A quick introduction to Reactive Programming which is programming with asynchronous data streams. With RxJS you are given an amazing toolbox of functions to combine, create and filter any of those streams.
Beyond fault tolerance with actor programming - Fabio Tiriticco - Codemotion ...Codemotion
The Actor model has been around for a while, but only the Reactive revolution is bringing it to trend. Find out how your application can benefit from Actors to achieve Resilience - the ability to spring back into shape from a failure state. Akka is a toolkit that brings Actors to the JVM - think Java or Scala - and that leverages on them to help you build concurrent, distributed and resilient applications.
Beyond Fault Tolerance with Actor ProgrammingFabio Tiriticco
Actor Programming is a software building approach that lets you can go beyond fault tolerance and achieve Resilience, which is the capacity of a system to self-heal and spring back into a fresh shape. First I'll introduce the difference between Reactive Programming and Reactive Systems, and then we'll go over a couple of implementation examples using Scala and Akka.
The coupled GitHub repository with the code is here: https://github.com/ticofab/ActorDemo
Justin Donaldson's speech at WWX2014, other videos, photos, slides and comments : http://www.silexlabs.org/?p=202971
The 4th International Haxe Conference WWX2014 organized by Silex Labs took place from 23th to 26th may 2014 in Paris.
http://wwx.silexlabs.org/2014/
Beyond fault tolerance with actor programming - Fabio Tiriticco - Codemotion ...Codemotion
The Actor model has been around for a while, but only the Reactive revolution is bringing it to trend. Find out how your application can benefit from Actors to achieve Resilience - the ability to spring back into shape from a failure state. Akka is a toolkit that brings Actors to the JVM - think Java or Scala - and that leverages on them to help you build concurrent, distributed and resilient applications.
Beyond Fault Tolerance with Actor ProgrammingFabio Tiriticco
Actor Programming is a software building approach that lets you can go beyond fault tolerance and achieve Resilience, which is the capacity of a system to self-heal and spring back into a fresh shape. First I'll introduce the difference between Reactive Programming and Reactive Systems, and then we'll go over a couple of implementation examples using Scala and Akka.
The coupled GitHub repository with the code is here: https://github.com/ticofab/ActorDemo
Justin Donaldson's speech at WWX2014, other videos, photos, slides and comments : http://www.silexlabs.org/?p=202971
The 4th International Haxe Conference WWX2014 organized by Silex Labs took place from 23th to 26th may 2014 in Paris.
http://wwx.silexlabs.org/2014/
Flow Base Programming with Node-RED and Functional Reactive Programming with ...Sven Beauprez
To overcome callback hell in node.js, the presentation gives an overview of the advantages of using Flow Based Programming with node-RED and Functional Reactive Programming with Bacon.js.
The presentation was made for IoTBE (Internet of Things Belgium) user group to prove that node.js can actually be used for the Internet of Things (IoT).
Stream Processing with CompletableFuture and Flow in Java 9Trayan Iliev
Stream based data / event / message processing becomes preferred way of achieving interoperability and real-time communication in distributed SOA / microservice / database architectures.
Beside lambdas, Java 8 introduced two new APIs explicitly dealing with stream data processing:
- Stream - which is PULL-based and easily parallelizable;
- CompletableFuture / CompletionStage - which allow composition of PUSH-based, non-blocking, asynchronous data processing pipelines.
Java 9 will provide further support for stream-based data-processing by extending the CompletableFuture with additional functionality – support for delays and timeouts, better support for subclassing, and new utility methods.
More, Java 9 provides new java.util.concurrent.Flow API implementing Reactive Streams specification that enables reactive programming and interoperability with libraries like Reactor, RxJava, RabbitMQ, Vert.x, Ratpack, and Akka.
The presentation will discuss the novelties in Java 8 and Java 9 supporting stream data processing, describing the APIs, models and practical details of asynchronous pipeline implementation, error handling, multithreaded execution, asyncronous REST service implementation, interoperability with existing libraries.
There are provided demo examples (code on GitHub) using Completable Future and Flow with:
- JAX-RS 2.1 AsyncResponse, and more importantly unit-testing the async REST service method implementations;
- CDI 2.0 asynchronous observers (fireAsync / @ObservesAsync);
An introduction to reactive programming concepts and basics. I aim here to show what's reactive programming, why it's used and show some frameworks and benchmarks that support it.
ClojureScript - Making Front-End development Fun again - John Stevenson - Cod...Codemotion
Front-end development has an amazing assortment of libraries and tools, yet it can seem very complex and doest seem much fun. So we'll live code a ClojureScript application (with a bit of help from Git) and show how development doesn't have to be complex or slow. Through live evaluation, we can build a reactive, functional application. Why not take a look at a well designed language that uses modern functional & reactive concepts for building Front-End apps. You are going to have to trans-pile anyway, so why not use a language, libraries and tooling that is bursting with fun to use.
Reactive Java: Promises and Streams with Reakt (JavaOne Talk 2016)Rick Hightower
see labs at https://github.com/advantageous/j1-talks-2016
Import based on PPT so there is more notes. This is from our JavaOne Talk 2016 on Reakt, reactive Java programming with promises, circuit breakers, and streams. Reakt is a reactive Java lib that provides promises, streams, and a reactor to handle asynchronous call coordination. It was influenced by the design of promises in ES6. You want to async-call serviceA and then serviceB, take the results of serviceA and serviceB, and then call serviceC. Then, based on the results of call C, call D or E and then return the results to the original caller. Calls to A, B, C, D, and E are all async calls, and none should take longer than 10 seconds. If they do, then return a timeout to the original caller. The whole async call sequence should time out in 20 seconds if it does not complete and should also check for circuit breakers and provide back pressure feedback so the system does not have cascading failures. Learn more in this session.
Reactive Java: Promises and Streams with Reakt (JavaOne talk 2016)Rick Hightower
see labs at https://github.com/advantageous/j1-talks-2016
Import based on PDF. This is from our JavaOne Talk 2016 on Reakt, reactive Java programming with promises, circuit breakers, and streams. Reakt is a reactive Java lib that provides promises, streams, and a reactor to handle asynchronous call coordination. It was influenced by the design of promises in ES6. You want to async-call serviceA and then serviceB, take the results of serviceA and serviceB, and then call serviceC. Then, based on the results of call C, call D or E and then return the results to the original caller. Calls to A, B, C, D, and E are all async calls, and none should take longer than 10 seconds. If they do, then return a timeout to the original caller. The whole async call sequence should time out in 20 seconds if it does not complete and should also check for circuit breakers and provide back pressure feedback so the system does not have cascading failures. Learn more in this session.
The word "Reactive" can be confusing. As the founder of the Reactive Amsterdam meetup, I can tell there are two main topics here: Functional Reactive Programming (here with reference to Android) and "Reactive" in the sense of the Reactive Manifesto.
[DevDay2019] Lean UX - By Bryant Castro, Bryant Castro at WizelineDevDay.org
Lean UX helps teams build the minimal product necessary to validate risky assumptions and minimize the time to market with the right product. On this lecture, Lean UX principles and its value to the product cycle will be introduced. Also, the methods and tools that will help you get feedback from users and learn rapidly will be discussed. This session is geared towards those who are interested in UX but have no much experience, those looking for new methods to improve their current product processes, and anyone interested in design, business, and user centered design.
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UX Design is on a radical rise. The most successful companies like Google or Uber know that great UX is no longer a nice-to-have but a key business driver. Szilard Toth (CTO e·pilot) and Nicolas Python (Head of Design KLARA) talk about their own experience of UX Design in modern engineering environments. Whether you're a business leader or an engineer, learn why you'll lose without UX Design.
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Flow Base Programming with Node-RED and Functional Reactive Programming with ...Sven Beauprez
To overcome callback hell in node.js, the presentation gives an overview of the advantages of using Flow Based Programming with node-RED and Functional Reactive Programming with Bacon.js.
The presentation was made for IoTBE (Internet of Things Belgium) user group to prove that node.js can actually be used for the Internet of Things (IoT).
Stream Processing with CompletableFuture and Flow in Java 9Trayan Iliev
Stream based data / event / message processing becomes preferred way of achieving interoperability and real-time communication in distributed SOA / microservice / database architectures.
Beside lambdas, Java 8 introduced two new APIs explicitly dealing with stream data processing:
- Stream - which is PULL-based and easily parallelizable;
- CompletableFuture / CompletionStage - which allow composition of PUSH-based, non-blocking, asynchronous data processing pipelines.
Java 9 will provide further support for stream-based data-processing by extending the CompletableFuture with additional functionality – support for delays and timeouts, better support for subclassing, and new utility methods.
More, Java 9 provides new java.util.concurrent.Flow API implementing Reactive Streams specification that enables reactive programming and interoperability with libraries like Reactor, RxJava, RabbitMQ, Vert.x, Ratpack, and Akka.
The presentation will discuss the novelties in Java 8 and Java 9 supporting stream data processing, describing the APIs, models and practical details of asynchronous pipeline implementation, error handling, multithreaded execution, asyncronous REST service implementation, interoperability with existing libraries.
There are provided demo examples (code on GitHub) using Completable Future and Flow with:
- JAX-RS 2.1 AsyncResponse, and more importantly unit-testing the async REST service method implementations;
- CDI 2.0 asynchronous observers (fireAsync / @ObservesAsync);
An introduction to reactive programming concepts and basics. I aim here to show what's reactive programming, why it's used and show some frameworks and benchmarks that support it.
ClojureScript - Making Front-End development Fun again - John Stevenson - Cod...Codemotion
Front-end development has an amazing assortment of libraries and tools, yet it can seem very complex and doest seem much fun. So we'll live code a ClojureScript application (with a bit of help from Git) and show how development doesn't have to be complex or slow. Through live evaluation, we can build a reactive, functional application. Why not take a look at a well designed language that uses modern functional & reactive concepts for building Front-End apps. You are going to have to trans-pile anyway, so why not use a language, libraries and tooling that is bursting with fun to use.
Reactive Java: Promises and Streams with Reakt (JavaOne Talk 2016)Rick Hightower
see labs at https://github.com/advantageous/j1-talks-2016
Import based on PPT so there is more notes. This is from our JavaOne Talk 2016 on Reakt, reactive Java programming with promises, circuit breakers, and streams. Reakt is a reactive Java lib that provides promises, streams, and a reactor to handle asynchronous call coordination. It was influenced by the design of promises in ES6. You want to async-call serviceA and then serviceB, take the results of serviceA and serviceB, and then call serviceC. Then, based on the results of call C, call D or E and then return the results to the original caller. Calls to A, B, C, D, and E are all async calls, and none should take longer than 10 seconds. If they do, then return a timeout to the original caller. The whole async call sequence should time out in 20 seconds if it does not complete and should also check for circuit breakers and provide back pressure feedback so the system does not have cascading failures. Learn more in this session.
Reactive Java: Promises and Streams with Reakt (JavaOne talk 2016)Rick Hightower
see labs at https://github.com/advantageous/j1-talks-2016
Import based on PDF. This is from our JavaOne Talk 2016 on Reakt, reactive Java programming with promises, circuit breakers, and streams. Reakt is a reactive Java lib that provides promises, streams, and a reactor to handle asynchronous call coordination. It was influenced by the design of promises in ES6. You want to async-call serviceA and then serviceB, take the results of serviceA and serviceB, and then call serviceC. Then, based on the results of call C, call D or E and then return the results to the original caller. Calls to A, B, C, D, and E are all async calls, and none should take longer than 10 seconds. If they do, then return a timeout to the original caller. The whole async call sequence should time out in 20 seconds if it does not complete and should also check for circuit breakers and provide back pressure feedback so the system does not have cascading failures. Learn more in this session.
The word "Reactive" can be confusing. As the founder of the Reactive Amsterdam meetup, I can tell there are two main topics here: Functional Reactive Programming (here with reference to Android) and "Reactive" in the sense of the Reactive Manifesto.
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Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
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My slides at Nordic Testing Days 6.6.2024
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Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
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Neha Bajwa, Vice President of Product Marketing, Neo4j
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
15. Reactive Extension - Observables
15
// From primitive data type
Rx.Observable.just(12)
// From array
Rx.Observable.from([1, 2, 3, 4])
// From interval
Rx.Observable.interval(10)
// From promise
Rx.Observable.fromPromise(promise)
// From DOM events
Rx.Observable.fromEvent(
document.getElementById('keyword'),
'keyup'
)