Sometimes we want to keep or collect errors for later examination. We’ll use Cats to help us pick the story we want to tell when handling errors; accumulated errors or first error wins. Monads will be included!
non-strict functions, bottom and scala by-name parametersPhilip Schwarz
Download for perfect quality.
Non strict functions, bottom and scala by-name parameters - ‘a close look’, through the work of Runar Bjarnason, Paul Chiusano, Martin Odersky, Bill Venners, Lex Spoon, Alvin Alexander, Mark Lewis and Aleksandar Prokopec.
This document discusses side effects in Scala programs and proposes an IO monad to encapsulate side effects. Some key points:
- It introduces an IO trait that represents computations that may produce side effects. IO values can be combined using flatMap and map to sequence computations.
- Common side effects like printing are modeled as IO values. An interpreter is defined to actually run IO actions by pattern matching on the data structure.
- The document shows how the IO monad allows separating pure logic from effects, running effects in order, and capturing external interactions like input/output as data. This provides better control over side effects.
- Later sections discuss implementing the interpreter recursively to properly sequence nested effects. The
With my simple implementation I wanted to demonstrate the basic ideas of th IO Monad.
My impl of the IO Monad is just a feasibility study, not production code!
When coding my impl of IO I was very much inspired by cats.effect.IO and monix.eval.Task which I studied at that time. Both are implementions of the IO Monad.
The API of my IO is very similar to the basics of Monix Task. This IO implementation also helped me to understand the IO Monad (of cats-effect) and Monix Task.
Interop with Future is also supported. You can convert IO to a Future. Vice versa you can convert a Future to an IO.
The development of my impl can be followed step by step in the code files in package iomonad.
Composing an App with Free Monads (using Cats)Hermann Hueck
In this talk I will explain what Free Monads are and how to use them (using the Cats implementation).
After having shown the basics I build a small app by composing several
Free Monads to a small program.
I discuss the pros and cons of this technique.
Finally I will demonstrate how to avoid some boilerplate with the FreeK library.
This document discusses type classes in Scala and Haskell. It provides a recap of implicits in Scala, including implicit parameters, conversions, and views. It then introduces type class concepts and examples from the Scala standard library like Ordering and Numeric. It explains how to define a Printable type class and instances for types like Int and Cat. It discusses improved designs using companion objects and implicit classes. Finally, it covers where to define type class instances, such as for standard types in the companion object and domain types in their package.
Free Monads are a powerful technique that can separate the representation of programs from the messy details of how they get run.
I'll go into the details of how they work, how to use them for fun and profit in your own code, and demonstrate a live Free Monad-driven tank game.
Supporting code at https://github.com/kenbot/free
Functional Programming 101 with Scala and ZIO @FunctionalWorldJorge Vásquez
This document provides an overview of functional programming concepts and the ZIO library. It discusses the characteristics of pure functions, including being total, deterministic, and having no side effects. It contrasts functional programming with object-oriented programming. Benefits of the functional paradigm discussed include local reasoning, referential transparency, conciseness, easier testing, and support for parallel programming. The document then introduces the ZIO library, describing how it allows building resilient, asynchronous, and efficient applications using functional principles. It outlines ZIO's data type and how it represents effects.
For decades, the Functor, Monoid, and Foldable type class hierarchies have dominated functional programming. Implemented in libraries like Scalaz and Cats, these type classes have an ancient origin in Haskell, and they have repeatedly proven useful for advanced functional programmers, who use them to maximize code reuse and increase code correctness.
Yet, as these type classes have been copied into Scala and aged, there is a growing awareness of their drawbacks, ranging from being difficult to teach to weird operators that don’t make sense in Scala (ap from Applicative), to overlapping and lawless type classes (Semigroupal), to a complete inability to abstract over data types that possess related structure (such as isomorphic applicatives).
In this presentation, John A. De Goes introduces a new Scala library with a completely different factoring of functional type classes—one which throws literally everything away and starts from a clean slate. In this new factoring, type classes leverage Scala’s strengths, including variance and modularity. Pieces fit together cleanly and uniformly, and in a way that satisfies existing use cases, but enables new ones never before possible. Finally, type classes are named, organized, and described in a way that makes teaching them easier, without compromising on algebraic principles.
If you’ve ever thought functional type classes were too impractical or too confusing or too restrictive, now’s your chance to get a fresh perspective on a library that just might make understanding functional programming easier than ever before!
Monoids - Part 1 - with examples using Scalaz and CatsPhilip Schwarz
A monoid is an algebraic structure consisting of a set and a binary operation that is associative and has an identity element. Some key properties of monoids include:
1) A monoid consists of a type A, a binary operation op that combines two values of type A, and a zero value that acts as an identity.
2) The binary operation must be associative, meaning op(op(x,y),z) = op(x,op(y,z)).
3) The zero value must satisfy the identity laws: op(x, zero) = x and op(zero, x) = x.
4) Common examples of monoids include string concatenation
Here I link up up some very useful material by Robert Norris (@tpolecat) and Martin Odersky (@odersky) to introduce Monad laws and reinforce the importance of checking the laws. E.g. while Option satisfies the laws, Try is not a lawful Monad: it trades the left identity law for the bullet-proof principle.
* ERRATA *
1) on the first slide the Kleisli composition signature appears three times, but one of the occurrences is incorrect in that it is (>=>)::(a->mb)->(b->mb)->(a->mc) whereas is should be (>=>)::(a->mb)->(b->mc)->(a->mc) - thank you Jules Ivanic.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
Left and Right Folds- Comparison of a mathematical definition and a programm...Philip Schwarz
We compare typical definitions of the left and right fold functions, with their mathematical definitions in Sergei Winitzki’s upcoming book: The Science of Functional Programming.
Errata:
Slide 13: "The way 𝑓𝑜𝑙𝑑𝑙 does it is by associating to the right" - should, of course ,end in "to the left".
Functional Domain Modeling - The ZIO 2 WayDebasish Ghosh
Principled way to design and implement functional domain models using some of the patterns of domain driven design. DDD, as the name suggests, is focused towards the domain model and the patterns of architecture that it encourages are also based on how we think of interactions amongst the basic abstractions of the domain. Of course the primary goal of the talk is to discuss how Scala and Zio 2 can be a potent combination in realizing the implementation of such models. This is not a talk on FP, the focus will be on how to structure and modularise an application based on some of the patterns of DDD.
Http4s, Doobie and Circe: The Functional Web StackGaryCoady
Http4s, Doobie and Circe together form a nice platform for building web services. This presentations provides an introduction to using them to build your own service.
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit – Haskell and...Philip Schwarz
First see the problem solved using the List monad and a Scala for comprehension.
Then see the Scala program translated into Haskell, both using a do expressions and using a List comprehension.
Understand how the Scala for comprehension is desugared, and what role the withFilter function plays.
Also understand how the Haskell do expressions and List comprehension are desugared, and what role the guard function plays.
Scala code for Part 1: https://github.com/philipschwarz/n-queens-combinatorial-problem-scala-part-1
Errata: on slide 30, the resulting lists should be Haskell ones rather than Scala ones.
Introduction to programming with ZIO functional effectsJorge Vásquez
This document provides an introduction to functional programming concepts and the ZIO library. It discusses programming with pure functions, handling side effects through effect types, and the ZIO data type for describing side effects in a type-safe way. It then introduces some common ZIO type aliases like Task, UIO, RIO, IO and URIO. The document concludes by mentioning a live coding example of building a Hangman game using ZIO.
download for better quality - Learn about the sequence and traverse functions
through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala
ZIO-Direct allows direct style programming with ZIO. This library provides a *syntactic sugar* that is more powerful than for-comprehensions as well as more natural to use. Simply add the `.run` suffix to any ZIO effect in order to retrieve it's value.
Nat, List and Option Monoids -from scratch -Combining and Folding -an examplePhilip Schwarz
Nat, List and Option Monoids, from scratch. Combining and Folding: an example.
Code: https://github.com/philipschwarz/nat-list-and-option-monoids-from-scratch-combining-and-folding-an-example
Domain Modeling Made Functional (KanDDDinsky 2019)Scott Wlaschin
(video at https://fsharpforfunandprofit.com/ddd/)
Statically typed functional programming languages encourage a very different way of thinking about types. The type system is your friend, not an annoyance, and can be used in many ways that might not be familiar to OO programmers. Types can be used to represent the domain in a fine-grained, self documenting way. And in many cases, types can even be used to encode business rules so that you literally cannot create incorrect code. You can then use the static type checking almost as an instant unit test — making sure that your code is correct at compile time. In this talk, we'll look at some of the ways you can use types as part of a domain driven design process, with some simple real world examples in F#. No jargon, no maths, and no prior F# experience necessary.
Effective testing with Pytest focuses on using Pytest to its full potential. Key aspects include using fixtures like monkeypatch and mocker to control dependencies, libraries like factoryboy and faker to generate test data, and freezegun to control time. Tests should be fast, readable, and maintainable by applying best practices for organization, parametrization, markers, and comparing results in an effective manner.
Map(), flatmap() and reduce() are your new best friends: simpler collections,...Chris Richardson
This document summarizes a presentation about functional programming and how functions like map(), flatMap(), and reduce() can simplify collection processing, concurrency, and big data problems. The presentation introduces functional programming concepts and how languages like Java 8 have adopted these with features like lambda expressions and streams. It provides examples of how to use streams to map, filter, and reduce collections in a more declarative way compared to imperative for loops. It also discusses how functions and futures can help simplify concurrent operations by allowing asynchronous work to be expressed more clearly.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
This document summarizes a presentation on Scala given by Jonas Boner. It shows how to build a simple chat application in 30 lines of code using Lift framework. It then discusses Scala as a scalable, pragmatic language that blends object-oriented and functional programming. Traits allow for rich and extensible abstractions. Pattern matching, immutable data structures, and functions as first-class values enable a functional style. Tools like SBT and frameworks like Lift make Scala productive for real-world use.
For decades, the Functor, Monoid, and Foldable type class hierarchies have dominated functional programming. Implemented in libraries like Scalaz and Cats, these type classes have an ancient origin in Haskell, and they have repeatedly proven useful for advanced functional programmers, who use them to maximize code reuse and increase code correctness.
Yet, as these type classes have been copied into Scala and aged, there is a growing awareness of their drawbacks, ranging from being difficult to teach to weird operators that don’t make sense in Scala (ap from Applicative), to overlapping and lawless type classes (Semigroupal), to a complete inability to abstract over data types that possess related structure (such as isomorphic applicatives).
In this presentation, John A. De Goes introduces a new Scala library with a completely different factoring of functional type classes—one which throws literally everything away and starts from a clean slate. In this new factoring, type classes leverage Scala’s strengths, including variance and modularity. Pieces fit together cleanly and uniformly, and in a way that satisfies existing use cases, but enables new ones never before possible. Finally, type classes are named, organized, and described in a way that makes teaching them easier, without compromising on algebraic principles.
If you’ve ever thought functional type classes were too impractical or too confusing or too restrictive, now’s your chance to get a fresh perspective on a library that just might make understanding functional programming easier than ever before!
Monoids - Part 1 - with examples using Scalaz and CatsPhilip Schwarz
A monoid is an algebraic structure consisting of a set and a binary operation that is associative and has an identity element. Some key properties of monoids include:
1) A monoid consists of a type A, a binary operation op that combines two values of type A, and a zero value that acts as an identity.
2) The binary operation must be associative, meaning op(op(x,y),z) = op(x,op(y,z)).
3) The zero value must satisfy the identity laws: op(x, zero) = x and op(zero, x) = x.
4) Common examples of monoids include string concatenation
Here I link up up some very useful material by Robert Norris (@tpolecat) and Martin Odersky (@odersky) to introduce Monad laws and reinforce the importance of checking the laws. E.g. while Option satisfies the laws, Try is not a lawful Monad: it trades the left identity law for the bullet-proof principle.
* ERRATA *
1) on the first slide the Kleisli composition signature appears three times, but one of the occurrences is incorrect in that it is (>=>)::(a->mb)->(b->mb)->(a->mc) whereas is should be (>=>)::(a->mb)->(b->mc)->(a->mc) - thank you Jules Ivanic.
This document is useful when use with Video session I have recorded today with execution, This is document no. 2 of course "Introduction of Data Science using Python". Which is a prerequisite of Artificial Intelligence course at Ethans Tech.
Disclaimer: Some of the Images and content have been taken from Multiple online sources and this presentation is intended only for Knowledge Sharing
Left and Right Folds- Comparison of a mathematical definition and a programm...Philip Schwarz
We compare typical definitions of the left and right fold functions, with their mathematical definitions in Sergei Winitzki’s upcoming book: The Science of Functional Programming.
Errata:
Slide 13: "The way 𝑓𝑜𝑙𝑑𝑙 does it is by associating to the right" - should, of course ,end in "to the left".
Functional Domain Modeling - The ZIO 2 WayDebasish Ghosh
Principled way to design and implement functional domain models using some of the patterns of domain driven design. DDD, as the name suggests, is focused towards the domain model and the patterns of architecture that it encourages are also based on how we think of interactions amongst the basic abstractions of the domain. Of course the primary goal of the talk is to discuss how Scala and Zio 2 can be a potent combination in realizing the implementation of such models. This is not a talk on FP, the focus will be on how to structure and modularise an application based on some of the patterns of DDD.
Http4s, Doobie and Circe: The Functional Web StackGaryCoady
Http4s, Doobie and Circe together form a nice platform for building web services. This presentations provides an introduction to using them to build your own service.
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit – Haskell and...Philip Schwarz
First see the problem solved using the List monad and a Scala for comprehension.
Then see the Scala program translated into Haskell, both using a do expressions and using a List comprehension.
Understand how the Scala for comprehension is desugared, and what role the withFilter function plays.
Also understand how the Haskell do expressions and List comprehension are desugared, and what role the guard function plays.
Scala code for Part 1: https://github.com/philipschwarz/n-queens-combinatorial-problem-scala-part-1
Errata: on slide 30, the resulting lists should be Haskell ones rather than Scala ones.
Introduction to programming with ZIO functional effectsJorge Vásquez
This document provides an introduction to functional programming concepts and the ZIO library. It discusses programming with pure functions, handling side effects through effect types, and the ZIO data type for describing side effects in a type-safe way. It then introduces some common ZIO type aliases like Task, UIO, RIO, IO and URIO. The document concludes by mentioning a live coding example of building a Hangman game using ZIO.
download for better quality - Learn about the sequence and traverse functions
through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala
ZIO-Direct allows direct style programming with ZIO. This library provides a *syntactic sugar* that is more powerful than for-comprehensions as well as more natural to use. Simply add the `.run` suffix to any ZIO effect in order to retrieve it's value.
Nat, List and Option Monoids -from scratch -Combining and Folding -an examplePhilip Schwarz
Nat, List and Option Monoids, from scratch. Combining and Folding: an example.
Code: https://github.com/philipschwarz/nat-list-and-option-monoids-from-scratch-combining-and-folding-an-example
Domain Modeling Made Functional (KanDDDinsky 2019)Scott Wlaschin
(video at https://fsharpforfunandprofit.com/ddd/)
Statically typed functional programming languages encourage a very different way of thinking about types. The type system is your friend, not an annoyance, and can be used in many ways that might not be familiar to OO programmers. Types can be used to represent the domain in a fine-grained, self documenting way. And in many cases, types can even be used to encode business rules so that you literally cannot create incorrect code. You can then use the static type checking almost as an instant unit test — making sure that your code is correct at compile time. In this talk, we'll look at some of the ways you can use types as part of a domain driven design process, with some simple real world examples in F#. No jargon, no maths, and no prior F# experience necessary.
Effective testing with Pytest focuses on using Pytest to its full potential. Key aspects include using fixtures like monkeypatch and mocker to control dependencies, libraries like factoryboy and faker to generate test data, and freezegun to control time. Tests should be fast, readable, and maintainable by applying best practices for organization, parametrization, markers, and comparing results in an effective manner.
Map(), flatmap() and reduce() are your new best friends: simpler collections,...Chris Richardson
This document summarizes a presentation about functional programming and how functions like map(), flatMap(), and reduce() can simplify collection processing, concurrency, and big data problems. The presentation introduces functional programming concepts and how languages like Java 8 have adopted these with features like lambda expressions and streams. It provides examples of how to use streams to map, filter, and reduce collections in a more declarative way compared to imperative for loops. It also discusses how functions and futures can help simplify concurrent operations by allowing asynchronous work to be expressed more clearly.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
This document summarizes a presentation on Scala given by Jonas Boner. It shows how to build a simple chat application in 30 lines of code using Lift framework. It then discusses Scala as a scalable, pragmatic language that blends object-oriented and functional programming. Traits allow for rich and extensible abstractions. Pattern matching, immutable data structures, and functions as first-class values enable a functional style. Tools like SBT and frameworks like Lift make Scala productive for real-world use.
The document summarizes Scala, a functional programming language that runs on the Java Virtual Machine (JVM). It discusses Scala's core features like being object-oriented, type inference, and support for functional programming with immutable data structures and passing functions as parameters. It also provides examples of using Scala collections like List and Array, and functions like map, filter, flatMap, and foldLeft/reduceLeft. Finally, it demonstrates using Scala for domain-specific languages and shows examples of defining DSLs for querying and generating JavaScript.
Most developers will be familiar with lex, flex, yacc, bison, ANTLR, and other tools to generate parsers for use inside their own code. Erlang, the concurrent functional programming language, has its own pair, leex and yecc, for accomplishing most complicated text-processing tasks. This talk is about how the seemingly simple prospect of parsing text turned into a new parser toolkit for Erlang, and why functional programming makes parsing fun and awesome.
At the beggining, we had RDDs. A distributed Scala collection! Then, the DataFrame API showed up. It brought with it tons of datasource implementations and a query optimizer. But... where are our types? Are we really referring to a column with a string and then casting it? In Scala? This meant more runtime errors and made the code harder to refactor. Then, we got Datasets. We have types again! The bad thing is that lambdas kill some of the performance we can achieve with dataframes. Also, runtime errors don't completely disappear.
The Frameless library tries to solve these problems so we can get all the performance, keep our types and reduce the runtime errors. This talk explains the pros and cons of the library, and also dives deeper into some implementation details to make it possible.
R is an open source statistical computing platform that is rapidly growing in popularity within academia. It allows for statistical analysis and data visualization. The document provides an introduction to basic R functions and syntax for assigning values, working with data frames, filtering data, plotting, and connecting to databases. More advanced techniques demonstrated include decision trees, random forests, and other data mining algorithms.
An overview of two types of graph databases: property databases and knowledge/RDF databases, together with their dominant respective query languages, Cypher and SPARQL. Also a quick look at some property DB frameworks, including TinkerPop and its query language, Gremlin.
This document discusses various Ruby array and string methods like capitalize, each_char, map, sample, shuffle, zip, and more. Code snippets demonstrate how to use these methods on arrays and strings in Ruby. The document also discusses using Node.js and IRB to test Ruby code snippets and the potential to write tests for Ruby code using a BDD style.
The document discusses various primitive data types including numeric, boolean, character, and string types. It describes integer types like byte, short, int that can store negative numbers using two's complement. Floating point types are represented as fractions and exponents. Boolean types are either true or false. Character types are stored as numeric codes. String types can have static, limited dynamic, or fully dynamic lengths. User-defined types like enumerations and subranges are also covered. The document also discusses array types including their initialization, operations, and implementation using row-major and column-major ordering. Associative arrays are described as unordered collections indexed by keys. Record and union types are summarized.
pragmaticrealworldscalajfokus2009-1233251076441384-2.pdfHiroshi Ono
The document discusses Scala and functional programming concepts. It provides examples of building a chat application in 30 lines of code using Lift, defining case classes and actors for messages. It summarizes that Scala is a pragmatically oriented, statically typed language that runs on the JVM and has a unique blend of object-oriented and functional programming. Functional programming concepts like immutable data structures, functions as first-class values, and for-comprehensions are demonstrated with examples in Scala.
pragmaticrealworldscalajfokus2009-1233251076441384-2.pdfHiroshi Ono
This document discusses Scala and its features. It provides an example of building a chat application in 30 lines of code using Lift framework. It also demonstrates pattern matching, functional data structures like lists and tuples, for comprehensions, and common Scala tools and frameworks. The document promotes Scala as a pragmatic and scalable language that blends object-oriented and functional programming. It encourages learning more about Scala.
pragmaticrealworldscalajfokus2009-1233251076441384-2.pdfHiroshi Ono
The document discusses Scala and functional programming concepts. It provides examples of building a chat application in 30 lines of code using Lift, defining messages as case classes, and implementing a chat server and comet component. It then summarizes that Scala is a pragmatically-oriented, statically typed language that runs on the JVM and provides a unique blend of object-oriented and functional programming. Traits allow for code reuse and multiple class inheritances. Functional programming concepts like immutable data structures, higher-order functions, and for-comprehensions are discussed.
pragmaticrealworldscalajfokus2009-1233251076441384-2.pdfHiroshi Ono
The document discusses Scala and functional programming concepts. It provides examples of building a chat application in 30 lines of code using Lift, defining messages as case classes, and implementing a chat server and comet component. It then summarizes that Scala is a pragmatically-oriented, statically typed language that runs on the JVM and provides a unique blend of object-oriented and functional programming. Traits allow for static and dynamic mixin-based composition. Functional programming concepts like immutable data structures, higher-order functions, and for-comprehensions are discussed.
Many developers will be familiar with lex, flex, yacc, bison, ANTLR, and other related tools to generate parsers for use inside their own code. For recognizing computer-friendly languages, however, context-free grammars and their parser-generators leave a few things to be desired. This is about how the seemingly simple prospect of parsing some text turned into a new parser toolkit for Erlang, and why functional programming makes parsing fun and awesome
This document provides an overview of the Python programming language. It covers topics such as syntax, types and objects, operators and expressions, functions, classes and object-oriented programming, modules and packages, input/output, and the Python execution environment. It also discusses generators and iterators versus regular functions, namespaces and scopes, and classes in Python.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
Python 101++: Let's Get Down to Business!Paige Bailey
You've started the Codecademy and Coursera courses; you've thumbed through Zed Shaw's "Learn Python the Hard Way"; and now you're itching to see what Python can help you do. This is the workshop for you!
Here's the breakdown: we're going to be taking you on a whirlwind tour of Python's capabilities. By the end of the workshop, you should be able to easily follow any of the widely available Python courses on the internet, and have a grasp on some of the more complex aspects of the language.
Please don't forget to bring your personal laptop!
Audience: This course is aimed at those who already have some basic programming experience, either in Python or in another high level programming language (such as C/C++, Fortran, Java, Ruby, Perl, or Visual Basic). If you're an absolute beginner -- new to Python, and new to programming in general -- make sure to check out the "Python 101" workshop!
When you write unit tests for your projects, there’s a fair chance that you do so by following the classical « Given-When-Then » paradigm, in which you set some input data, execute the code you’re testing, and finally assert that its outcome is indeed the one you expected.
While this approach is perfectly sound, it does suffer one downside: your program will only be tested on the static input data defined in your tests, and there is no real guarantee that this data does cover all edge cases. This can be especially problematic for SDK developers, who, by definition, have a very hard time anticipating all the different situations in which their code will be used.
To improve on this issue, another approach exists, and it is called property-based testing. The idea behind it is very simple: you write your tests by defining properties that must always be true for your program. For example, « an array reversed twice is always equal to itself ». The testing framework will then generate random input values and test wether the property holds or not. And, as you can imagine, this approach is extremely good at narrowing down on overlooked edge cases.
In Swift, we are lucky enough to already have a full-fledged implementation called SwiftCheck, that enables property-based testing (https://github.com/typelift/SwiftCheck). The goal of this talk is thus to explain how property-based testing can be a powerful addition to a testing suite, and give actual and actionable examples of how it can be added to a project using SwiftCheck.
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185
GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
https://arxiv.org/abs/2501.16495
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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri
AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions
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Adobe Lightroom Classic is a desktop-based software application for editing and managing digital photos. It focuses on providing users with a powerful and comprehensive set of tools for organizing, editing, and processing their images on their computer. Unlike the newer Lightroom, which is cloud-based, Lightroom Classic stores photos locally on your computer and offers a more traditional workflow for professional photographers.
Here's a more detailed breakdown:
Key Features and Functions:
Organization:
Lightroom Classic provides robust tools for organizing your photos, including creating collections, using keywords, flags, and color labels.
Editing:
It offers a wide range of editing tools for making adjustments to color, tone, and more.
Processing:
Lightroom Classic can process RAW files, allowing for significant adjustments and fine-tuning of images.
Desktop-Focused:
The application is designed to be used on a computer, with the original photos stored locally on the hard drive.
Non-Destructive Editing:
Edits are applied to the original photos in a non-destructive way, meaning the original files remain untouched.
Key Differences from Lightroom (Cloud-Based):
Storage Location:
Lightroom Classic stores photos locally on your computer, while Lightroom stores them in the cloud.
Workflow:
Lightroom Classic is designed for a desktop workflow, while Lightroom is designed for a cloud-based workflow.
Connectivity:
Lightroom Classic can be used offline, while Lightroom requires an internet connection to sync and access photos.
Organization:
Lightroom Classic offers more advanced organization features like Collections and Keywords.
Who is it for?
Professional Photographers:
PCMag notes that Lightroom Classic is a popular choice among professional photographers who need the flexibility and control of a desktop-based application.
Users with Large Collections:
Those with extensive photo collections may prefer Lightroom Classic's local storage and robust organization features.
Users who prefer a traditional workflow:
Users who prefer a more traditional desktop workflow, with their original photos stored on their computer, will find Lightroom Classic a good fit.
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Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
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After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations.
Visual Effects:
.
It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements.
Video Compositing:
.
After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual.
Animation:
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It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects.
Integration with Adobe Creative Cloud:
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After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro.
Post-Production Tool:
.
After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
Not So Common Memory Leaks in Java WebinarTier1 app
This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers
Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
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Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev
If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
14. AA => => FF[[BB]]
case class AncientRecord(
age: String,
name: String,
rateOfIncome: String
)
case class Citizen(
age: Int,
name: String,
rateOfIncome: Double
)
// fate of record not guaranteed, via F
def parseRecord(rec: AncientRecord): F[Citizen]
16. FOR COMPREHENSIONFOR COMPREHENSION
def parseRecord(rec: AncientRecord): Option[Citizen] =
for {
age <- parseAge(rec.age)
name <- parseName(rec.name)
rate <- parseRate(rec.rate)
} yield Citizen(age, name, rate)
17. PARSING WITH OPTIONPARSING WITH OPTION
def parseRecord(rec: AncientRecord): Option[Citizen] =
for {
age <- parseAge(rec.age) // Some[Int]
name <- parseName(rec.name) // Some[String]
rate <- parseRate(rec.rate) // Some[Double]
} yield
Citizen(age, name, rate) // Some[Citizen]
18. NOT ALL AVAILABLENOT ALL AVAILABLE
def parseRecord(rec: AncientRecord): Option[Citizen] =
for {
age <- parseAge(rec.age) // Some[Int]
name <- parseName(rec.name) // None
rate <- parseRate(rec.rate) // (does not run)
} yield
Citizen(age, name, rate) // None
25. PARSING WITH TRYPARSING WITH TRY
def parseRecord(rec: AncientRecord): Try[Citizen] =
for {
age <- parseAge(rec.age) // Success[Int]
name <- parseName(rec.name) // Failure[String]
rate <- parseRate(rec.rate) // (does not run)
} yield
Citizen(age, name, rate) // Failure[Citizen]
27. Name Happy path Sad path
Option[_] Some[_] None
Try[_] Success[_] Failure[_]
30. ERROR ADTERROR ADT
sealed trait ParsingErr
case class UnreadableAge (s: String) extends ParsingErr
case class UnreadableName(s: String) extends ParsingErr
case class UnreadableRate(s: String) extends ParsingErr
57. PARSING WITH EITHERPARSING WITH EITHER
def parseRecord(rec: AncientRecord): Either[ParsingErr, Citizen] =
for {
age <- parseAge(rec.age) // Left[ParsingErr, Int]
name <- parseName(rec.name) // (does not run)
rate <- parseRate(rec.rate) // (does not run)
} yield
Citizen(age, name, rate) // only reports first error
69. VALIDATED AND EITHERVALIDATED AND EITHER
Used for capturing errors as data
"Exclusive disjunction"
(payload A or error E but never both)
Bifunctor over A and E