

    Gonioreflectometer

    A gonioreflectometer is a device for measuring a bidirectional reflectance distribution function (BRDF). The device consists of a light source illuminating the material to be measured and a sensor that captures light reflected from that material. The light source should be able to illuminate and the sensor should be able to capture data from a hemisphere around the target. The hemispherical rotation dimensions of the sensor and light source are the four dimensions of the BRDF. The 'gonio' part of the word refers to the device's ability to measure at different angles. Several similar devices have been built and used to capture data for similar functions. Most of these devices use a camera instead of the light intensity-measuring sensor to capture a two-dimensional sample of the target. Examples include: a spatial gonioreflectometer for capturing the SBRDF (McAllister, 2002). a camera gantry for capturing the light field (Levoy and Hanrahan, 1996). an unnamed device for capturing the bidirectional texture function (Dana et al., 1999).

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  • Xuedong Huang

    Xuedong Huang

    Xuedong David Huang (born October 20, 1962) is a Chinese-American computer scientist and technology executive who has made contributions to spoken language processing and artificial intelligence, including Azure AI Services. He is Zoom's chief technology officer after serving as Microsoft's Technical Fellow and Azure AI Chief Technology Officer for 30 years. Huang is a strong advocate of AI for Accessibility, and AI for Cultural Heritage. == Education == Huang received his PhD from the University of Edinburgh in 1989 (sponsored by the British ORS and Edinburgh University Scholarship), his MS from Tsinghua University in 1984, and BS from Hunan University in 1982. == Career == After receiving his PhD in 1989, Huang joined Carnegie Mellon University and worked with Raj Reddy and Kai-Fu Lee on speech recognition. At CMU, he directed the Sphinx-II speech system research which achieved the best performance in every category of DARPA's 1992 benchmarking. Microsoft Research recruited him to found and lead Microsoft's spoken language initiatives in 1993. His co-authored book Spoken Language Processing and his Historical speech recognition review succinctly summarize several generations of spoken language research. As Microsoft's Mr. Speech for three decades, Huang has been instrumental in creating Microsoft's Speech Application Programming Interface (SAPI), shipping Microsoft Speech Server, and modernizing spoken language and integrative AI services via Azure AI, which not only enables millions of 3rd party customers but also powers up Microsoft's Windows, Office, Teams, and Azure OpenAI Services. Huang helped Microsoft and Azure Cognitive Services achieve multiple industry's first human parity milestones on the following open research tasks: transcribing conversational speech, machine translation, conversational QnA, and computer vision image captioning. Huang has made significant contributions to the software and AI industry through his executive leadership and his scientific publications, owning more than 170 US patents and impacting billions through Azure AI enabled products and services. In 2016, Wired magazine named him one of 25 Geniuses. In 2021, Azure AI was named the winner of InfoWorld's Technology of the Year Award. Huang was awarded the Allen Newell research excellence medal in 1992, and IEEE Speech Processing Best Paper in 1993. He was recognized as an IEEE Fellow by Institute of Electrical and Electronics Engineers in 2000, named ACM Fellow by Association for Computing Machinery in 2017, and a member of Washington State Academy of Sciences. Huang received 2022 Asian American Corporate Leadership Award, and IEEE Amar Bose Industrial Leader Award. In 2023, he was elected a member of the US National Academy of Engineering (NAE), and a member of the American Academy of Arts and Sciences.

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  • The Best Free AI Clip Maker for Beginners

    The Best Free AI Clip Maker for Beginners

    Looking for the best AI clip maker? An AI clip maker is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI clip maker slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Seppo Linnainmaa

    Seppo Linnainmaa

    Seppo Ilmari Linnainmaa (born 28 September 1945) is a Finnish mathematician and computer scientist known for creating the modern version of backpropagation. == Biography == He was born in Pori. He received his MSc in 1970 and introduced a reverse mode of automatic differentiation in his MSc thesis. In 1974 he obtained the first doctorate ever awarded in computer science at the University of Helsinki. In 1976, he became Assistant Professor. From 1984 to 1985 he was Visiting Professor at the University of Maryland, USA. From 1986 to 1989 he was Chairman of the Finnish Artificial Intelligence Society. From 1989 to 2007, he was Research Professor at the VTT Technical Research Centre of Finland. He retired in 2007. == Backpropagation == Explicit, efficient error backpropagation in arbitrary, discrete, possibly sparsely connected, neural networks-like networks was first described in Linnainmaa's 1970 master's thesis, albeit without reference to NNs, when he introduced the reverse mode of automatic differentiation (AD), in order to efficiently compute the derivative of a differentiable composite function that can be represented as a graph, by recursively applying the chain rule to the building blocks of the function. Linnainmaa published it first, following Gerardi Ostrowski who had used it in the context of certain process models in chemical engineering some five years earlier, but didn't publish.

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  • Excalidraw

    Excalidraw

    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.

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  • PROMT

    PROMT

    ProMT is a lead Russian developer of language translation software for businesses and private users since 1991. The company provides on-premises software based on neural technologies. == History == On March 6, 1998, ProMT launched a free online translation services, which is now known as PROMT.One. In 1997, ProMT and the French company Softissimo developed a line of products for the European company Reverso. == Technology == Historically, ProMT systems used rule-based machine translation (RBMT) technology. In 2011 a hybrid approach which combined rule-based and statistical MT was implemented. In 2019, ProMT introduced its new neural technology and flagship solution - PROMT Neural Translation Server. Since then all MT systems developed by ProMT are based on neural machine translation. The software can run on Microsoft Windows, Linux, MacOS, iOS and Android and works in offline mode providing secure machine translation. As of 2025, it translates 62 languages from and to English, German, and Russian.

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  • Is an AI Virtual Assistant Worth It in 2026?

    Is an AI Virtual Assistant Worth It in 2026?

    Shopping for the best AI virtual assistant? An AI virtual assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI virtual assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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