LuaJIT is a just-in-time compiler for the Lua 5.1 programming language. It was created in 2005 and compiles Lua bytecode to machine code during execution for improved performance. LuaJIT is compatible with standard Lua APIs and libraries but not bytecode. It uses a technique called dynamic assembly to generate optimized machine code at runtime.
Lua as a business logic language in high load applicationIlya Martynov
This report covers our experience building custom HTTP web server used for the delivery of internet advertising. The application design has as one goals finding the right balance between high performance and ease of development. To achieve this goal we are using Lua as a business logic scripting language embedded into C++ application. The report tries to explain how and why we use Lua and how the choice of Lua affects architecture of the application.
Fluentd Hacking Guide at RubyKaigi 2014Naotoshi Seo
This document summarizes a talk on hacking the Fluentd log streaming framework. It discusses Fluentd's bootstrap sequence and how it loads plugins. It explains how input plugins pass data to output plugins and how the BufferedOutput plugin works by buffering data to avoid blocking. It cautions that output plugins can block which BufferedOutput avoids, but it can also get stuck if the buffer exceeds capacity. It recommends tuning buffer size and thread counts to improve performance.
This document discusses various techniques for IPv6 transition and coexistence with IPv4, including:
- Dual-stack which allows simultaneous support of both IPv4 and IPv6.
- Tunnels which encapsulate IPv6 packets in IPv4 packets to provide IPv6 connectivity through IPv4 networks.
- Translation techniques like NAT64 which allow communication between IPv4-only and IPv6-only nodes.
“p4alu” is a P4 program who would parse UDP packet with payload in "p4alu header format" and apply calculation.
This program is tested using BMv2 simple_switch P4 target.
This document discusses debugging CPython processes with gdb. It begins by stating the goals of making gdb a useful option for debugging and highlighting common issues. It then discusses why debugging is important for large open source projects like OpenStack. Several typical problems that gdb can help with are described, such as hung processes, stepping into native code, and interpreter crashes. Advantages of gdb over pdb are outlined. Prerequisites for using gdb to debug Python are covered, including ensuring gdb has Python support and obtaining CPython debugging symbols. Various gdb commands for debugging Python processes are demonstrated, such as attaching to a process, printing tracebacks and locals, and executing Python code in the debugged process. Finally, some common gotchas are highlighted around virtual environments
Python for IoT discusses building Pyaiot, a system to connect constrained IoT devices to the web. Pyaiot uses common IoT protocols like CoAP and MQTT to allow bidirectional communication between low-power devices and a web dashboard. The author details how Pyaiot was implemented using Python and asyncio to be multi-protocol, modular, and reactive in a real-time manner. Lessons learned include some initial challenges with asyncio, but that Python facilitated fast development of the complex system to meet the initial requirements.
When working with big data or complex algorithms, we often look to parallelize our code to optimize runtime. By taking advantage of a GPUs 1000+ cores, a data scientist can quickly scale out solutions inexpensively and sometime more quickly than using traditional CPU cluster computing. In this webinar, we will present ways to incorporate GPU computing to complete computationally intensive tasks in both Python and R.
See the full presentation here: 👉 https://vimeo.com/153290051
Learn more about the Domino data science platform: https://www.dominodatalab.com
How fluentd fits into the modern software landscapePhil Wilkins
The document discusses using Fluentd to manage logs. It provides an overview of Fluentd, including how it can aggregate and route logs from multiple sources to various outputs like Elasticsearch. It also discusses approaches to scaling Fluentd in distributed environments like Kubernetes, including using sidecars. Real-world challenges with log management are addressed, such as the need to consolidate logs from many distributed services and support multiple analytics tools.
The document provides an overview of the libfabric interface for high performance networking. It discusses the high-level architecture including interfaces, services, and the object model. It describes the control interface for querying capabilities and attributes. It also presents a simple ping-pong example using reliable datagram messaging endpoints to illustrate basic usage.
This document summarizes a presentation about using FluentD for end-to-end monitoring. It discusses the challenges of monitoring modern distributed applications and introduces FluentD as a highly pluggable framework that can capture logs and metrics from various sources and filter, aggregate, and route the data to various outputs like databases, alerting services, and visualization tools. It then provides examples of using FluentD to address challenges like consolidating logs from microservices and filtering critical events. Potential approaches for scaling FluentD in containerized environments are also discussed.
Spend some time working with OpenAPI and gRPC and you’ll notice that these two technologies have a lot in common. Both are open source efforts, both describe APIs, and both promise better experiences for API producers and consumers. So why do we need both? If we do, what value does each provide? What can each project learn from the other? We’ll bring the two together for a side-by-side comparison and pose answers to these and other questions about two API methodologies that will do much to influence the future of networked APIs.
Adopt DevOps philosophy on your Symfony projects (Symfony Live 2011)Fabrice Bernhard
This is the presentation given at the Symfony Live 2011 conference. It is an introduction to the new agile movement spreading in the technical operations community called DevOps and how to adopt it on web development projects, in particular Symfony projects.
Plan of the slides :
- Configuration Management
- Development VM
- Scripted deployment
- Continuous deployment
Tools presented in the slides:
- Puppet
- Vagrant
- Fabric
- Jenkins / Hudson
This document summarizes the Minko 3D graphics engine. Minko 3 will allow scripting 3D apps with Lua and C++ across platforms like Windows, Mac, Linux, Android, iOS, HTML5 and Flash. Key points include:
- Minko 3 is being developed to target new platforms while maintaining a "design once, deploy everywhere" workflow and improving performance.
- It will use C++11 as the core framework with plugins and Lua for scripting 3D apps, allowing development once and deployment across platforms.
- Lua was chosen over ActionScript for its better performance, operators overloading, and support for coroutines which allow non-blocking functions.
IronPython combines the best of Python and .NET by allowing Python code to run on the .NET framework. Microsoft developed IronRuby and IronPython which implement Ruby and Python respectively on .NET. These dynamic languages are supported by the Dynamic Language Runtime (DLR) which provides a common environment for dynamic languages to run on .NET.
How to write a well-behaved Python command line applicationgjcross
Tutorial #1 from PyCon AU 2012
Python is a fantastic scripting language. It is easy to hack up quick scripts for all sorts of problems - without a lot more effort, that hack can become a robust, easily maintained command line application that your users love.
This tutorial covers how to write useful, well-behaved command line applications that are a joy to use:
* Easily process command line options
* Write a script that can be used interactively or as a filter
* Display help to the user
* Gracefully handle and report errors, to the user and the shell
* Trap and process signals in a robust manner
* Create an easily configured application
* Use a range of the Python standard library modules for easier command line scripting
* Test your application
* Set up the supporting files that any well-behaved application should have, eg. a man page
* How to package your application for other people to use
This tutorial will assume a very basic knowledge of Python and some familiarity with the command line environment of your choice.
Network Test Automation - Net Ops Coding 2015Hiroshi Ota
1. The document discusses network test automation using tools like Serverspec, Infrataster, Lbspec, and Rspec-ssltls to test network configurations and connectivity. These tools use Ruby and RSpec to test servers, DNS, firewalls, load balancers, and SSL/TLS without requiring changes to production systems.
2. Examples are provided showing how to test server reachability, DNS entries, firewall rules, load balancer behavior, and SSL/TLS settings using the different tools. Tests can be run to check configurations without affecting live networks.
3. Running the RSpec tests produces results indicating how many examples passed and failed, allowing engineers to test network changes with confidence before deploying
ROS-Industrial is an open-source extension of ROS that aims to solve problems in industrial robotics and automation. It provides drivers for common industrial robots and devices, capabilities for applications like automated painting, and a growing community for developers. The ROS-Industrial Consortium was formed to accelerate development, provide a voice for end users, and build an ecosystem around ROS-Industrial through membership fees and collaborative research projects.
This document provides an overview of ROS (Robotic Operating System) components including nodes, topics, services, and parameters. It describes how to set up a ROS workspace using packages and stacks, configure ROS paths, build packages, and use core ROS tools like rosrun and roslaunch. It also explains how to create ROS messages for topics and services, write publishing/subscribing nodes, and service clients.
This document summarizes the development of Lore's machine learning and NLP platform using Python. It started as a monolithic Python server but evolved into a microservices architecture using Docker, Kubernetes, and Celery for parallelization. Key lessons included using DevOps tools like Docker for development and deployment, Celery to parallelize tasks, and wrapping services to improve modularity, flexibility, and performance. The platform now supports multiple products and consulting work in a scalable and maintainable way.
IronRuby is a Ruby implementation that compiles Ruby code to .NET Intermediate Language. It allows Ruby code to run on the .NET Common Language Runtime and interoperate with .NET libraries. IronRuby was started by Microsoft but is now an open source project. It enables Ruby developers to build applications that integrate with existing .NET systems and libraries. However, IronRuby is still missing support for some Ruby standards like OpenSSL and has a lower test passing rate than MRI Ruby.
Ractor is a new experimental feature in Ruby 3.0 that allows Ruby code to run in parallel on CPUs. It manages objects per Ractor and can move objects between Ractors, making moved objects invisible to the original Ractor. It can share certain "shareable" objects like modules, classes, and frozen objects between Ractors. For web applications to fully utilize Ractors, an experimental application server called Right Speed was created that uses Rack and runs processing workers on Ractors. However, there are still problems to address like exceptions when closing connections and accessing non-shareable constants and instance variables across Ractors before Ractors can be ready for production use in web applications.
Netty is a NIO client server framework that enables quick development of network applications such as protocol servers and clients. It is asynchronous and uses non-blocking IO to share threads across many connections. Netty supports protocols like TCP, UDP, HTTP and provides codecs for serialization and compression. Companies like Twitter, Facebook, and Red Hat use Netty in their projects.
Rust is a multi-paradigm systems programming language focused on safety, especially safe concurrency. It was created by Mozilla in 2006 and released in 2015. Rust aims for speed, concurrency, and safety through eliminating garbage collection and ensuring memory safety and thread safety via its ownership and borrowing system. While syntactically similar to C++, Rust puts an emphasis on writing safe code and preventing common bugs like buffer overflows.
Pulumi is an open source infrastructure as code tool that allows developers to define cloud infrastructure in popular programming languages like TypeScript, Python, and Go. This enables infrastructure to be defined as code and deployed consistently across clouds like AWS, Azure, and GCP. Pulumi uses a desired state model where it compares the actual and desired state of resources and only makes changes where needed. It was created by a Seattle startup in 2018 and supports defining infrastructure from code for both traditional resources like VMs as well as modern architectures like containers and serverless functions.
JRuby allows developers to write plugins for data processing systems like Norikra and Embulk in Ruby while taking advantage of Java libraries and the JVM. Norikra is a stream processing system that allows SQL queries over data streams. It is written in JRuby and uses the Java Esper library. Embulk is an open-source ETL tool that loads data between databases and file formats using plugins. Both systems use a plugin architecture where plugins can be written in JRuby or Java and are distributed as Ruby gems. This allows for a pluggable ecosystem that benefits from Ruby's productivity while utilizing Java libraries and the JVM's performance.
This document summarizes some key features and benefits of the Go programming language. It discusses Go's support for concurrency with lightweight goroutines and channels for communication between goroutines. It also covers Go's syntax which is similar to C but with memory safety due to garbage collection, and its standard library and tools. Finally it provides an example of Go's use at a large company for building high throughput low latency network services.
How fluentd fits into the modern software landscapePhil Wilkins
The document discusses using Fluentd to manage logs. It provides an overview of Fluentd, including how it can aggregate and route logs from multiple sources to various outputs like Elasticsearch. It also discusses approaches to scaling Fluentd in distributed environments like Kubernetes, including using sidecars. Real-world challenges with log management are addressed, such as the need to consolidate logs from many distributed services and support multiple analytics tools.
The document provides an overview of the libfabric interface for high performance networking. It discusses the high-level architecture including interfaces, services, and the object model. It describes the control interface for querying capabilities and attributes. It also presents a simple ping-pong example using reliable datagram messaging endpoints to illustrate basic usage.
This document summarizes a presentation about using FluentD for end-to-end monitoring. It discusses the challenges of monitoring modern distributed applications and introduces FluentD as a highly pluggable framework that can capture logs and metrics from various sources and filter, aggregate, and route the data to various outputs like databases, alerting services, and visualization tools. It then provides examples of using FluentD to address challenges like consolidating logs from microservices and filtering critical events. Potential approaches for scaling FluentD in containerized environments are also discussed.
Spend some time working with OpenAPI and gRPC and you’ll notice that these two technologies have a lot in common. Both are open source efforts, both describe APIs, and both promise better experiences for API producers and consumers. So why do we need both? If we do, what value does each provide? What can each project learn from the other? We’ll bring the two together for a side-by-side comparison and pose answers to these and other questions about two API methodologies that will do much to influence the future of networked APIs.
Adopt DevOps philosophy on your Symfony projects (Symfony Live 2011)Fabrice Bernhard
This is the presentation given at the Symfony Live 2011 conference. It is an introduction to the new agile movement spreading in the technical operations community called DevOps and how to adopt it on web development projects, in particular Symfony projects.
Plan of the slides :
- Configuration Management
- Development VM
- Scripted deployment
- Continuous deployment
Tools presented in the slides:
- Puppet
- Vagrant
- Fabric
- Jenkins / Hudson
This document summarizes the Minko 3D graphics engine. Minko 3 will allow scripting 3D apps with Lua and C++ across platforms like Windows, Mac, Linux, Android, iOS, HTML5 and Flash. Key points include:
- Minko 3 is being developed to target new platforms while maintaining a "design once, deploy everywhere" workflow and improving performance.
- It will use C++11 as the core framework with plugins and Lua for scripting 3D apps, allowing development once and deployment across platforms.
- Lua was chosen over ActionScript for its better performance, operators overloading, and support for coroutines which allow non-blocking functions.
IronPython combines the best of Python and .NET by allowing Python code to run on the .NET framework. Microsoft developed IronRuby and IronPython which implement Ruby and Python respectively on .NET. These dynamic languages are supported by the Dynamic Language Runtime (DLR) which provides a common environment for dynamic languages to run on .NET.
How to write a well-behaved Python command line applicationgjcross
Tutorial #1 from PyCon AU 2012
Python is a fantastic scripting language. It is easy to hack up quick scripts for all sorts of problems - without a lot more effort, that hack can become a robust, easily maintained command line application that your users love.
This tutorial covers how to write useful, well-behaved command line applications that are a joy to use:
* Easily process command line options
* Write a script that can be used interactively or as a filter
* Display help to the user
* Gracefully handle and report errors, to the user and the shell
* Trap and process signals in a robust manner
* Create an easily configured application
* Use a range of the Python standard library modules for easier command line scripting
* Test your application
* Set up the supporting files that any well-behaved application should have, eg. a man page
* How to package your application for other people to use
This tutorial will assume a very basic knowledge of Python and some familiarity with the command line environment of your choice.
Network Test Automation - Net Ops Coding 2015Hiroshi Ota
1. The document discusses network test automation using tools like Serverspec, Infrataster, Lbspec, and Rspec-ssltls to test network configurations and connectivity. These tools use Ruby and RSpec to test servers, DNS, firewalls, load balancers, and SSL/TLS without requiring changes to production systems.
2. Examples are provided showing how to test server reachability, DNS entries, firewall rules, load balancer behavior, and SSL/TLS settings using the different tools. Tests can be run to check configurations without affecting live networks.
3. Running the RSpec tests produces results indicating how many examples passed and failed, allowing engineers to test network changes with confidence before deploying
ROS-Industrial is an open-source extension of ROS that aims to solve problems in industrial robotics and automation. It provides drivers for common industrial robots and devices, capabilities for applications like automated painting, and a growing community for developers. The ROS-Industrial Consortium was formed to accelerate development, provide a voice for end users, and build an ecosystem around ROS-Industrial through membership fees and collaborative research projects.
This document provides an overview of ROS (Robotic Operating System) components including nodes, topics, services, and parameters. It describes how to set up a ROS workspace using packages and stacks, configure ROS paths, build packages, and use core ROS tools like rosrun and roslaunch. It also explains how to create ROS messages for topics and services, write publishing/subscribing nodes, and service clients.
This document summarizes the development of Lore's machine learning and NLP platform using Python. It started as a monolithic Python server but evolved into a microservices architecture using Docker, Kubernetes, and Celery for parallelization. Key lessons included using DevOps tools like Docker for development and deployment, Celery to parallelize tasks, and wrapping services to improve modularity, flexibility, and performance. The platform now supports multiple products and consulting work in a scalable and maintainable way.
IronRuby is a Ruby implementation that compiles Ruby code to .NET Intermediate Language. It allows Ruby code to run on the .NET Common Language Runtime and interoperate with .NET libraries. IronRuby was started by Microsoft but is now an open source project. It enables Ruby developers to build applications that integrate with existing .NET systems and libraries. However, IronRuby is still missing support for some Ruby standards like OpenSSL and has a lower test passing rate than MRI Ruby.
Ractor is a new experimental feature in Ruby 3.0 that allows Ruby code to run in parallel on CPUs. It manages objects per Ractor and can move objects between Ractors, making moved objects invisible to the original Ractor. It can share certain "shareable" objects like modules, classes, and frozen objects between Ractors. For web applications to fully utilize Ractors, an experimental application server called Right Speed was created that uses Rack and runs processing workers on Ractors. However, there are still problems to address like exceptions when closing connections and accessing non-shareable constants and instance variables across Ractors before Ractors can be ready for production use in web applications.
Netty is a NIO client server framework that enables quick development of network applications such as protocol servers and clients. It is asynchronous and uses non-blocking IO to share threads across many connections. Netty supports protocols like TCP, UDP, HTTP and provides codecs for serialization and compression. Companies like Twitter, Facebook, and Red Hat use Netty in their projects.
Rust is a multi-paradigm systems programming language focused on safety, especially safe concurrency. It was created by Mozilla in 2006 and released in 2015. Rust aims for speed, concurrency, and safety through eliminating garbage collection and ensuring memory safety and thread safety via its ownership and borrowing system. While syntactically similar to C++, Rust puts an emphasis on writing safe code and preventing common bugs like buffer overflows.
Pulumi is an open source infrastructure as code tool that allows developers to define cloud infrastructure in popular programming languages like TypeScript, Python, and Go. This enables infrastructure to be defined as code and deployed consistently across clouds like AWS, Azure, and GCP. Pulumi uses a desired state model where it compares the actual and desired state of resources and only makes changes where needed. It was created by a Seattle startup in 2018 and supports defining infrastructure from code for both traditional resources like VMs as well as modern architectures like containers and serverless functions.
JRuby allows developers to write plugins for data processing systems like Norikra and Embulk in Ruby while taking advantage of Java libraries and the JVM. Norikra is a stream processing system that allows SQL queries over data streams. It is written in JRuby and uses the Java Esper library. Embulk is an open-source ETL tool that loads data between databases and file formats using plugins. Both systems use a plugin architecture where plugins can be written in JRuby or Java and are distributed as Ruby gems. This allows for a pluggable ecosystem that benefits from Ruby's productivity while utilizing Java libraries and the JVM's performance.
This document summarizes some key features and benefits of the Go programming language. It discusses Go's support for concurrency with lightweight goroutines and channels for communication between goroutines. It also covers Go's syntax which is similar to C but with memory safety due to garbage collection, and its standard library and tools. Finally it provides an example of Go's use at a large company for building high throughput low latency network services.
Python was created in the late 1980s by Guido van Rossum and first released in 1991. It gained popularity and saw new releases in 2000 and 2008. Python is an open source, general purpose, interpreted, object-oriented programming language used widely for web development, science, and more due to its large community and support. Performance depends more on frameworks and architecture than language alone.
Python is a general purpose, dynamic, high-level and interpreted programming language. It is used widely in data science, machine learning, web development, automation and more. Python was created in the 1990s by Guido van Rossum to be an interpreted language that bridged the gap between C and shell scripting. It has many advantages like being readable, cross-platform, having a large standard library and being open source.
Lua is a lightweight scripting language embedded in many applications like Wireshark and Redis. It is small but powerful, with features like closures, coroutines, and metatables. Lua is embedded via its C API and allows for extending applications with modules written in Lua. Popular modules include LuaSocket and LuaSQL. Lua sees widespread use due to its small size, speed, portability, and ability to extend large C/C++ applications with scripting.
Programming in python in detail concept .pptxKavitha713564
The document provides information on comparisons between programming languages C, Python, Java and Python. It discusses key features, pros and cons of each language. C was developed in 1972 and is a procedural, compiled language known for efficiency. Python was developed in 1991 and is a high-level, interpreted, object-oriented language known for simplicity. Java was developed in 1995 and is a compiled, class-based, object-oriented language known for platform independence. Both Python and Java are popular languages but Python is easier to learn and more expressive while Java is faster and more secure.
This document is a term paper on the programming language Lua submitted by Varun Sharma to Amity University. It includes a declaration signed by Varun stating that the work is original, a certificate signed by his professor confirming his work, and an index of sections to be covered in the paper. The paper will provide an introduction to Lua, discuss its history and evolution, benefits, and how to program in Lua. It will also compare Lua to other scripting languages.
Python is a widely used general purpose programming language that was created in the late 1980s by Guido van Rossum. It emphasizes code readability and has a large standard library. It supports multiple programming paradigms like object oriented, imperative, and functional programming. Compared to other languages, Python programs are typically shorter than equivalent programs in languages like Java due to features like dynamic typing.
Introduction to python -easiest way to understand python for beginners
What is Python…?
Differences between programming and scripting language
Programming Paradigms
History of Python
Scope of Python
Why do people use Python?
Installing Python
Python is an easy to learn programming language that is widely used for a variety of tasks. It has a simple syntax that allows developers to focus on solving problems rather than dealing with complex language features. Python code can be written quickly and read easily by others. It also has a large ecosystem of libraries and frameworks that support application development, data science, machine learning, and more. While not the fastest language, Python makes up for it with versatility and the ability to connect different systems through its "glue" programming capabilities.
Python is an interpreted, object-oriented, high-level programming language that emphasizes code readability. It has a large standard library, dynamic typing, and is available for free on all major platforms. Python supports multiple programming paradigms including procedural, object-oriented, and functional programming. It is commonly used for web development, scripting, and rapid application development due to its simple syntax and readability.
Python is a powerful and object-oriented programming language that has grown rapidly in popularity due to its simplicity and flexibility. It supports multiple programming paradigms and has a large standard library. Python source code is first compiled to bytecode, which is then executed by the Python Virtual Machine. While Java may be faster for single algorithms, Python is easier for beginners to learn and its dynamic typing and automatic memory management make programs quicker to write. It has gained widespread use for web development, data science, and scripting.
Python is a widely used general-purpose, high-level programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C.The language provides constructs intended to enable clear programs on both a small and large scale.Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.
This document introduces Python and discusses its main features and advantages over other languages like Java. Python is described as a high-level, multi-paradigm language with simple yet powerful semantics and a focus on productivity. It discusses how Python code is more concise, readable and fun to write compared to Java, C#, and other languages. Python trusts the programmer and aims to avoid getting in the way. It also has a rich standard library and ecosystem of third-party libraries.
Python is an interpreted, object-oriented programming language created by Guido van Rossum in 1990. It has a clear, readable syntax and is used for rapid prototyping, scripting, web development and more. Key features of Python include its clean syntax, extensive standard library, readability, extensibility via C/C++, and emphasis on code readability and maintenance through use of whitespace and comments.
This document provides an overview of the Python programming language. It discusses Python's history, key features such as being easy to use, scalable, high-level, object-oriented, interpreted, and having a rich core library. It also covers Python's uses in areas like web development, databases, GUI programming, and more. The document is intended to introduce readers to Python and provide context for a book on making use of the language.
Python For All | Software Professionals, QA & DevOps professionalsNilesh Sutar
This training is designed for entry level software professionals, testers and DevOps professionals to spread awareness and generate interest about Python
The document provides an overview of the history and key features of the Python programming language. It discusses that Python was created in 1989 by Guido van Rossum as an easy to use scripting language. It gained popularity through its open source model and large standard library. Key features include being easy to learn, having a clear readable syntax, being interpreted, object-oriented, cross-platform, and having a vast library of functions. Popular applications of Python include GUI programs, web development, science, education, and more.
Today python and java development are extremely popular among programmers when it comes to choosing a programming language to utilize in data science. Explore the pdf to get a better idea about these two programming languages.
Domino IQ – Was Sie erwartet, erste Schritte und Anwendungsfällepanagenda
Webinar Recording: https://www.panagenda.com/webinars/domino-iq-was-sie-erwartet-erste-schritte-und-anwendungsfalle/
HCL Domino iQ Server – Vom Ideenportal zur implementierten Funktion. Entdecken Sie, was es ist, was es nicht ist, und erkunden Sie die Chancen und Herausforderungen, die es bietet.
Wichtige Erkenntnisse
- Was sind Large Language Models (LLMs) und wie stehen sie im Zusammenhang mit Domino iQ
- Wesentliche Voraussetzungen für die Bereitstellung des Domino iQ Servers
- Schritt-für-Schritt-Anleitung zur Einrichtung Ihres Domino iQ Servers
- Teilen und diskutieren Sie Gedanken und Ideen, um das Potenzial von Domino iQ zu maximieren
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.
Evaluation Challenges in Using Generative AI for Science & Technical ContentPaul Groth
Evaluation Challenges in Using Generative AI for Science & Technical Content.
Foundation Models show impressive results in a wide-range of tasks on scientific and legal content from information extraction to question answering and even literature synthesis. However, standard evaluation approaches (e.g. comparing to ground truth) often don't seem to work. Qualitatively the results look great but quantitive scores do not align with these observations. In this talk, I discuss the challenges we've face in our lab in evaluation. I then outline potential routes forward.
Jira Administration Training – Day 1 : IntroductionRavi Teja
This presentation covers the basics of Jira for beginners. Learn how Jira works, its key features, project types, issue types, and user roles. Perfect for anyone new to Jira or preparing for Jira Admin roles.
Presentation given at the LangChain community meetup London
https://lu.ma/9d5fntgj
Coveres
Agentic AI: Beyond the Buzz
Introduction to AI Agent and Agentic AI
Agent Use case and stats
Introduction to LangGraph
Build agent with LangGraph Studio V2
Data Virtualization: Bringing the Power of FME to Any ApplicationSafe Software
Imagine building web applications or dashboards on top of all your systems. With FME’s new Data Virtualization feature, you can deliver the full CRUD (create, read, update, and delete) capabilities on top of all your data that exploit the full power of FME’s all data, any AI capabilities. Data Virtualization enables you to build OpenAPI compliant API endpoints using FME Form’s no-code development platform.
In this webinar, you’ll see how easy it is to turn complex data into real-time, usable REST API based services. We’ll walk through a real example of building a map-based app using FME’s Data Virtualization, and show you how to get started in your own environment – no dev team required.
What you’ll take away:
-How to build live applications and dashboards with federated data
-Ways to control what’s exposed: filter, transform, and secure responses
-How to scale access with caching, asynchronous web call support, with API endpoint level security.
-Where this fits in your stack: from web apps, to AI, to automation
Whether you’re building internal tools, public portals, or powering automation – this webinar is your starting point to real-time data delivery.
Soulmaite review - Find Real AI soulmate reviewSoulmaite
Looking for an honest take on Soulmaite? This Soulmaite review covers everything you need to know—from features and pricing to how well it performs as a real AI soulmate. We share how users interact with adult chat features, AI girlfriend 18+ options, and nude AI chat experiences. Whether you're curious about AI roleplay porn or free AI NSFW chat with no sign-up, this review breaks it down clearly and informatively.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/state-space-models-vs-transformers-for-ultra-low-power-edge-ai-a-presentation-from-brainchip/
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit.
At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
Create Your First AI Agent with UiPath Agent BuilderDianaGray10
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Presented by: Anish Kumar
LinkedIn: https://www.linkedin.com/in/anishkumar/
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3. Script for making Applications
Background
- The complexity of applications increased extremely.
- Applications need to be applied large-scale refactoring.
What can the script do ?
- Can reduce code-size and increase maintainability.
- Make it easy to append new applications.
- When the middleware layer is modified, the modification doesn’t propagate to the
application layer.
It’s limited to the script-engine layer.
Candidate script languages
- Lua, Python
5. What’s Lua
Lua is Implemented and maintained by Tecgraf*1.
Lua has used in many industrial applications
such as Adobe’s Photoshop Lightroom.
Lua is the leading scripting language in games.
Lua is Embedded in games ( e.g. World of Warcraft ) and Ginga*2.
: Tecgraf - http://www.tecgraf.puc-rio.br/
*1
: Ginga brasil – 브라질향 데이터방송 규격
*2
6. Why Lua
Lua is small
- The source contains around 17,000 Lines of C.
- All standard libraries take just 144K Bytes.
Lua is powerful
- Lua provides meta-mechanisms for implementing classes and inheritance.
- Lua provides garbage collection.
Lua is fast
- Several benchmarks show Lua as the one of the fastest languages.
Lua is portable & embeddable
- Lua is definitely easy to be embedded with small footprint.
Lua is free : MIT License*1
: MIT License - http://ko.wikipedia.org/wiki/MIT_%EB%9D%BC%EC%9D%B4%EC%84%A0%EC%8A%A4
*1
7. Lua-Application Prototyping - 1/2
We ported mobile Calculator into Lua
- We implemented UI Wrapper functions which are required in Calculator.
- GTK-related functions and Softkey functions.
- Calculator Logic is implemented by Lua script.
- Main functions which is needed in 4 fundamental rules of Arithmetic are ported now.
- The prototype of Lua-Calculator is successfully ported in our mobile platform.
10. What’s Python
Released first by Guido van Rossum in 1991.
Now managed by Python Software Foundation*1
Embedded as a scripting language in these S/W products. *2
- GIMP, gedit, Vim, Maya etc
- In mobile domain, Python for s60*3
Features a fully dynamic type, automatic memory management.
*1
: Python Software Foundation – Wikipedia, http://en.wikipedia.org/wiki/Python_Software_Foundation
*2
: Python-embedded S/W – Wikipedia, http://en.wikipedia.org/wiki/Python_software
*3
: Python for s60 – nokia.com http://wiki.opensource.nokia.com/projects/Python_for_S60
11. Why Python
Highly Readable
- Python uses whitespace indentation to delimit statement blocks.
Powerful Standard Library
- Python has a very large standard library.
Powerful Glue Language
- Python has the ability to use a lower-level language such as C/C++.
- Python can easily attach new modules by using ctypes*1 or swig*2, etc.
Free
- Python Software Foundation License*3
: Allows modifications to the source code and derivative works.
*1
: Ctypes – http://www.python.org/doc/2.5/lib/module-ctypes.html
*2
: Swig – http://www.swig.org
*3
: Python Software Foundation License – http://en.wikipedia.org/wiki/Python_Software_Foundation_License
12. Python-Application Prototyping : Calculator
To wrap gtk layer, we try to use PyGTK*1.
platform-dependent functions such as Softkeys
are not yet implemented by hand.
Calculator UI : 114 Lines of python code
- Example UI code :
Y:funcalc.py
: PyGTK : GTK wrapper for python - http://www.pygtk.org/
*1
14. Lua vs Python
Lua Python
Binary size about 200KB About 2MB
(Lua version 5.1) (Python version 2.6)
Memory usage Lua is more compact
Speed Lua is faster*1
Multi-threading Thread-safe Poor-quality
Standard Library Small & compact Rich & powerful
Glue ability Easy Normal
*1
: see also Lua vs Python : CPU+Memory
15. What is the most suitable for mobile applications
Lua is more compact and faster than Python. But Python is further
powerful.
- Python has lots of build-in modules and many open-source wrapper modules such as
pyGtk.
In case of mobile application which has abundant resources,
Python is the most suitable for applications.