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Go and Object Oriented Programming

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PyData NYC 2019

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The document summarizes Ibis, an open source Python framework for data analysis that provides a high-level language for expressing data transformations and analytics independently of backend execution engines like Pandas or Spark. It introduces Ibis by comparing different approaches to scaling data science workflows from Pandas on a single machine to distributed systems. It then demonstrates how to express the same data preparation and analysis logic in Ibis and have it execute on different backends like Pandas or PySpark without code changes. The document shows how Ibis compilers translate expressions to backend-specific code to enable seamless transition between backends.

Sprite js vs craftyjs

Sprite.js is a framework for creating animations and games using sprites that works across desktop and mobile browsers. Crafty.js is a lightweight modular game engine that includes components like animation, collision detection, and sprites. Sprite.js focuses on animation but lacks some features, while Crafty.js is a full-featured engine that lacks physics support. Both support 2D, parallel and isometric views but Crafty.js has more features and support for audio and multiple graphics.

Architectural Tradeoff in Learning-Based Software

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In classical software development, developers write explicit instructions in a programming language to hardcode the explicit behavior of software systems. By writing each line of code, the programmer instructs the software to have the desirable behavior by exploring a specific point in program space. Recently, however, software systems are adding learning components that, instead of hardcoding an explicit behavior, learn a behavior through data. The learning-intensive software systems are written in terms of models and their parameters that need to be adjusted based on data. In learning-enabled systems, we specify some constraints on the behavior of a desirable program (e.g., a data set of input–output pairs of examples) and use the computational resources to search through the program space to find a program that satisfies the constraints. In neural networks, we restrict the search to a continuous subset of the program space. This talk provides experimental evidence of making tradeoffs for deep neural network models, using the Deep Neural Network Architecture system as a case study. Concrete experimental results are presented; also featured are additional case studies in big data (Storm, Cassandra), data analytics (configurable boosting algorithms), and robotics applications.

Refactoring Ruby Code

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This document discusses refactoring code in Ruby. It provides examples of refactoring techniques like replacing conditionals with polymorphism, introducing named parameters, replacing magic numbers with symbolic constants, extracting surrounding methods, and replacing loops with closure methods. The goal of refactoring is to clean up code without changing its external behavior in order to make it more readable, flexible and maintainable.

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The document provides information about game development using Unity. It discusses concepts like game engines, Unity interface and components, character control, game design, gameplay, basic components, enemy AI, memory management and optimization. It also covers topics such as the anatomy of video games, the game development process, 2D and 3D art, what Unity is and why to use it, its interface and execution order of event functions. Additionally, it summarizes Mecanim workflow, asset preparation, terrain editing, adding water and skyboxes, importing assets, lightmapping, fog, game design, and enemy pathfinding using waypoints.

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This document discusses layout and animation performance in Android. It begins with an overview of how motion is perceived by the human eye and how to achieve smooth motion. It then covers topics like measuring and laying out views, optimizing for the GPU, using hardware layers for animation, and getting size information during animation using ViewTreeObserver. The document provides guidance on profiling performance, reducing unnecessary layout requests, and techniques for creating smooth animations in Android.

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After 10 years of Object Orientated Java, 2 years of Functional Programming in Scala was enough to convince me that I could never switch back. But why? The answer is simple: Functional Scala lets you think less. It reduces the number of moving parts you need to hold in your head, lets you stay focussed and saves your mental stack from overflowing. In this talk I'll show you how to stop treating Scala as a better Java and start exploring the world of Functional Programming. I'll use code examples to demonstrate a four step path that'll let you ease yourself into the world of Functional Programming while continuing to deliver production quality code. With each step we'll learn a new technique, and understand how it leaves you with less to think about: Hopefully this talk will convince you that Functional Scala leads to code that's easier to hold in your head, and leave you excited about learning a new paradigm.

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A design system can vastly improve your team's productivity, but most of all, it leads to better products! The challenge lies in creating a mature system and leading its adoption across the company successfully. Let's talk about how we learned to meet the needs of different designers and developers on different products, on different tech stacks. Attendees will go home with tips they can use to improve design systems of any stage.

Refactoring

Refactoring is a technique for restructuring existing code without changing its external behavior. It involves reorganizing code to improve factors like design, readability, and maintainability. Some reasons to refactor include removing duplication, improving design, reducing complexity, and decreasing technical debt. Refactoring should be done incrementally with unit tests to ensure changes don't introduce bugs. It involves techniques like extracting methods, simplifying conditionals, and improving names. Refactoring helps keep code healthy and manageable as a project evolves over time.

EuRuKo JRuby Talk 2008

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JRuby is a Java implementation of the Ruby language that aims to provide compatibility with Ruby while adding features of the Java platform like native threading and access to Java libraries. The presenters provide an overview of JRuby, demonstrate how to create Swing GUIs and graphics applications with JRuby, and discuss its use for web applications like Ruby on Rails. They also highlight tools for IDE integration and show examples of JRuby being used in production systems.

Going Staff

What's up with becoming a Staff Engineer? What does it mean? Is it just for people who want to keep coding? How do you become a Staff Engineer and what does the work entail? What if I told you, that being a Staff engineer actually required a lot of communication and collaboration skills? In this talk, let's answer all those questions - as it's still quite fuzzy what a Staff engineer actually is.

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The document discusses various topics related to open source software including welcoming and helping users, releasing software early and often, treating open source software as a hobby rather than work, using micro libraries, benchmarking code performance, learning as you go, co-maintaining projects, reporting and fixing issues, being polite, discussing ideas for improvements, considering how software may be used, making open source more diverse and sustainable, and enjoying participating in open source projects.

Elixir & Phoenix – Fast, Concurrent and Explicit

Key takeaways What are Elixir and Phoenix? What makes them standout among programming languages and frameworks? Why would I want to use Functional Programming, what are the benefits and why does it work so well for the web? How capable is Erlang (Whatsapp example) performance and reliability wise and why would I consider it for a project? How does explicitness help in system design? Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.

Functioning Among Humans

In the development world most people are striving for technical excellence: better code, faster run times, more convenient interfaces, better databases… But is that really what helps us create better software? In the end software development is done by groups of people creating products together. To do that communication and collaboration are essential. You can be the best programmer ever, but if you can’t efficiently work with others what good does it do you? This talk will introduce you to relevant, easy to grasp concepts of collaboration and communication as well as give you food for thought.

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Do You Need That Validation? Let Me Call You Back About It

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Elixir is great, so clearly we'll all rewrite our applications in Elixir. Mostly, you can't and shouldn't do that. This presentation will show you another path. You’ll see how at Liefery, we started with small steps instead of rewriting everything. This allowed us to reap the benefits earlier and get comfortable before getting deeper into it. We’ll examine in detail the tactics we used to create two Elixir apps for new requirements, and how we integrated them with our existing Rails code base. Join us on our tale of adopting Elixir and Phoenix and see what we learned, what we loved, and what bumps we hit along the road

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“What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess - but how do you know? How long would that function take with a million elements? Is that tail-recursive function always faster? Benchmarking is here to give you the answers, but there are many pitfalls in setting up a good benchmark and analyzing the results. This talk will guide you through, introduce best practices, and surprise you with some results along the way. You didn’t think that the order of arguments could influence its performance...or did you?

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In the development world most people are striving for technical excellence: better code, faster run times, more convenient interfaces, better databases, faster deployments… But is that really what makes us better at developing software? In the end software development is done by groups of people creating products together. To do that communication and collaboration between humans is essential - you can be the best programmer ever, if you can’t efficiently work with others what good does it do you? This talk will give you a primer and food for further thought.

Stop Guessing and Start Measuring - Benchmarking Practice (Poly Version)

This is the Polyconf version of the talk, including a little MJIT vs. GraalVM rebuttal, JavaScript, SQL, Ruby and Elixir to be truly Poly. “What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess, your intuition might be correct - but how do you know? Benchmarking is here to give you the answers, but there are many pitfalls in setting up a good benchmark and analyzing the results. This talk will guide you through, introduce best practices, and surprise you with some unexpected benchmarking results. You didn’t think that the order of arguments could influence its performance...or did you?

Code, Comments, Concepts, Comprehension – Conclusion?

How fast is it really? Benchmarking in Practice (Ruby Version)

The document describes various benchmarks performed to compare the performance of different Ruby implementations and algorithms for sorting arrays. The benchmarks show that: 1) CRuby generally outperforms JRuby in sorting performance, with CRuby sorting arrays 2-3 times faster than JRuby in the benchmarks. 2) The ".sort.reverse" approach is the fastest method for reversing a sorted array, performing up to 7 times faster than using a sorting block. 3) Using "sort_by" with a transformation is generally faster than a sorting block but slower than ".sort.reverse".

How fast ist it really? Benchmarking in practice

“What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess what’s fastest or how long something will take, but do you know? How long does it take to sort a list of 1 Million elements? Are tail-recursive functions always the fastest? Benchmarking is here to answer these questions. However, there are many pitfalls around setting up a good benchmark and interpreting the results. This talk will guide you through, introduce best practices and show you some surprising benchmarking results along the way.

Introducing Elixir the easy way

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Elixir and Phoenix are known for their speed, but that’s far from their only benefit. Elixir isn’t just a fast Ruby and Phoenix isn’t just Rails for Elixir. Through pattern matching, immutable data structures and new idioms your programs can not only become faster but more understandable and maintainable. This talk will take a look at what’s great, what you might miss and augment it with production experience and advice.

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