This document introduces monad transformers and illustrates their usage in Scala. It defines the Reader and IO monads and shows how they can be composed using the ReaderT transformer. This allows defining computations that both read configuration values and perform side effects. The document recommends using type classes like MonadReader and MonadBase to define computations over monad stacks in a parametrically polymorphic way. It provides examples of defining and running such computations over a ReaderT[IO, ?] stack.
This talk introduces type classes for Scala users.
I tried to build the conversation with fewer assumptions, so beginners wouldn't be overwhelmed. I also tried to keep objective in my presentation, but my advocacy for prinicipled use of implicits and type classes is definitely in there.
The source code is on GitHub and compiles with Emacs+org-beamer-mode: https://github.com/shajra/shajra-presentations/blob/master/scala-typeclasses.org
Explains the basic concepts of Category Theory, useful terminology to help understand the literature, and why it's so relevant to software engineering.
A very basic introductory presentation on Category Theory. Focus was on directly relating category theory to programming in a functional language. Scala was the language of choice for the presentation.
Basic coverage of Functors. Monads to follow in later talk.
This talk introduces type classes for Scala users.
I tried to build the conversation with fewer assumptions, so beginners wouldn't be overwhelmed. I also tried to keep objective in my presentation, but my advocacy for prinicipled use of implicits and type classes is definitely in there.
The source code is on GitHub and compiles with Emacs+org-beamer-mode: https://github.com/shajra/shajra-presentations/blob/master/scala-typeclasses.org
Explains the basic concepts of Category Theory, useful terminology to help understand the literature, and why it's so relevant to software engineering.
A very basic introductory presentation on Category Theory. Focus was on directly relating category theory to programming in a functional language. Scala was the language of choice for the presentation.
Basic coverage of Functors. Monads to follow in later talk.
Functional programming in kotlin with Arrow [Sunnytech 2018]Emmanuel Nhan
Ā
Slides from my talk about Kotlin & Functional programming with Arrow which I gave at Sunny Tech 2018 (http://sunny-tech.io)
It showcases how Kotlin is a good fit for functional programming, thanks to Arrow.
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, and others (Martin Odersky, Derek Wyatt, Adelbert Chang)
Introduction to Category Theory for software engineersPranav Prakash
Ā
An Introduction to Category Theory for Software Engineers. By Dr Steve Easterbrook
Associate Professor,
Dept of Computer Science,
University of Toronto
sme@cs.toronto.edu
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, and Sam Halliday, author of "Functional Programming for Mortals with Scalaz" https://leanpub.com/fpmortals
19. Data Structures and Algorithm ComplexityIntro C# Book
Ā
In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to use. We will explain how to choose between data structures like hash-tables, arrays, dynamic arrays and sets implemented by hash-tables or balanced trees. Almost all of these structures are implemented as part of NET Framework, so to be able to write efficient and reliable code we have to learn to apply the most appropriate structures for each situation.
Download for better quality.
Functor Composition - including (starting from) the definition in Bartosz Milewski's Category Theory book. https://github.com/hmemcpy/milewski-ctfp-pdf
What would be a good answer to the question of what functors are? To help formulating a reasonably well rounded answer, I have made some slides capturing what I considere particularly useful aspects of the following sourcesā exposition of the concept of Functor:
* Bartosz Milewskiās Scala eXchange 2017 Keynote, The Maths Behind Types
*Paul Chiusano and Runar Bjarnasonās book, Functional Programming in Scala
* Debasish Ghoshās book - Functional and Reactive Domain Modeling
* Christopher Allen, and Julie Moronuki's book - Haskell Programming from First Principles
Abstract: This workshop teaches basic algorithms in whiteboarding interviews. All the code examples are in Python and the course has dual purpose teaching basic Python programming.
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Download for better quality.
Monads do not Compose. Not in a generic way - There is no general way of composing monads.
A comment from RĆŗnar Bjarnason, coauthor of FP in Scala: "They do compose in a different generic way. For any two monads F and G we can take the coproduct which is roughly Free of Either F or G (up to normalization)".
Another comment from Sergei Winitzki (which caused me to upload https://www.slideshare.net/pjschwarz/addendum-to-monads-do-not-compose): "It is a mistake to think that a traversable monad can be composed with another monad. It is true that, given `Traversable`, you can implement the monad's methods (pure and flatMap) for the composition with another monad (as in your slides 21 to 26), but this is a deceptive appearance. The laws of the `Traversable` typeclass are far insufficient to guarantee the laws of the resulting composed monad. The only traversable monads that work correctly are Option, Either, and Writer. It is true that you can implement the type signature of the `swap` function for any `Traversable` monad. However, the `swap` function for monads needs to satisfy very different and stronger laws than the `sequence` function from the `Traversable` type class. I'll have to look at the "Book of Monads"; but, if my memory serves, the FPiS book does not derive any of these laws." See https://www.linkedin.com/feed/update/urn:li:groupPost:41001-6523141414614814720?commentUrn=urn%3Ali%3Acomment%3A%28groupPost%3A41001-6523141414614814720%2C6532108273053761536%29
Publishers around the world struggle with how their metadata morphs as it travels from their domains into the wilds of retailers, distributors, and other partners. Despite the use of industry standards like ONIX, there is still a lot of inconsistency that can happen when a title record is sent out to so many different destinations. In this session, we will look at that flow of data and consider some of the best practices you can use to make your metadata more consistent and more robust across various end points. We will talk about: the flow metadata takes both inside and outside a publishing house; how the flow of metadata is affected by different metadata formats; real-world examples of metadata that is delivered to different retailers, with explanations of the differences and quirks youāll encounter; and, best practices for creating and maintaining your metadata.
Functional programming in kotlin with Arrow [Sunnytech 2018]Emmanuel Nhan
Ā
Slides from my talk about Kotlin & Functional programming with Arrow which I gave at Sunny Tech 2018 (http://sunny-tech.io)
It showcases how Kotlin is a good fit for functional programming, thanks to Arrow.
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, and others (Martin Odersky, Derek Wyatt, Adelbert Chang)
Introduction to Category Theory for software engineersPranav Prakash
Ā
An Introduction to Category Theory for Software Engineers. By Dr Steve Easterbrook
Associate Professor,
Dept of Computer Science,
University of Toronto
sme@cs.toronto.edu
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, and Sam Halliday, author of "Functional Programming for Mortals with Scalaz" https://leanpub.com/fpmortals
19. Data Structures and Algorithm ComplexityIntro C# Book
Ā
In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to use. We will explain how to choose between data structures like hash-tables, arrays, dynamic arrays and sets implemented by hash-tables or balanced trees. Almost all of these structures are implemented as part of NET Framework, so to be able to write efficient and reliable code we have to learn to apply the most appropriate structures for each situation.
Download for better quality.
Functor Composition - including (starting from) the definition in Bartosz Milewski's Category Theory book. https://github.com/hmemcpy/milewski-ctfp-pdf
What would be a good answer to the question of what functors are? To help formulating a reasonably well rounded answer, I have made some slides capturing what I considere particularly useful aspects of the following sourcesā exposition of the concept of Functor:
* Bartosz Milewskiās Scala eXchange 2017 Keynote, The Maths Behind Types
*Paul Chiusano and Runar Bjarnasonās book, Functional Programming in Scala
* Debasish Ghoshās book - Functional and Reactive Domain Modeling
* Christopher Allen, and Julie Moronuki's book - Haskell Programming from First Principles
Abstract: This workshop teaches basic algorithms in whiteboarding interviews. All the code examples are in Python and the course has dual purpose teaching basic Python programming.
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Download for better quality.
Monads do not Compose. Not in a generic way - There is no general way of composing monads.
A comment from RĆŗnar Bjarnason, coauthor of FP in Scala: "They do compose in a different generic way. For any two monads F and G we can take the coproduct which is roughly Free of Either F or G (up to normalization)".
Another comment from Sergei Winitzki (which caused me to upload https://www.slideshare.net/pjschwarz/addendum-to-monads-do-not-compose): "It is a mistake to think that a traversable monad can be composed with another monad. It is true that, given `Traversable`, you can implement the monad's methods (pure and flatMap) for the composition with another monad (as in your slides 21 to 26), but this is a deceptive appearance. The laws of the `Traversable` typeclass are far insufficient to guarantee the laws of the resulting composed monad. The only traversable monads that work correctly are Option, Either, and Writer. It is true that you can implement the type signature of the `swap` function for any `Traversable` monad. However, the `swap` function for monads needs to satisfy very different and stronger laws than the `sequence` function from the `Traversable` type class. I'll have to look at the "Book of Monads"; but, if my memory serves, the FPiS book does not derive any of these laws." See https://www.linkedin.com/feed/update/urn:li:groupPost:41001-6523141414614814720?commentUrn=urn%3Ali%3Acomment%3A%28groupPost%3A41001-6523141414614814720%2C6532108273053761536%29
Publishers around the world struggle with how their metadata morphs as it travels from their domains into the wilds of retailers, distributors, and other partners. Despite the use of industry standards like ONIX, there is still a lot of inconsistency that can happen when a title record is sent out to so many different destinations. In this session, we will look at that flow of data and consider some of the best practices you can use to make your metadata more consistent and more robust across various end points. We will talk about: the flow metadata takes both inside and outside a publishing house; how the flow of metadata is affected by different metadata formats; real-world examples of metadata that is delivered to different retailers, with explanations of the differences and quirks youāll encounter; and, best practices for creating and maintaining your metadata.
BFF Pattern in Action: SoundCloudās MicroservicesBora Tunca
Ā
At SoundCloud we managed to break away from the monolith while delivering key business features. Our journey towards a microservices architecture has not been a straightforward one. We experimented a lot to reach the set of tools and technologies that we use today. We changed how we build our applications. We introduced specific apis for our mobile and web clients. We call them BFFs (backend for the frontend). They became the central piece of SoundCloudās architecture. We rethought how we monitor our services. We created a service registry for knowledge sharing. While making all these changes, we benefited from the learnings of our peer companies. This talk will share our learnings from this journey: what worked for us and what we moved away from.
Demystifying the Inner Workings of Amazon KeywordsBookNet Canada
Ā
Take a deep dive into the mysterious world of Amazon keywords. Using lessons learned from one publisherās journey to uncover the online retailerās inner workings and enhance discoverability, Leeman will offer advice on best practices, workarounds, and successful workflows. From character limits to lemmatization, no stone is left unturned.
11 flowers gifts which are perfect for allergy sufferersCeline Wilson
Ā
It is very difficult task to choose flowers for the people who are suffering from allergy problem. Here are 11 Flowers Gifts ideas which are perfect for Allergy Sufferers.
For every exercise, completes about 3-4 sets. increase the weight for each set and continues reps until failure. On a weekly basis, the training routine consists of two workout sessions every day for six days a week.
Shape Safety in Tensor Programming is Easy for a Theorem Prover -SBTB 2021Peng Cheng
Ā
We present shapesafe (https://github.com/tribbloid/shapesafe) - the most comprehensive compile-time verifier for scala linear algebra - by only exploiting scala's type system as a theorem prover. This new paradigm allows type-level tensor computations, even those as complex as composite neural network blocks, to be rewritten, simplified and verified while being written. We will talk about its design and limitations, and most important, what we have observed and learned from it
Kyo is a next-generation effect system that introduces an approach based on algebraic effects to deliver straightforward APIs in the pure Functional Programming paradigm. Unlike similar solutions, Kyo achieves this without inundating developers with esoteric concepts from Category Theory or using cryptic symbolic operators. This results in a development experience that is both intuitive and robust.
Kyo generalizes the innovative effect rotation mechanism introduced by ZIO. While ZIO restricts effects to two channels, namely dependency injection and short-circuiting, Kyo allows for an arbitrary number of effectful channels. This enhancement offers developers greater flexibility in effect management and simplifies Kyo's internal codebase through more principled design patterns.
In addition to this novel approach to effect handling, Kyo provides seamless direct syntax inspired by Monadless and a comprehensive set of built-in effects like Aborts for short-circuiting, Envs for dependency injection, and Fibers for green threads with fine-grained uncooperative preemption.
After over two years in development, the first public release of the project will be made during Functional Scala 2023. Attendees will also be treated to benchmark results that showcase Kyo's unparalleled performance.
Scientific Computing with Python Webinar March 19: 3D Visualization with MayaviEnthought, Inc.
Ā
In this webinar, Didrik Pinte provides an introduction to MayaVi, the 3D interactive visualization library for the open source Enthought Tool Suite. These tools provide scientists and engineers a sophisticated Python development framework for analysis and visualization.
Big picture of category theory in scala with deep dive into contravariant and...Piotr ParadziÅski
Ā
A big picture of category theory in Scala - starting from regular functors with additional structure (Apply, Applicative, Monad) to Comonads. Usually, we think about structures like Monoids in a monoidal category with particular tensor. In here I analyze just signatures of different abstractions.
Exploration of Contravariant functors as a way to model computation "backward" or abstract over input with the ability to prepend operation. Examples for predicates, sorting, show and function input (or any other function parameter except the last one).
Profunctors as abstraction unifying Functors and Contravariant functors to model both input and output. Example for Profunctor - function with one argument.
Relation to Bifunctors, Kan extensions, Adjunctions, and Free constructions.
Big picture of category theory in scala with deep dive into contravariant and...Scalac
Ā
A big picture of category theory in Scala - starting from regular functors with additional structure (Apply, Applicative, Monad) to Comonads. Usually, we think about structures like Monoids in a monoidal category with particular tensor. In here I analyze just signatures of different abstractions.
Exploration of Contravariant functors as a way to model computation "backward" or abstract over input with the ability to prepend operation. Examples for predicates, sorting, show and function input (or any other function parameter except the last one).
Profunctors as abstraction unifying Functors and Contravariant functors to model both input and output. Example for Profunctor - function with one argument.
Relation to Bifunctors, Kan extensions, Adjunctions, and Free constructions.
A talk from Toronto's FITC Spotlight on Hardware talk. I spoke about using tools like Openframeworks, OpenCV, and the Kinect to create Interactive Installations, and paired it with an interactive lighting installation.
References, citations, and source code can be found here: http://www.andrewlb.com/2013/06/sls-notes/
What did functional programming ever do for us (software engineers)? An overview of advantages of functional programming, with code examples in Scala. See recorded presentation at https://youtu.be/glDudJ3fqLk
Three Dimensional Scaffold Direct Writer for Fabricating Fiber-Reinforced Mat...Rohan Rath
Ā
Senior/capstone design project. First place regional and second place global winner at the IIE Annual Conference & Exposition, John Deere sponsored technical paper competition. This paper was presented at the IIE Northeastern university regional conference in Boston, MA (March 2015) and at the IIE annual global conference & exposition in Nashville, TN (May 2015).
Accelerating Random Forests in Scikit-LearnGilles Louppe
Ā
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
Similar to Classy Monad Transformers (Stop Eff'ing Around) (20)
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Ā
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. Itās here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
Ā
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
AI Genie Review: Worldās First Open AI WordPress Website CreatorGoogle
Ā
AI Genie Review: Worldās First Open AI WordPress Website Creator
šš Click Here To Get More Info šš
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
ā Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
ā First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
ā Publish Automated Posts and Pages using AI Genie directly on Your website
ā 50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
ā Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
ā Just Enter the title, and your Content for Pages and Posts will be ready on your website
ā Automatically insert visually appealing images into posts based on keywords and titles.
ā Choose the temperature of the content and control its randomness.
ā Control the length of the content to be generated.
ā Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
ā 100% Easy-to-Use, Newbie-Friendly Technology
ā 30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
Ā
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Ā
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
Ā
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
Ā
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
šš Click Here To Get More Info šš
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
ā Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
ā Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
ā Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
ā Fully automated AI articles bulk generation!
ā Auto-post or schedule stunning AI content across all your accounts at onceāWordPress, Facebook, LinkedIn, Blogger, and more.
ā With one keyword or URL, generate complete websites, landing pages, and moreā¦
ā Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
ā Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
ā Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
ā Save over $5000 per year and kick out dependency on third parties completely!
ā Brand New App: Not available anywhere else!
ā Beginner-friendly!
ā ZERO upfront cost or any extra expenses
ā Risk-Free: 30-Day Money-Back Guarantee!
ā Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Need for Speed: Removing speed bumps from your Symfony projects ā”ļøÅukasz ChruÅciel
Ā
No one wants their application to drag like a car stuck in the slow lane! Yet itās all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. Weāll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
AI Pilot Review: The Worldās First Virtual Assistant Marketing SuiteGoogle
Ā
AI Pilot Review: The Worldās First Virtual Assistant Marketing Suite
šš Click Here To Get More Info šš
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
ā Deploy AI expert bots in Any Niche With Just A Click
ā With one keyword, generate complete funnels, websites, landing pages, and more.
ā More than 85 AI features are included in the AI pilot.
ā No setup or configuration; use your voice (like Siri) to do whatever you want.
ā You Can Use AI Pilot To Create your version of AI Pilot And Charge People For Itā¦
ā ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
ā ZERO Limits On Features Or Usages
ā Use Our AI-powered Traffic To Get Hundreds Of Customers
ā No Complicated Setup: Get Up And Running In 2 Minutes
ā 99.99% Up-Time Guaranteed
ā 30 Days Money-Back Guarantee
ā ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
RequirementāāāCollecting the Requirements is the first Phase in the SSLC process.
Feasibility Studyāāāafter completing the requirement process they move to the design phase.
Designāāāin this phase, they start designing the software.
Codingāāāwhen designing is completed, the developers start coding for the software.
Testingāāāin this phase when the coding of the software is done the testing team will start testing.
Installationāāāafter completion of testing, the application opens to the live server and launches!
Maintenanceāāāafter completing the software development, customers start using the software.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Ā
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
4. Materials
This presentation and all code is
at github.com/shajra/shajra-presentations/tree/master/scala-mtl
compiler-checked by Rob Norrisās sbt-tut plugin.
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 4 / 46
5. In lieu of time
Assuming knowledge of
Scala implicits
type classes
for-yield sugar w.r.t. Monad.
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6. Monads, Explicitly
1trait Monad[M[_]] {
3def pure[A](a: A): M[A]
5def flatMap[A, B](ma: M[A])(f: A => M[B]): M[B] =
6flatten(map(ma)(f))
8def flatten[A](mma: M[M[A]]): M[A] =
9flatMap(mma)(identity)
11def map[A, B](ma: M[A])(f: A => B): M[B] =
12flatMap(ma)(f andThen pure)
14}
Note: the Monad type class has three laws
very important, but elided for time
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 6 / 46
7. Monad syntax with implicits
For convenience (e.g. with for-yield)
1implicit class OpsA[A](a: A) {
3def pure[M[_]](implicit M: Monad[M]): M[A] =
4M pure a
6}
8implicit class
9MonadOps[M[_], A](ma: M[A])(implicit M: Monad[M]) {
11def map[B](f: A => B): M[B] =
12M.map(ma)(f)
14def flatMap[B](f: A => M[B]): M[B] =
15M.flatMap(ma)(f)
17}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 7 / 46
9. Where people come from
Enterprise Java
1trait DbConn; trait MetricsConn
3class UsersDao @Inject() (db: DbConn)
5class InsightsDao @Inject()
6(db: DbConn, metrics: MetricsConn)
8class App @Inject() (users: UsersDao, insights: InsightsDao)
Complaints
no compile-time safety
lacks composition with other FP practices
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 9 / 46
10. A ļ¬rst response
Have you tried passing a parameter to a function?
1trait DbConn; trait MetricsConn
3case class User(name: String)
4case class Insight(desc: String)
6def newUser(db: DbConn)(name: String): User = ???
8def getInsight
9(db: DbConn, metrics: MetricsConn)(user: User)
10: Insight = ???
12def runApp(db: DbConn, metrics: MetricsConn): Unit = ???
Observations
safer (no runtime reļ¬ection)
feels like āmanualā dependency injection
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 10 / 46
11. A second response
Passing a parameter is just the āreaderā monad
1case class Reader[R, A](run: R => A)
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 11 / 46
12. A second response
Readerās monad instance
1implicit def readerMonad[R]: Monad[Reader[R, ?]] =
2new Monad[Reader[R, ?]] {
4def pure[A](a: A): Reader[R, A] =
5Reader { _ => a }
7override def flatMap[A, B]
8(ra: Reader[R, A])(f: A => Reader[R, B])
9: Reader[R, B] =
10Reader { r => f(ra run r) run r }
12}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 12 / 46
14. A second response
Beneļ¬ts
Plumbing is hidden a little.
Weāre getting some composition.
Complaints
A global conļ¬g is anti-modular.
Side-eļ¬ects! Is this even FP?
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 14 / 46
15. Eļ¬ect-tracking types
Naive implementation for presentation (stack unsafe)
1class IO[A](a: => A) {
2def unsafeRun: A = a
3}
5object IO { def apply[A](a: => A) = new IO(a) }
7implicit def ioMonad: Monad[IO] =
8new Monad[IO] {
9def pure[A](a: A): IO[A] = IO(a)
10override def flatMap[A, B]
11(ioa: IO[A])(f: A => IO[B]): IO[B] =
12IO(f(ioa.unsafeRun).unsafeRun)
13}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 15 / 46
16. Eļ¬ect-tracking types
No side-eļ¬ects while composing
1def getTime: IO[Long] = IO { System.currentTimeMillis }
2def printOut[A](a: A): IO[Unit] = IO { println(a) }
4def sillyIO: IO[Unit] =
5for {
6t <- getTime
7_ <- printOut(t)
8_ <- printOut(t)
9} yield ()
Run at the āend of the worldā
1scala> sillyIO.unsafeRun
21490293856842
31490293856842
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 16 / 46
17. Thus far we have
Two monads
Reader passes in a parameters
IO tracks an eļ¬ect
Is composing them useful?
Reader[IO[A]]
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 17 / 46
18. Letās compose our monads
But in general, monads donāt compose
1case class Compose[F[_], G[_], A](fga: F[G[A]])
3def impossible[F[_] : Monad, G[_] : Monad]
4: Monad[Compose[F, G, ?]] = ???
Even if we can ļ¬atten F[F[A]] and G[G[A]]
Itās hard to ļ¬atten F[G[F[G[A]]]].
Can we compose IO and Reader speciļ¬cally?
Yes, thatās exactly what monad transformers do.
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 18 / 46
19. Many monads have respective transformers
Readerās transformer ā ReaderT
1case class ReaderT[R, M[_], A](run: R => M[A])
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 19 / 46
20. ReaderTās monad instance
Depends on inner typeās monad instance
1implicit def readerTMonad[R, M[_]]
2(implicit M: Monad[M]): Monad[ReaderT[R, M, ?]] =
4new Monad[ReaderT[R, M, ?]] {
6def pure[A](a: A): ReaderT[R, M, A] =
7ReaderT { _ => M.pure(a) }
9override def flatMap[A, B]
10(ma: ReaderT[R, M, A])(f: A => ReaderT[R, M, B])
11: ReaderT[R, M, B] =
12ReaderT { r => M.flatMap(ma run r) { f(_) run r } }
14}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 20 / 46
22. A useful typeclass for readers
1trait MonadReader[R, M[_]] {
3def monad: Monad[M]
5def ask: M[R]
6def local[A](ma: M[A])(f: R => R): M[A]
8}
10object MonadReader {
11def ask[M[_], R](implicit MR: MonadReader[R, M]): M[R] =
12MR.ask
13}
Note: the MonadReader type class has laws
very important, but elided for time
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 22 / 46
23. Creating MonadReader for ReaderT
1implicit def readerTMonadReader[R, M[_]]
2(implicit M: Monad[M])
3: MonadReader[R, ReaderT[R, M, ?]] =
4new MonadReader[R, ReaderT[R, M, ?]] {
6val monad = readerTMonad(M)
8def ask: ReaderT[R, M, R] = ReaderT { _.pure[M] }
10def local[A]
11(ma: ReaderT[R, M, A])(f: R => R): ReaderT[R, M, A] =
12ReaderT { ma run f(_) }
14}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 23 / 46
24. Using stacks with parametric polymorphism
Stack not speciļ¬ed, only constrained
1def abstractlyBuilt[M[_] : Monad : MonadReader[Int, ?[_]]]
2: M[(String, Int)] =
3for {
4c <- "hi".pure[M]
5r <- MonadReader.ask[M, Int]
7// can't do this yet
8// t <- ReaderT { (_: Int) => getTime }
10// nicer syntax would be
11// getTime.liftBase[M]
13} yield (c, r)
Stack speciļ¬ed when run
1scala> abstractlyBuilt[Stack].run(1).unsafeRun
2res18: (String, Int) = (hi,1)
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 24 / 46
25. One more useful lift
For lifting your base monad
1trait MonadBase[B[_], M[_]] {
3def monadBase: Monad[B]
4def monad: Monad[M]
6def liftBase[A](base: B[A]): M[A]
8}
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 25 / 46
26. A lift behind the scenes
People used to complain about this
1trait MonadTrans[T[_[_], _]] {
2def liftT[G[_] : Monad, A](a: G[A]): T[G, A]
3}
But now it can be internal plumbing
Donāt lift too much!
With the SI-2712 ļ¬x, you donāt have to
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 26 / 46
27. Many other transformers
Transformer Underlying Type class
IdentityT[M[_], A] M[A]
ReaderT[S, M[_], A] R =>M[A] MonadReader[R, M[_]]
StateT[S, M[_], A] S =>M[(S, A)] MonadState[S, M[_]]
OptionT[M[_], A] M[Option[A]] MonadOption[E, M[_]]
EitherT[E, M[_], A] M[Either[E,A]] MonadError[E, M[_]]
ContT[M[_], A] (A =>M[R])=>M[R] MonadCont[M[_]]
. . . . . . . . .
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 27 / 46
28. Some transformers commute eļ¬ects
But we end up with O(n2
) to support them
1implicit def readerTMonadState[R, S, M[_]]
2(implicit MS: MonadState[S, M])
3: MonadState[S, ReaderT[R, M, ?]] =
4??? // can be implemented lawfully
Not all transformers commute eļ¬ects
1implicit def contTMonadError[R, E, M[_]]
2(implicit ME: MonadError[E, M])
3: MonadError[E, ContT[M, ?]] =
4??? // would break MonadError laws if implemented
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 28 / 46
29. What have we got thus far?
Improvements
separations of concerns (Reader from IO)
no side-eļ¬ects
Remaining Complaint
still using a global conļ¬guration
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 29 / 46
30. Classy Monad Transformers Example
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 30 / 46
31. Setup
Using a fork of AloĆÆs Cochardās āscato-styleā Scalaz 8
1import scalaz.Prelude.Base._
Notable diļ¬erences
minimal subtyping
SI-2712 ļ¬xed!
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 31 / 46
32. Some abstractions
Our conļ¬guration from before
1case class DbConfig()
2case class MetricsConfig()
3case class AppConfig(db: DbConfig, metrics: MetricsConfig)
Sukant Hajra / @shajra Classy Monad Transformers (Stop Eļ¬āing) March 24, 2017 32 / 46
35. Make an āappā monad
Use whatever stack makes sense
1type AppStack[A] = ReaderT[AppConfig, IO, A]
2case class App[A](run: AppStack[A])
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36. Make instances for the āappā monad
Haskellās NewTypeDeriving would be nice here
1implicit val appInstances
2: MonadDb[App] with MonadMetrics[App] =
3new MonadDb[App] with MonadMetrics[App] {
4def monadBase =
5new MonadBaseClass[IO, App] with
6MonadClass.Template[App] with
7BindClass.Ap[App] {
8def pure[A](a: A): App[A] = App(a.pure[AppStack])
9def flatMap[A, B]
10(ma: App[A])(f: A => App[B]): App[B] =
11App(ma.run.flatMap(f andThen { _.run }))
12def liftBase[A](base: IO[A]) =
13App(base.liftBase[AppStack])
14def monadBase = Monad[IO]
15}
16def ask = MonadReader.ask[AppStack, AppConfig]
17def dbConfig = App(ask.map { _.db })
18def metricsConfig = App(ask.map { _.metrics })
19}
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