Architecture DSLs are a useful tool to capture the cornerstones of platform or product line architectures. In addition, interesting analyses can be performed on the models, and much of the infrastructure and configuration code can be generated. On the flip side, these DSLs themselves must be architected consciously: while many architectural abstractions are specific to any given platform or product line, many other aspects are generic and hence can be reused for several architecture DSLs. In this talk I trace how my thinking on architecture modeling has changed over time, and how this is reflected in the architecture DSLs I have built (or helped to build), and how evolving tools have made these changes possible.
The document contains information about a programming paradigms course for the sixth semester of the computer science and engineering department at A.V.C. College of Engineering. It includes the syllabus, units covered, textbooks, and faculty details. The syllabus covers five units: object-oriented programming fundamentals, inheritance, event-driven programming, generic programming, and concurrent programming. The textbook listed is Core Java: Volume I – Fundamentals by Cay S. Horstmann and Gary Cornell. The faculty name provided is Parvathi.M and their designation is Assistant Professor.
From Programming to Modeling And Back AgainMarkus Voelter
This document discusses programming languages and modeling. It notes that programming languages are not expressive, high-level, abstract, domain-specific, or modular enough. It proposes addressing this by using modeling with higher-level, domain-specific concepts and notations, and code generation from models. However, modeling and programming tools have traditionally been separate worlds. The document envisions a future where modeling and programming are integrated by mixing models and programs, and developing languages and tools that support programming at different levels of abstraction from different viewpoints. Enabling technologies could include advanced parser generators and projectional editing, while available tools mentioned include Eclipse Xtext and JetBrains' Meta Programming System for developing domain-specific languages.
This document provides an introduction to clean coding principles including identifying bad code through code smells, writing tests, and refactoring code. It outlines good practices for writing transparent, reasonable, and usable code and emphasizes writing tests as a safety belt and leaving the codebase in better condition. The document presents object-oriented design patterns, principles, knowledge of languages and libraries, code smells, testing, and code reviews/refactoring as tools for writing clean code. It links to an example of refactoring code in a step-by-step manner and emphasizes writing tests as the starting point and keeping them as the grammar for the system.
The document discusses computer programming paradigms and languages. It provides a history of programming languages from machine code to modern high-level languages. It covers key concepts like abstraction, computational paradigms including imperative and functional paradigms, and the lambda calculus foundation of functional programming. Programming language characteristics like readability, portability and security/reliability are also examined.
Understanding, measuring and improving code quality in JavaScriptMark Daggett
The document discusses various ways to measure code quality, including objective and subjective metrics. It describes metrics like cyclomatic complexity, Halstead metrics, and NPATH complexity which measure different aspects of code such as complexity, readability, maintainability, and testability. The document also discusses tools that can analyze code quality and produce reports on lines of code, arguments per function, and other metrics. Overall, the document provides an overview of different techniques for measuring code quality both quantitatively and qualitatively.
Good quality code is an essential property of a software because it could lead to financial losses or waste of time needed for further maintenance, modification or adjustments if code quality is not good enough.
A programming language is a formal language used to describe computations. It consists of syntax, semantics, and tools like compilers or interpreters. Programming languages support different paradigms like procedural, functional, object-oriented, and logic-based approaches. Variables are named locations in memory that hold values and have properties like name, scope, type, and values.
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
Through this slide i am providing you some basic differences between coding and programming, so if u do not know the basic differences between coding and programming then go through this ppt.
for any programming assignment help go through our website-
https://programmingshark.com/
Course: Programming Languages and Paradigms:
A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ...
Course: Programming Languages and Paradigms:
This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics
This document summarizes a workshop on programming paradigms. It defines programming paradigms as fundamental styles of computer programming that serve as patterns or models for programming languages. The workshop covers various programming concepts like eager vs lazy evaluation, procedures vs functions, external vs internal state, concurrency, and static vs dynamic typing. It also uses examples like searching and higher order functions to illustrate differences between paradigms like Java vs Prolog and Java vs Haskell.
This document provides an introduction to the C programming language. It discusses the evolution of C from earlier languages like BCPL and B. C is described as a mid-level, structured programming language that is widely used and gives good machine efficiency while also being understandable by programmers. The document also defines key concepts like algorithms, flowcharts, pseudocode, ASCII/scan codes, and the differences between compilers and interpreters. It provides examples and questions to help learn about programming in C.
1. Programming languages enable humans to communicate with computers by using words, symbols, and codes to direct a computer to perform tasks and control mechanical devices.
2. Early programming languages progressed from machine language and assembly language as first and second generation languages, to higher-level languages like FORTRAN, BASIC, and COBOL as third generation languages. Fourth and fifth generation languages provided more specialized and visual interfaces.
3. Key aspects of programming include structured and object-oriented design, variables and constants, operators, flow control, translation methods like compilers and interpreters, and the development process of problem analysis, design, coding, testing and documentation. Understanding different programming elements and techniques is essential for writing effective programs.
This document discusses different programming paradigms and languages. It describes batch programs which run without user interaction and event-driven programs which respond to user events. It lists many popular programming languages from Machine Language to Java and C#, and describes low-level languages that are close to machine code and high-level languages that are more human-readable. It also discusses language translators like compilers, interpreters, and assemblers and how they convert code between languages. Finally, it covers testing, debugging, and different types of errors in code like syntax, semantic, and run-time errors.
This document discusses the evolution of programming languages from machine languages (1GL) to natural languages (5GL). It begins by defining a programming language as a formal notation system that describes computation in a readable and machine-readable form. It then categorizes generations of programming languages based on their level of abstraction, from low-level machine codes (1GL) to high-level languages (3GL) to domain-specific languages (DSL). The document provides examples and characteristics of languages from each generation.
1. The document discusses getting started with programming in Java, including compiling and running a simple "Hello World" Java program. It explains that a Java program is written as source code in a .java file, then compiled using the javac compiler into a .class file, which is run by the Java Virtual Machine (java).
2. It provides the code for a simple "Hello World" Java program consisting of a main method that prints the text, and explains some key aspects like class and file names.
3. It describes using the terminal to navigate directories and run the javac compiler and java programs, as well as downloading the Java Development Kit for the compiler and JVM.
Program, Language, & Programming Language
Object Oriented Programming vs Procedure Oriented Programming
About C
Why still Learn C?
Basic Terms
C Stuff
C Syntax
C Program
Quality metrics and angular js applicationsnadeembtech
This document discusses various ways to measure and ensure code quality in JavaScript programs. It covers topics like linting, testing, complexity analysis, and static code analysis. Linters like JSHint and ESLint can check for style and syntax issues. Testing tools like Jasmine, Karma, and Protractor allow writing unit and integration tests. Complexity can be measured using metrics related to lines of code, arguments, nesting, and more. Tools like Plato and complexity-report provide metrics and visualizations. Adopting these practices helps reduce bugs and technical debt.
This presentation will introduce you to programming languages that support different programming paradigms and to give you the knowledge of basic concepts and techniques that will allow them to differentiate between various programming paradigms.
The document discusses developing a code quality process including selecting code quality tools like Checkstyle, PMD, and FindBugs to check code style, errors, test coverage, and more. It emphasizes controlling quality to reduce technical debt and bugs. Setting up the tools on a project initially found thousands of issues. The process involved documenting rules, configuring tools, and integrating checks into the build to fail on violations. Manager support and refactoring tasks are important for the ongoing process.
This presentation provides an overview of various programming paradigms including imperative, declarative, functional, object-oriented, and multi-paradigm. It discusses the basic concepts and definitions of each paradigm, provides examples of commonly used languages, and briefly compares the different approaches. The presentation concludes that while no consensus exists on the best paradigm, procedural and object-oriented paradigms using languages like C, C++, and Java tend to be most popular for introductory courses.
There are three main types of programming languages: procedural, declarative, and object-oriented. Procedural languages specify operations in a sequence and use subprograms, variables, and data types. Declarative languages declare facts and rules to solve problems using queries. Object-oriented languages are modeled around objects that can have attributes and methods, and classes act as blueprints to create object instances that can inherit properties from other classes.
This document discusses factors to consider when choosing a programming language such as the type of application being developed, the skills of the programming team, the type of data being used, the operating system requirements, and the need for speed of execution or available APIs and functions. It also recommends practices like using modular and well-named code with internal commentary.
The document discusses switch case statements and looping in programming. It provides examples of switch case statements that check the value of a variable and execute different code blocks depending on the value. It also discusses the different types of loops - for, while, and do-while loops - and provides examples of each. The examples demonstrate how to use switch cases and loops to repeat blocks of code or make decisions based on variable values.
This document discusses structured programming, functional programming, programming style, coding guidelines, software documentation, and challenges in software implementation. Structured programming breaks problems down into smaller pieces and uses modular programming and structured coding. Functional programming uses mathematical functions and avoids side effects. Good programming style and coding guidelines make code more readable and maintainable. Software documentation includes requirements, design, technical, and user documentation. Challenges include code reuse and compatibility issues.
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
Through this slide i am providing you some basic differences between coding and programming, so if u do not know the basic differences between coding and programming then go through this ppt.
for any programming assignment help go through our website-
https://programmingshark.com/
Course: Programming Languages and Paradigms:
A brief introduction to imperative programming principles: history, von neumann, BNF, variables (r-values, l-values), modifiable data structures, order of evaluation, static and dynamic scopes, referencing environments, call by value, control flow (sequencing, selection, iteration), ...
Course: Programming Languages and Paradigms:
This introduces concepts related to programming languate design: abstraction, a bit of history, the syntax, semantics and pragmatics of programming languages, languages as abstraction, thought shaper, simplifier and law enforcer.program verification, denotational and operational semantics
This document summarizes a workshop on programming paradigms. It defines programming paradigms as fundamental styles of computer programming that serve as patterns or models for programming languages. The workshop covers various programming concepts like eager vs lazy evaluation, procedures vs functions, external vs internal state, concurrency, and static vs dynamic typing. It also uses examples like searching and higher order functions to illustrate differences between paradigms like Java vs Prolog and Java vs Haskell.
This document provides an introduction to the C programming language. It discusses the evolution of C from earlier languages like BCPL and B. C is described as a mid-level, structured programming language that is widely used and gives good machine efficiency while also being understandable by programmers. The document also defines key concepts like algorithms, flowcharts, pseudocode, ASCII/scan codes, and the differences between compilers and interpreters. It provides examples and questions to help learn about programming in C.
1. Programming languages enable humans to communicate with computers by using words, symbols, and codes to direct a computer to perform tasks and control mechanical devices.
2. Early programming languages progressed from machine language and assembly language as first and second generation languages, to higher-level languages like FORTRAN, BASIC, and COBOL as third generation languages. Fourth and fifth generation languages provided more specialized and visual interfaces.
3. Key aspects of programming include structured and object-oriented design, variables and constants, operators, flow control, translation methods like compilers and interpreters, and the development process of problem analysis, design, coding, testing and documentation. Understanding different programming elements and techniques is essential for writing effective programs.
This document discusses different programming paradigms and languages. It describes batch programs which run without user interaction and event-driven programs which respond to user events. It lists many popular programming languages from Machine Language to Java and C#, and describes low-level languages that are close to machine code and high-level languages that are more human-readable. It also discusses language translators like compilers, interpreters, and assemblers and how they convert code between languages. Finally, it covers testing, debugging, and different types of errors in code like syntax, semantic, and run-time errors.
This document discusses the evolution of programming languages from machine languages (1GL) to natural languages (5GL). It begins by defining a programming language as a formal notation system that describes computation in a readable and machine-readable form. It then categorizes generations of programming languages based on their level of abstraction, from low-level machine codes (1GL) to high-level languages (3GL) to domain-specific languages (DSL). The document provides examples and characteristics of languages from each generation.
1. The document discusses getting started with programming in Java, including compiling and running a simple "Hello World" Java program. It explains that a Java program is written as source code in a .java file, then compiled using the javac compiler into a .class file, which is run by the Java Virtual Machine (java).
2. It provides the code for a simple "Hello World" Java program consisting of a main method that prints the text, and explains some key aspects like class and file names.
3. It describes using the terminal to navigate directories and run the javac compiler and java programs, as well as downloading the Java Development Kit for the compiler and JVM.
Program, Language, & Programming Language
Object Oriented Programming vs Procedure Oriented Programming
About C
Why still Learn C?
Basic Terms
C Stuff
C Syntax
C Program
Quality metrics and angular js applicationsnadeembtech
This document discusses various ways to measure and ensure code quality in JavaScript programs. It covers topics like linting, testing, complexity analysis, and static code analysis. Linters like JSHint and ESLint can check for style and syntax issues. Testing tools like Jasmine, Karma, and Protractor allow writing unit and integration tests. Complexity can be measured using metrics related to lines of code, arguments, nesting, and more. Tools like Plato and complexity-report provide metrics and visualizations. Adopting these practices helps reduce bugs and technical debt.
This presentation will introduce you to programming languages that support different programming paradigms and to give you the knowledge of basic concepts and techniques that will allow them to differentiate between various programming paradigms.
The document discusses developing a code quality process including selecting code quality tools like Checkstyle, PMD, and FindBugs to check code style, errors, test coverage, and more. It emphasizes controlling quality to reduce technical debt and bugs. Setting up the tools on a project initially found thousands of issues. The process involved documenting rules, configuring tools, and integrating checks into the build to fail on violations. Manager support and refactoring tasks are important for the ongoing process.
This presentation provides an overview of various programming paradigms including imperative, declarative, functional, object-oriented, and multi-paradigm. It discusses the basic concepts and definitions of each paradigm, provides examples of commonly used languages, and briefly compares the different approaches. The presentation concludes that while no consensus exists on the best paradigm, procedural and object-oriented paradigms using languages like C, C++, and Java tend to be most popular for introductory courses.
There are three main types of programming languages: procedural, declarative, and object-oriented. Procedural languages specify operations in a sequence and use subprograms, variables, and data types. Declarative languages declare facts and rules to solve problems using queries. Object-oriented languages are modeled around objects that can have attributes and methods, and classes act as blueprints to create object instances that can inherit properties from other classes.
This document discusses factors to consider when choosing a programming language such as the type of application being developed, the skills of the programming team, the type of data being used, the operating system requirements, and the need for speed of execution or available APIs and functions. It also recommends practices like using modular and well-named code with internal commentary.
The document discusses switch case statements and looping in programming. It provides examples of switch case statements that check the value of a variable and execute different code blocks depending on the value. It also discusses the different types of loops - for, while, and do-while loops - and provides examples of each. The examples demonstrate how to use switch cases and loops to repeat blocks of code or make decisions based on variable values.
This document discusses structured programming, functional programming, programming style, coding guidelines, software documentation, and challenges in software implementation. Structured programming breaks problems down into smaller pieces and uses modular programming and structured coding. Functional programming uses mathematical functions and avoids side effects. Good programming style and coding guidelines make code more readable and maintainable. Software documentation includes requirements, design, technical, and user documentation. Challenges include code reuse and compatibility issues.
This document discusses structured programming, functional programming, programming style, coding guidelines, software documentation, and challenges in software implementation. Structured programming breaks problems down into smaller pieces and uses modular programming and structured coding. Functional programming uses mathematical functions and concepts like recursion. Good programming style and coding guidelines make code readable and understandable. Software documentation includes requirements, design, technical, and user documentation. Challenges include code reuse, version management, and designing for target hosts.
The document provides information about computing and programming fundamentals. It discusses the programming process, including developing a program through the program development life cycle of planning, coding, testing, and maintaining a program. It also describes algorithms, flowcharts, and pseudocode - tools used to design programs. Algorithms are sets of steps to solve a problem, flowcharts use graphical symbols to represent program logic, and pseudocode uses a simplified language to design programs before coding.
Programming involves writing computer programs and algorithms using programming languages. There are different types of programming languages from low-level machine languages close to hardware to high-level languages that are more abstract. Popular programming paradigms have evolved over time like structured programming, modular programming, object-oriented programming, and declarative programming to help manage complex programs. The most commonly used programming languages today include Java, Python, and JavaScript.
The document discusses the iterative software development process (SDP). It describes the main stages as analysis, design, implementation, testing, documentation, evaluation, and maintenance. At each stage, specific tasks are undertaken, such as creating requirements in analysis, designing algorithms and diagrams in design, coding in implementation, testing in testing, creating user guides in documentation, and fixing bugs in maintenance. People involved include clients, analysts, project managers, and programmers. Documents created include specifications, algorithms, test reports, guides, and evaluation/maintenance reports.
Rails simplifies the software development process through conventions over configuration and by embracing agile practices. Rails uses simple naming conventions and reflection so that developers can focus on business logic rather than configuration. This allows features like MVC, DRY principles and database mapping to work automatically without extensive setup. The combination of Ruby's elegance and Rails' conventions makes building applications faster and easier for developers.
This document discusses refactoring code through careful modifications that do not change functionality. It emphasizes that refactoring requires rigorous unit testing to be done correctly. Successful refactoring can be achieved through uninterrupted work, pair programming, or test-driven development with an automated testing framework. Tests should be written early and often to catch errors introduced during refactoring. The minimum requirement for testing is assert statements, which provide an easy way to start testing without complex tools. Overall, the document stresses that refactoring is best approached as a disciplined process guided by thorough automated testing.
The document provides a checklist of topics for a code quality training agenda, including: TDD/automated tests, continuous delivery, refactoring, design patterns, emergent design, SOLID principles, SOA, paired programming, UML/object modeling, and miscellaneous code craftsmanship best practices. It notes that the list is not comprehensive and the goal is to help organizations determine which topics would be most valuable for their teams. Descriptions are provided for some of the highlighted topics.
The document provides information on various DevOps concepts through a question and answer format. It defines design patterns as solutions to common problems faced by developers that represent best practices. It describes continuous deployment as instrumenting important project life cycle steps when moving code to production. It distinguishes between functional testing which targets business goals and requirements, and non-functional testing which focuses on aspects like performance and security. It explains the differences between white box testing which uses internal knowledge and black box testing which does not. It provides examples of resilience test tools like Hystrix and Chaos Monkey. It describes extreme programming as an agile methodology focused on customer satisfaction and team collaboration. It defines pair programming as two programmers working together on the same code. Finally
Periodic Table of Agile Principles and PracticesJérôme Kehrli
Recently I fell by chance on the Periodic Table of the Elements... Long time no see... Remembering my physics lessons in University, I always loved that table. I remembered spending hours understanding the layout and admiring the beauty of its natural simplicity.
So I had the idea of trying the same layout, not the same approach since both are not comparable, really only the same layout for Agile Principles and Practices.
The result is in this presentation: The Periodic Table of Agile Principles and Practices:
Agile Methodologies And Extreme ProgrammingUtkarsh Khare
The document discusses Agile development and Extreme Programming (XP). It provides an overview of 12 key practices of XP, including planning games, small releases, test-driven development, pair programming, collective ownership, continuous integration and 40-hour work weeks. It also discusses how XP aims to solve software engineering problems through intensive teamwork, handling changes and staff turnover, and involving customers.
Software design is the process of planning the structure and interfaces of a software program to ensure it functions properly and meets requirements. It includes architectural design to break the program into components and detailed design to break components into classes and interfaces. Software design patterns provide reusable solutions to common problems in design. The most important patterns include adapter, factory method, state, builder, strategy, observer, and singleton. The software design process involves research, prototyping, development, testing, and maintenance.
Introduction to programming language (basic)nharsh2308
This document provides an introduction to programming topics including algorithms, pseudocode, flowcharts, programming languages, compilers, interpreters, testing, debugging and documentation. It discusses the basic model of computation involving understanding requirements, inputs/outputs, designing program layout and output, selecting techniques, and testing. Algorithms are defined as ordered sequences of operations to solve a problem. Pseudocode and flowcharts are used to represent program logic without real syntax. Programming languages are categorized as low-level (machine code) or high-level, with compilers and interpreters used to translate high-level languages. Testing and debugging involve inputting data to find and fix errors. Documentation records the development process for users.
Your startup on AWS - How to architect and maintain a Lean and Mean account J...angelo60207
Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
The State of Web3 Industry- Industry ReportLiveplex
Web3 is poised for mainstream integration by 2030, with decentralized applications potentially reaching billions of users through improved scalability, user-friendly wallets, and regulatory clarity. Many forecasts project trillions of dollars in tokenized assets by 2030 , integration of AI, IoT, and Web3 (e.g. autonomous agents and decentralized physical infrastructure), and the possible emergence of global interoperability standards. Key challenges going forward include ensuring security at scale, preserving decentralization principles under regulatory oversight, and demonstrating tangible consumer value to sustain adoption beyond speculative cycles.
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.
AI Agents in Logistics and Supply Chain Applications Benefits and ImplementationChristine Shepherd
AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.
No-Code Workflows for CAD & 3D Data: Scaling AI-Driven InfrastructureSafe Software
When projects depend on fast, reliable spatial data, every minute counts.
AI Clearing needed a faster way to handle complex spatial data from drone surveys, CAD designs and 3D project models across construction sites. With FME Form, they built no-code workflows to clean, convert, integrate, and validate dozens of data formats – cutting analysis time from 5 hours to just 30 minutes.
Join us, our partner Globema, and customer AI Clearing to see how they:
-Automate processing of 2D, 3D, drone, spatial, and non-spatial data
-Analyze construction progress 10x faster and with fewer errors
-Handle diverse formats like DWG, KML, SHP, and PDF with ease
-Scale their workflows for international projects in solar, roads, and pipelines
If you work with complex data, join us to learn how to optimize your own processes and transform your results with FME.
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
Developing Schemas with FME and Excel - Peak of Data & AI 2025Safe Software
When working with other team members who may not know the Esri GIS platform or may not be database professionals; discussing schema development or changes can be difficult. I have been using Excel to help illustrate and discuss schema design/changes during meetings and it has proven a useful tool to help illustrate how a schema will be built. With just a few extra columns, that Excel file can be sent to FME to create new feature classes/tables. This presentation will go thru the steps needed to accomplish this task and provide some lessons learned and tips/tricks that I use to speed the process.
Enabling BIM / GIS integrations with Other Systems with FMESafe Software
Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes.
To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.
Your startup on AWS - How to architect and maintain a Lean and Mean accountangelo60207
Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
Artificial Intelligence in the Nonprofit Boardroom.pdfOnBoard
OnBoard recently partnered with Microsoft Tech for Social Impact on the AI in the Nonprofit Boardroom Survey, an initiative designed to uncover the current and future role of artificial intelligence in nonprofit governance.
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...Anish Kumar
Presented by: Anish Kumar
LinkedIn: https://www.linkedin.com/in/anishkumar/
This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks).
This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights.
Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.
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
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.
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.
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2. Do you remember the old waterfall model? The waterfall model is a sequential software development process, in which progress is seen as flowing steadily downwards (like a waterfall ) through the phases of Conception, Initiation, Analysis , Design (validation), Construction, Testing and maintenance .
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4. Wide criticism: It is impossible, for any non-trivial project, to get one phase of a software product's lifecycle perfected before moving on to the next phases and learning from them. Clients may not be aware of exactly what requirements they want before they see a working prototype and can comment upon it; they may change their requirements constantly, and program designers and implementers may have little control over this.
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6. Agile software development refers to a group of software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams .
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8. Agile methods break tasks into small increments with minimal planning, and do not directly involve long-term planning. Iterations are short time frames that typically last from one to four weeks. Each iteration involves a team working through a full software development cycle including planning, requirements analysis, design, coding, unit testing , and acceptance testing . The goal of an iteration is to have an available release (with minimal bugs) at the end of each iteration.
9. Minimize overall risk by adapting to changes quickly. The ultimate goal is to REDUCE COST .
10. Maintain the Theory of the Code : Great teams have a shared understanding of how the software system represents the world. Therefore they know where to modify the code when a requirement change occurs. They know exactly where to go hunting for a bug that has been found. They communicate well with each other about the world and the software.
11. Build Less: It has been shown that we build many more features than are actually used. In fact only about 20% of functionality we build is used often or always. More than 60% of all functionality built in software is rarely or never used!
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13. Pay Less for Bug Fixes: Typically, anywhere between 60%-90% of software cost goes into the maintenance phase. Most of our money goes into keeping the software alive and useful for our clients after the initial build.
14. Pay Less for Changes: The only thing constant in today’s software market is change. If we can embrace change, plan for it, and reduce its cost when it eventually happens we can make significant savings.
15. Agile development => Set of chosen practices => Reduce cost => 1- Maintain code theory. 2- Build less. 3- Pay less for bugs. 4- Pay less for changes
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17. Simple design meets the requirements for the current iteration and no more. Simple design reduces cost because you build less code to meet the requirements and you maintain less code afterwards. Simple designs are easier to build, understand, and maintain. Simple design<=> Build less
18. The practice of Refactoring code changes the structure of the code while maintaining its behavior. Costs are reduced because continuous refactoring keeps the design from degrading over time, ensuring that the code is easy to understand, maintain, and change. Refactoring <=> Pay less for change
19. Automated developer tests are a set of tests that are written and maintained by developers to reduce the cost of finding and fixing defects—thereby improving code quality—and to enable the change of the design as requirements are addressed incrementally. Automated developer tests reduce the cost of software development by creating a safety-net of tests that catch bugs early and enabling the incremental change of design. Have a look at TDD or BDD. Developer Tests <=> Pay less for bugs fixing
20. Ruby was conceived on February 24, 1993 by Yukihiro Matsumoto (Matz) who wished to create a new language that balanced functional programming with imperative programming. According to Matsumoto he "wanted a scripting language that was more powerful than Perl, and more object-oriented than Python. That's why I decided to design my own language".
21. Selecting even numbers from a range: (1..25).select {|x| x % 2 == 0 } #=> [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
23. Ruby is a dynamic , reflective , general purpose object-oriented programming language that combines syntax inspired by Perl with Smalltalk-like features. Ruby originated in Japan during the mid-1990s and was initially developed and designed by Yukihiro "Matz" Matsumoto . It is based on Perl , Smalltalk , Eiffel , Ada , and Lisp . Ruby supports multiple programming paradigms , including functional , object oriented , imperative and reflective . It also has a dynamic type system and automatic memory management ; it is therefore similar in varying respects to Python , Perl , Lisp , Dylan , and CLU .
24. Dynamic programming language is a term used broadly in computer science to describe a class of high-level programming languages that execute at runtime many common behaviors that could include extension of the program, by adding new code, by extending objects and definitions, or by modifying the type system, all during program execution.
25. Eval : Evaluating code on runtime. eval “x=1; x+=5” #=> 6 instance_eval, class_eval Higher order functions : Passing functions as arguments. In Ruby we pass blocks (anonymous functions) (1..25).select {|x| x % 2 == 0 } Reflection : Modifying program structure and behavior; treating code like data.
26. Monkey patching: Example: Sum array values class Array def sum self.inject{|sum,current| sum+current } end end [1,5,7,8].sum #=> 21
27. In computer science, reflection is the process by which a computer program can observe and modify its own structure and behavior. Ruby has a very wide set of methods for introspection and reflection.
28. class Foo def hi puts “hi” end def bye puts “bye” end end # Introspection API Foo.new .methods(false) #=> [“hi”,”bye”]
29. class Foo end f = Foo.new f.methods(false) #=>[] f.define_method(:hi) { puts “hello world!" } f.methods(false) #=>[“hi”] f.hi #=> hello world!
30. Ruby’s attr_accessor is an example of Ruby’s reflection use: class Foo attr_accessor :name # getter # def name ; @name ; end # setter # def name=(sn); @name = sn; end end
31. Everything is object even null values! nil.class #=> NilClass This is called Unified Object Model UOM. Cool, you don’t have to think of primitive and reference values, everything is an object and has its methods. Treating everything as an object will extend the language dynamicity to unlimited scopes. You can treat methods as objects as well.
32. Everything is evaluated as an expression in Ruby. Example: You can do this: if num == "one" then val = 1 elsif num == "two" then val = 2 else then val = 3 end But this is better: val = if num == "one" then 1 elsif num == "two" then 2 else 3 end
33. Methods are higher order functions; you can pass code blocks to methods, this makes you focus on the ‘what’ part instead of the ‘how’ one of your problem. Example: selecting even numbers from a range: Traditional imperative way: res = [] input = 1..25 for x in input res << x if x % 2 == 0 end Functional way: res = (1..25).select{|x| x % 2 == 0} How about odd numbers? res = (1..25).select{|x| x % 2 > 0}
34. Functional programming emphasize immutability(pure methods) thus no side effects! s = "hello" #=> "hello“ s2 = s.upcase #=> "HELLO“ puts s.upcase! #=> "HELLO“ puts s , s2 #=> "HELLO“, “hello”
35. Ruby community embraces Test Driven Development, and recently the more recent type of it called Behavior Driven Development BDD. TDD or BDD => Automated test suit => reduce the cost of finding and fixing defects => improving code quality => Productivity reduce cost
36. Remember: Agile development => reduce cost. Ruby code is at least 50% more concise than code written in other languages like Java, which means write less and get the same => productivity. Having less code means less bugs to fix and less code to maintain(refactor) => productivity. Ruby code is so readable, Ruby follows the principle of least surprise(POLS). Readable code means self documenting => productivity. TDD – BDD => improving code quality => Productivity
37. A Domain-specific language(DSL) is a computer language that’s targeted to a particular kind of problem, rather than a general purpose language that’s aimed at any kind of software problem. Regular expressions and CSS are 2 examples of DSLs. Any software language needs a parser and an interpreter(or compiler or a mix), but in DSLs, we have 2 types: external ones which need parsers and interpreters, and internal ones which rely on the hosting language power to give the feel of a particular language, and thus they don’t require their own parsers and they use the language’s interpreter.
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40. ActiveRecord ORM: Traditional: user = User.find(:conditions=>[“name=? and age = ? ”, “khelll”, 18]) Cool: user = User.find_by_name_and_age(“khelll”,26)
41. Ruby is used in almost all fields. The most common use is RubyOnRails or RoR. RoR is a inspiring web framework that was implemented in almost all living languages. RoR is used by yellowpages.com and twitter.com. Ruby is used by system admins as it’s easier to write than shell scripts. Ruby is used in telecomunication systems.
44. CRuby, the official Ruby implementation. JRuby, Java based implementation that runs on JVM, it integrates Java to be used with Ruby. IronRuby, .Net implementation.