Presentation used to give an introduction to Regular Expression in JavaScript at MercadoLibre Inc. Spanish: https://www.youtube.com/watch?v=skG03rdOhpo
This document contains summaries of several lectures about Python lists. It discusses how lists are sequences that can be indexed and traversed, are mutable unlike strings, and how list operations like slicing, methods, filtering and mapping can be used. It also covers deleting list elements, converting strings to lists, aliasing, passing lists as arguments and checking object references.
BoldRadius' Senior Software Developer Alejandro Lujan explains how to use higher order functions in Scala and illustrates them with some examples.
See the accompanying video at www.boldradius.com/blog
The document discusses arrays in C programming. It defines an array as a fixed-size collection of elements of the same data type. Arrays allow grouping of like-type data, such as lists of numbers, names, temperatures. Arrays consist of contiguous memory locations. The document describes declaring one-dimensional arrays using syntax like type arrayName[arraySize], and accessing elements using indexes like arrayName[0]. Arrays can be initialized during compilation by specifying values between curly brackets, or initialized at runtime.
The SAS ARRAY statement allows you to group variables together under a single name for processing within a DATA step. The ARRAY statement defines the array name, dimensions, variable names, and optionally initial values. Arrays make it convenient to process multiple variables together using DO loops and array references.
This document discusses optimizing images for use on the web. It covers choosing the appropriate file type based on the image content, reducing file size through compression and trimming unnecessary pixels, and using tools like Photoshop's "Save for Web" feature to balance image quality and download speed. The key considerations are file type (GIF, JPEG, PNG), size, compression level, and ensuring accessibility.
Regular expressions are patterns used to match character combinations in strings. They are used for search engines, validation, scraping, parsing, and more. Regular expressions use special characters like [], {}, (), | for matching sets, quantities, groups, and alternations. They also have flags like i for case-insensitive matching and g for finding all matches. Common regex tokens include \d, \s, \w for digit, space, word character classes and ., ^, $ for any character, start, end anchors.
This document provides an overview of using regular expressions (regex) in Java. It explains that regex allow you to describe and search for patterns in strings. The java.util.regex package includes the Pattern, Matcher, and PatternSyntaxException classes for working with regex in Java. The document then demonstrates various regex syntax like character classes, quantifiers, capturing groups, boundaries and more through code examples.
Regular Expressions: JavaScript And BeyondMax Shirshin
Regular Expressions is a powerful tool for text and data processing. What kind of support do browsers provide for that? What are those little misconceptions that prevent people from using RE effectively?
The talk gives an overview of the regular expression syntax and typical usage examples.
FUNDAMENTALS OF REGULAR EXPRESSION (RegEX).pdfBryan Alejos
Regular expressions (RegEx) are patterns used to match character combinations in strings. They are useful for tasks like validation, parsing, and finding/replacing text. Common RegEx syntax includes special characters for matching characters, word boundaries, repetition, groups, alternation, lookahead/behind. Examples show using RegEx in JavaScript to validate emails, URLs, passwords and extract matches from strings. Most programming languages support RegEx via built-in or library functions.
The document discusses scanning (lexical analysis) in compiler construction. It covers the scanning process, regular expressions, and finite automata. The scanning process identifies tokens from source code by categorizing characters as reserved words, special symbols, or other tokens. Regular expressions are used to represent patterns of character strings and define the language of tokens. Finite automata are mathematical models for describing scanning algorithms using states, transitions, and acceptance.
This document discusses strings, languages, and regular expressions. It defines key terms like alphabet, string, language, and operations on strings and languages. It then introduces regular expressions as a notation for specifying patterns of strings. Regular expressions are defined over an alphabet and can combine symbols, concatenation, union, and Kleene closure to describe languages. Examples are provided to illustrate regular expression notation and properties. Limitations of regular expressions in describing certain languages are also noted.
This document provides an overview of regular expressions and how they work with patterns and matchers in Java. It defines what a regular expression is, lists common uses of regex, and describes how to create patterns, use matchers to interpret patterns and perform matches, and handle exceptions. It also covers regex syntax like metacharacters, character classes, quantifiers, boundaries, and flags. Finally, it discusses capturing groups, backreferences, index methods, study methods, and replacement methods in the Matcher class.
The document discusses lexical analysis and lexical analyzer generators. It provides background on why lexical analysis is a separate phase in compiler design and how it simplifies parsing. It also describes how a lexical analyzer interacts with a parser and some key attributes of tokens like lexemes and patterns. Finally, it explains how regular expressions are used to specify patterns for tokens and how tools like Lex and Flex can be used to generate lexical analyzers from regular expression definitions.
This document discusses lexical analysis and regular expressions. It begins by outlining topics related to lexical analysis including tokens, lexemes, patterns, regular expressions, transition diagrams, and generating lexical analyzers. It then discusses topics like finite automata, regular expressions to NFA conversion using Thompson's construction, NFA to DFA conversion using subset construction, and DFA optimization. The role of the lexical analyzer and its interaction with the parser is also covered. Examples of token specification and regular expressions are provided.
compiler Design course material chapter 2gadisaAdamu
The document provides an overview of lexical analysis in compiler design. It discusses the role of the lexical analyzer in reading characters from a source program and grouping them into lexemes and tokens. Regular expressions are used to specify patterns for tokens. Non-deterministic finite automata (NFA) and deterministic finite automata (DFA) are used to recognize patterns and languages. Thompson's construction is used to translate regular expressions to NFAs, and subset construction is used to translate NFAs to equivalent DFAs. This process is used in lexical analyzer generators to automate the recognition of language tokens from regular expressions.
This document discusses regular expressions and their use in defining the syntax and tokens of a programming language. It provides examples of regular expressions for numeric literals and identifiers. Regular expressions allow the formal definition of the valid strings that make up the tokens in a language by using operators like concatenation, union, closure and more. They are used by scanners in compilers to extract tokens from source code.
Lexical analysis is the first phase of compilation where the character stream is converted to tokens. It must be fast. It separates concerns by having a scanner handle tokenization and a parser handle syntax trees. Regular expressions are used to specify patterns for tokens. A regular expression specification can be converted to a finite state automaton and then to a deterministic finite automaton to build a scanner that efficiently recognizes tokens.
Regular expressions are a powerful tool for searching, parsing, and modifying text patterns. They allow complex patterns to be matched with simple syntax. Some key uses of regular expressions include validating formats, extracting data from strings, and finding and replacing substrings. Regular expressions use special characters to match literal, quantified, grouped, and alternative character patterns across the start, end, or anywhere within a string.
Facebook Graph Search by Ole martin mørk for jdays2013 Gothenburg www.jdays.sehamidsamadi
This document summarizes a presentation about creating graph searches using Neo4j. It includes an agenda that covers introduction to search, Neo4j, parsing, and graph search. It provides examples of using Cypher to query a graph database and define patterns and relationships. It also discusses using parsing expression grammars and the Parboiled library to parse text into a graph structure that can enable graph search capabilities.
This document provides an overview and agenda for a presentation on creating graph searches using Neo4j. The presentation introduces graph databases and Neo4j, as well as parsing and graph search techniques. It discusses using the Cypher query language to define graph patterns and queries in Neo4j. Examples are provided to illustrate how to model data as nodes and relationships and write Cypher queries to search the graph. The document also briefly covers parsing with PEG grammars and the Parboiled library.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
The document discusses topics related to lexical analysis including:
1. The interaction between the scanner and parser where the scanner identifies tokens and strips whitespace and comments from the source code.
2. Input buffering techniques like buffer pairs and sentinels that allow the scanner to efficiently read character streams from the source code.
3. How regular expressions are used to specify patterns for token definitions in lexical analysis.
The compiler is software that converts a program written in a high-level language (Source Language) to a low-level language (Object/Target/Machine Language/0, 1’s).
A translator or language processor is a program that translates an input program written in a programming language into an equivalent program in another language. The compiler is a type of translator, which takes a program written in a high-level programming language as input and translates it into an equivalent program in low-level languages such as machine language or assembly language.
This document provides an introduction to regular expressions (RegEx). It explains that RegEx allows you to find, match, compare or replace text patterns. It then discusses the basic building blocks of RegEx, including characters, character classes, quantifiers, and assertions. It provides several examples of RegEx patterns to match names, words, ports numbers, and other patterns. It concludes with an overview of common RegEx match types like beginning/end of line, word boundaries, grouping, alternatives, and repetition.
Regular expressions allow matching and manipulation of textual data. They were first discovered by mathematician Stephen Kleene and their search algorithm was developed by Ken Thompson in 1968 for use in tools like ed, grep, and sed. Regular expressions follow certain grammars and use meta characters to match patterns in text. They are used for tasks like validation, parsing, and data conversion.
This summarizes a regex (regular expression) cheat sheet. It provides definitions and examples of common regex syntax elements including character classes, predefined character classes, boundary matches, logical operations, greedy/reluctant/possessive quantifiers, groups and backreferences, and pattern flags. It also summarizes the key Pattern and Matcher classes and methods in Java for working with regexes.
Regular Expressions: JavaScript And BeyondMax Shirshin
Regular Expressions is a powerful tool for text and data processing. What kind of support do browsers provide for that? What are those little misconceptions that prevent people from using RE effectively?
The talk gives an overview of the regular expression syntax and typical usage examples.
FUNDAMENTALS OF REGULAR EXPRESSION (RegEX).pdfBryan Alejos
Regular expressions (RegEx) are patterns used to match character combinations in strings. They are useful for tasks like validation, parsing, and finding/replacing text. Common RegEx syntax includes special characters for matching characters, word boundaries, repetition, groups, alternation, lookahead/behind. Examples show using RegEx in JavaScript to validate emails, URLs, passwords and extract matches from strings. Most programming languages support RegEx via built-in or library functions.
The document discusses scanning (lexical analysis) in compiler construction. It covers the scanning process, regular expressions, and finite automata. The scanning process identifies tokens from source code by categorizing characters as reserved words, special symbols, or other tokens. Regular expressions are used to represent patterns of character strings and define the language of tokens. Finite automata are mathematical models for describing scanning algorithms using states, transitions, and acceptance.
This document discusses strings, languages, and regular expressions. It defines key terms like alphabet, string, language, and operations on strings and languages. It then introduces regular expressions as a notation for specifying patterns of strings. Regular expressions are defined over an alphabet and can combine symbols, concatenation, union, and Kleene closure to describe languages. Examples are provided to illustrate regular expression notation and properties. Limitations of regular expressions in describing certain languages are also noted.
This document provides an overview of regular expressions and how they work with patterns and matchers in Java. It defines what a regular expression is, lists common uses of regex, and describes how to create patterns, use matchers to interpret patterns and perform matches, and handle exceptions. It also covers regex syntax like metacharacters, character classes, quantifiers, boundaries, and flags. Finally, it discusses capturing groups, backreferences, index methods, study methods, and replacement methods in the Matcher class.
The document discusses lexical analysis and lexical analyzer generators. It provides background on why lexical analysis is a separate phase in compiler design and how it simplifies parsing. It also describes how a lexical analyzer interacts with a parser and some key attributes of tokens like lexemes and patterns. Finally, it explains how regular expressions are used to specify patterns for tokens and how tools like Lex and Flex can be used to generate lexical analyzers from regular expression definitions.
This document discusses lexical analysis and regular expressions. It begins by outlining topics related to lexical analysis including tokens, lexemes, patterns, regular expressions, transition diagrams, and generating lexical analyzers. It then discusses topics like finite automata, regular expressions to NFA conversion using Thompson's construction, NFA to DFA conversion using subset construction, and DFA optimization. The role of the lexical analyzer and its interaction with the parser is also covered. Examples of token specification and regular expressions are provided.
compiler Design course material chapter 2gadisaAdamu
The document provides an overview of lexical analysis in compiler design. It discusses the role of the lexical analyzer in reading characters from a source program and grouping them into lexemes and tokens. Regular expressions are used to specify patterns for tokens. Non-deterministic finite automata (NFA) and deterministic finite automata (DFA) are used to recognize patterns and languages. Thompson's construction is used to translate regular expressions to NFAs, and subset construction is used to translate NFAs to equivalent DFAs. This process is used in lexical analyzer generators to automate the recognition of language tokens from regular expressions.
This document discusses regular expressions and their use in defining the syntax and tokens of a programming language. It provides examples of regular expressions for numeric literals and identifiers. Regular expressions allow the formal definition of the valid strings that make up the tokens in a language by using operators like concatenation, union, closure and more. They are used by scanners in compilers to extract tokens from source code.
Lexical analysis is the first phase of compilation where the character stream is converted to tokens. It must be fast. It separates concerns by having a scanner handle tokenization and a parser handle syntax trees. Regular expressions are used to specify patterns for tokens. A regular expression specification can be converted to a finite state automaton and then to a deterministic finite automaton to build a scanner that efficiently recognizes tokens.
Regular expressions are a powerful tool for searching, parsing, and modifying text patterns. They allow complex patterns to be matched with simple syntax. Some key uses of regular expressions include validating formats, extracting data from strings, and finding and replacing substrings. Regular expressions use special characters to match literal, quantified, grouped, and alternative character patterns across the start, end, or anywhere within a string.
Facebook Graph Search by Ole martin mørk for jdays2013 Gothenburg www.jdays.sehamidsamadi
This document summarizes a presentation about creating graph searches using Neo4j. It includes an agenda that covers introduction to search, Neo4j, parsing, and graph search. It provides examples of using Cypher to query a graph database and define patterns and relationships. It also discusses using parsing expression grammars and the Parboiled library to parse text into a graph structure that can enable graph search capabilities.
This document provides an overview and agenda for a presentation on creating graph searches using Neo4j. The presentation introduces graph databases and Neo4j, as well as parsing and graph search techniques. It discusses using the Cypher query language to define graph patterns and queries in Neo4j. Examples are provided to illustrate how to model data as nodes and relationships and write Cypher queries to search the graph. The document also briefly covers parsing with PEG grammars and the Parboiled library.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
The document discusses topics related to lexical analysis including:
1. The interaction between the scanner and parser where the scanner identifies tokens and strips whitespace and comments from the source code.
2. Input buffering techniques like buffer pairs and sentinels that allow the scanner to efficiently read character streams from the source code.
3. How regular expressions are used to specify patterns for token definitions in lexical analysis.
The compiler is software that converts a program written in a high-level language (Source Language) to a low-level language (Object/Target/Machine Language/0, 1’s).
A translator or language processor is a program that translates an input program written in a programming language into an equivalent program in another language. The compiler is a type of translator, which takes a program written in a high-level programming language as input and translates it into an equivalent program in low-level languages such as machine language or assembly language.
This document provides an introduction to regular expressions (RegEx). It explains that RegEx allows you to find, match, compare or replace text patterns. It then discusses the basic building blocks of RegEx, including characters, character classes, quantifiers, and assertions. It provides several examples of RegEx patterns to match names, words, ports numbers, and other patterns. It concludes with an overview of common RegEx match types like beginning/end of line, word boundaries, grouping, alternatives, and repetition.
Regular expressions allow matching and manipulation of textual data. They were first discovered by mathematician Stephen Kleene and their search algorithm was developed by Ken Thompson in 1968 for use in tools like ed, grep, and sed. Regular expressions follow certain grammars and use meta characters to match patterns in text. They are used for tasks like validation, parsing, and data conversion.
This summarizes a regex (regular expression) cheat sheet. It provides definitions and examples of common regex syntax elements including character classes, predefined character classes, boundary matches, logical operations, greedy/reluctant/possessive quantifiers, groups and backreferences, and pattern flags. It also summarizes the key Pattern and Matcher classes and methods in Java for working with regexes.
JavaScript Coding & Design Patterns discusses JavaScript style guides, namespaces, dependencies, dealing with browsers, separation of concerns, DOM scripting, events, functions, and design patterns like the Singleton, Module, and Prototypal Inheritance patterns. It covers topics like hoisting, scope, configuration objects, IIFEs, and using the new keyword and Object.create method for prototypal inheritance. The document provides examples and explanations of these JavaScript concepts and design patterns.
The document discusses the Document Object Model (DOM) and its core concepts. It covers how the DOM represents an HTML document as nodes organized in a tree structure, with different node types like elements, text, and attributes. It also describes common DOM manipulation methods for selecting, inserting, and removing nodes to modify the document. Key interfaces and inheritance are explained, along with how to work with different node types, events, and document fragments.
MercadoLibre is every day less requested with the IE7 browser. So in a few months we are going to leave giving support for that specific browser. Now, with a little previous analysis we are able to use the html5shiv tool. I used this slides to explain, share and reflect about how is and works the new HTML5 outline.
This was based on http://www.slideshare.net/hmammana/semantic-markup-creating-outline.
This document provides a summary of best practices for front-end development. It discusses semantic HTML, CSS organization and specificity, responsive images, JavaScript performance, and other optimization techniques. Key recommendations include writing semantic HTML first before styling, avoiding inline styles, properly structuring CSS with comments and organization, reducing requests by combining files and using sprites, and placing JavaScript before the closing body tag.
This presentation given for developers at Truelogic Software Solutions is about CSS and layouting. It is explained all the ways to re-position an element in the screen.
El documento habla sobre la tipografía en la web. Explica que aunque la web comenzó siendo solo texto, el 95% de su contenido aún es texto. Describe las familias tipográficas y cómo se ven afectadas en pantallas como las computadoras, celulares y tabletas. También cubre temas como la estructura semántica del HTML y la jerarquía visual del CSS para diseñar tipografías en la web. Finalmente, anticipa las posibilidades futuras del HTML5 y CSS3 para mejorar el diseño tipográfico en la web.
This document discusses semantic markup and outlines. It defines markup languages as systems for annotating documents in a way that is distinguishable from text. Semantic markup uses elements, attributes, and values that have specific predefined meanings. An outline is a list of potentially nested sections. Key HTML elements for outlines include headings (h1-h6) and sectioning elements (article, aside, nav, section). WAI-ARIA adds semantics for accessibility, while Microdata embeds semantics within existing content. Designers must consider accessibility and provide all needed context when labeling elements. Tools like HTML5 Outliner can help with outlines.
This presentation has been given to the MercadoLibre UX area to update the team the project state. It speaks about the most important challenges in the Q4.
The document provides information on HTML elements and best practices for frontend development. It discusses the basic structure of HTML with the <html>, <head>, and <body> elements. It also covers common text elements like <p>, <h1>-<h6>, and lists. The document explains how to semantically structure tables and provides examples of the <table>, <tr>, <td>, and <th> elements. It emphasizes writing accessible, valid HTML and separating structure, presentation, and behavior.
מכונות CNC קידוח אנכיות הן הבחירה הנכונה והטובה ביותר לקידוח ארונות וארגזים לייצור רהיטים. החלק נוסע לאורך ציר ה-x באמצעות ציר דיגיטלי מדויק, ותפוס ע"י צבת מכנית, כך שאין צורך לבצע setup (התאמות) לגדלים שונים של חלקים.
Bridging the divide: A conversation on tariffs today in the book industry - T...BookNet Canada
A collaboration-focused conversation on the recently imposed US and Canadian tariffs where speakers shared insights into the current legislative landscape, ongoing advocacy efforts, and recommended next steps. This event was presented in partnership with the Book Industry Study Group.
Link to accompanying resource: https://bnctechforum.ca/sessions/bridging-the-divide-a-conversation-on-tariffs-today-in-the-book-industry/
Presented by BookNet Canada and the Book Industry Study Group on May 29, 2025 with support from the Department of Canadian Heritage.
National Fuels Treatments Initiative: Building a Seamless Map of Hazardous Fu...Safe Software
The National Fuels Treatments Initiative (NFT) is transforming wildfire mitigation by creating a standardized map of nationwide fuels treatment locations across all land ownerships in the United States. While existing state and federal systems capture this data in diverse formats, NFT bridges these gaps, delivering the first truly integrated national view. This dataset will be used to measure the implementation of the National Cohesive Wildland Strategy and demonstrate the positive impact of collective investments in hazardous fuels reduction nationwide. In Phase 1, we developed an ETL pipeline template in FME Form, leveraging a schema-agnostic workflow with dynamic feature handling intended for fast roll-out and light maintenance. This was key as the initiative scaled from a few to over fifty contributors nationwide. By directly pulling from agency data stores, oftentimes ArcGIS Feature Services, NFT preserves existing structures, minimizing preparation needs. External mapping tables ensure consistent attribute and domain alignment, while robust change detection processes keep data current and actionable. Now in Phase 2, we’re migrating pipelines to FME Flow to take advantage of advanced scheduling, monitoring dashboards, and automated notifications to streamline operations. Join us to explore how this initiative exemplifies the power of technology, blending FME, ArcGIS Online, and AWS to solve a national business problem with a scalable, automated solution.
TrustArc Webinar - 2025 Global Privacy SurveyTrustArc
How does your privacy program compare to your peers? What challenges are privacy teams tackling and prioritizing in 2025?
In the sixth annual Global Privacy Benchmarks Survey, we asked global privacy professionals and business executives to share their perspectives on privacy inside and outside their organizations. The annual report provides a 360-degree view of various industries' priorities, attitudes, and trends. See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar features an expert panel discussion and data-driven insights to help you navigate the shifting privacy landscape. Whether you are a privacy officer, legal professional, compliance specialist, or security expert, this session will provide actionable takeaways to strengthen your privacy strategy.
This webinar will review:
- The emerging trends in data protection, compliance, and risk
- The top challenges for privacy leaders, practitioners, and organizations in 2025
- The impact of evolving regulations and the crossroads with new technology, like AI
Predictions for the future of privacy in 2025 and beyond
Floods in Valencia: Two FME-Powered Stories of Data ResilienceSafe Software
In October 2024, the Spanish region of Valencia faced severe flooding that underscored the critical need for accessible and actionable data. This presentation will explore two innovative use cases where FME facilitated data integration and availability during the crisis. The first case demonstrates how FME was used to process and convert satellite imagery and other geospatial data into formats tailored for rapid analysis by emergency teams. The second case delves into making human mobility data—collected from mobile phone signals—accessible as source-destination matrices, offering key insights into population movements during and after the flooding. These stories highlight how FME's powerful capabilities can bridge the gap between raw data and decision-making, fostering resilience and preparedness in the face of natural disasters. Attendees will gain practical insights into how FME can support crisis management and urban planning in a changing climate.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/solving-tomorrows-ai-problems-today-with-cadences-newest-processor-a-presentation-from-cadence/
Amol Borkar, Product Marketing Director at Cadence, presents the “Solving Tomorrow’s AI Problems Today with Cadence’s Newest Processor” tutorial at the May 2025 Embedded Vision Summit.
Artificial Intelligence is rapidly integrating into every aspect of technology. While the neural processing unit (NPU) often receives the majority of the spotlight as the ultimate AI problem solver, it is essential to recognize that not all AI workloads can be efficiently executed on an NPU and that neural network architectures are evolving rapidly. To create efficient chips and systems with market longevity, designers must plan for diverse AI workloads that include networks yet to be invented.
In this presentation, Borkar introduces a new processor from Cadence Tensilica. This new solution is designed to complement any NPU, creating the perfect synergy between the two processing engines and establishing a robust AI subsystem able to efficiently support workloads yet to be encountered. This combination allows developers to achieve efficiency and performance on the AI workloads of today and tomorrow, paving the way for future innovations in AI-powered devices.
PyData - Graph Theory for Multi-Agent Integrationbarqawicloud
Graph theory is a well-known concept for algorithms and can be used to orchestrate the building of multi-model pipelines. By translating tasks and dependencies into a Directed Acyclic Graph, we can orchestrate diverse AI models, including NLP, vision, and recommendation capabilities. This tutorial provides a step-by-step approach to designing graph-based AI model pipelines, focusing on clinical use cases from the field.
This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.
מכונת קנטים המתאימה לנגריות קטנות או גדולות (כמכונת גיבוי).
מדביקה קנטים מגליל או פסים, עד עובי קנט – 3 מ"מ ועובי חומר עד 40 מ"מ. בקר ממוחשב המתריע על תקלות, ומנועים מאסיביים תעשייתיים כמו במכונות הגדולות.
Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdfAlkin Tezuysal
As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL systems.
Mastering AI Workflows with FME - Peak of Data & AI 2025Safe Software
Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME’s powerful tools for training, optimization, and seamless integration
Kubernetes Security Act Now Before It’s Too LateMichael Furman
In today's cloud-native landscape, Kubernetes has become the de facto standard for orchestrating containerized applications, but its inherent complexity introduces unique security challenges. Are you one YAML away from disaster?
This presentation, "Kubernetes Security: Act Now Before It’s Too Late," is your essential guide to understanding and mitigating the critical security risks within your Kubernetes environments. This presentation dives deep into the OWASP Kubernetes Top Ten, providing actionable insights to harden your clusters.
We will cover:
The fundamental architecture of Kubernetes and why its security is paramount.
In-depth strategies for protecting your Kubernetes Control Plane, including kube-apiserver and etcd.
Crucial best practices for securing your workloads and nodes, covering topics like privileged containers, root filesystem security, and the essential role of Pod Security Admission.
Don't wait for a breach. Learn how to identify, prevent, and respond to Kubernetes security threats effectively.
It's time to act now before it's too late!
Creating an Accessible Future-How AI-powered Accessibility Testing is Shaping...Impelsys Inc.
Web accessibility is a fundamental principle that strives to make the internet inclusive for all. According to the World Health Organization, over a billion people worldwide live with some form of disability. These individuals face significant challenges when navigating the digital landscape, making the quest for accessible web content more critical than ever.
Enter Artificial Intelligence (AI), a technological marvel with the potential to reshape the way we approach web accessibility. AI offers innovative solutions that can automate processes, enhance user experiences, and ultimately revolutionize web accessibility. In this blog post, we’ll explore how AI is making waves in the world of web accessibility.
Domino IQ – What to Expect, First Steps and Use Casespanagenda
Webinar Recording: https://www.panagenda.com/webinars/domino-iq-what-to-expect-first-steps-and-use-cases/
HCL Domino iQ Server – From Ideas Portal to implemented Feature. Discover what it is, what it isn’t, and explore the opportunities and challenges it presents.
Key Takeaways
- What are Large Language Models (LLMs) and how do they relate to Domino iQ
- Essential prerequisites for deploying Domino iQ Server
- Step-by-step instructions on setting up your Domino iQ Server
- Share and discuss thoughts and ideas to maximize the potential of Domino iQ
Domino IQ – What to Expect, First Steps and Use Casespanagenda
Ad
JavaScript regular expression
1. GETTING STARTED
JavaScript Regular Expression
REFERENCES
Douglas Crockford, JavaScript: The Good Parts
Jan Goyvaerts and Steven Levithan, Regular Expression Cookbook
Stoyan Stefanov, Object-Oriented JavaScript
3. INTRODUCTION
• A regular expression is a specific kind of text pattern.
• JavaScript’s Regular Expression feature was borrowed from Perl.
• You can use it with many methods:
• match, replace, search, split in strings
• exec, test in regular expresion object