Thank you for helping us make the conference awesome! That's a great opportunity to connect with speakers :) Track owners set up and introduce speakers, manage the Q&A after each presentation and make sure sessions run on time. The simple algorithm is Connect to Zoom meeting Introduce speaker Check channel for questions and convey them to the speaker Invite everyone to q&a room and the hallway track Show the slide with the next talk

2. Lists We started with the tree example, but there is a simpler recursive data structure, a list. You may think of a list as a tree with the branching ratio of one. data List c = Nil | Cons c (List c) sealed trait List[C] case class Nil[C]() extends List[C] case class Cons[C](head: C, tail: List[C]) extends List[C]

TLDR Dear participants, to enjoy the conference make sure you... Register on Eventbrite. Join our Slack and check out our channels #talks, #hallway, #support. Join #Harmony track by JetBrains Join Zoom. Main track, Q&A room, and hallway track links will be shared with the registered attendees via email. Follow Twitch channels scalalove and scalalove2 for live streaming Check out our website. Enjoy the conference! <3

51:50 + Q: Are you planning to write more books? B: Yes 53:56 + Q: How many companies use this approach in production? O: A variety of different companies 1:02:30 + Q: How to define an algebra?

Track owners set up and introduce speakers, manage the Q&A after each presentation and make sure sessions run on time. Before the conference We will need your email associated with Zoom account you are going to use. And your hours of availability. Please fill this form. https://forms.gle/1usZMXtpyFPyDHHR9 Make sure you are a host of the webinar Find the speakers bio here You can discuss with the speaker how they want to be introduced

Traditional algebras deal with two things: Building and parsing expressions Evaluating expressions Building expressions Example of an expression: $$ x^2 + 3 x + 4$$ When we parse such an expression we create an expression tree. We start with

TLDR Dear speakers, to enjoy the conference make sure you... Join our Slack and check out our channels: #speakers, #talks, #hallway, #support. Submit your talk via papercall. Abstracts are optional, elevator pitches are required. Join Zoom and participate in a test call (to be scheduled later). There will be 2 main tracks, Q&A rooms, and a hallway track. Have a back up for streaming your slides: keynote live, google slides broadcasting, or anything you choose. Check out our website. Ping Oli for your Scalalovatar. Enjoy the conference!

Traditional algebra deals with two things: Building and parsing expressions Evaluating expressions Building expressions Example of an expression: $$ x^2 + 3 x + 4$$ When we parse such an expression we create an expression tree

You know how tricky it is to develop complex distributed systems. What about debugging them? It’s even trickier! Think of all the complexity: there are many services, and each of them may fail independently. Some failures may cascade into others, hiding root causes. Plus, there are external services that we don't control that may introduce new vulnerabilities! You may be wondering, then: how should we debug such complex distributed systems? Distributed tracing for the win! During my time at Expedia, I worked on a project where each request had to go through a dozen services, each deployed as an independent application. To figure out where a problem occurred, we had to guess and check a variety of logs and dashboards—a time-consuming and frustrating process, not unlike untangling a giant knot woven together with hundreds of threads. Eventually, our team adopted Haystack, an in-house-built OSS framework that made it easier to debug our distributed application. Since then, I’ve been using distributed tracing everywhere, and in this blog post, I’ll show you why.

Developers are experts at understanding code at a micro-level, including functions and syntax. But analyzing the architecture of a large, unfamiliar codebase quickly is radically different. There are thousands of files and commits, hundreds of thousands of LOC, and dozens of developers. How can you possibly reason about the codebase as a whole without knowing how all the pieces are working together? As a Solutions Architect at 47 Degrees, Oli has done a bit of consulting where she needs to quickly perform an architectural review of a large, unfamiliar codebase. Although tools like IntelliJ or Metals have some compelling code metrics for code discovery, Oli has mastered a powerful, but lesser-known system Structure101 that she will share with you in this talk. Join Oli to learn new ways you can quickly grasp a large, complex codebase!

Today we are going to talk about the lazy keyword in Scala. First, let's talk about what lazy is. It does what it name says -- it puts off some work until you need it which means the lazy expression is evaluated at most once, namely the first time the value is used. if it's never used, it will never be evaluated. To demonstrate, let's define an expensive calculation which takes no parameters and returns a Long value. def expensive: Long = { println("Hello") Thread.sleep(500) 2020 }

# Non Scala big conferences 2020 # Q2 ## Devoxx https://devoxx.com/#/ ### France 15-17 Apr CFP opening: Sunday, December 1, 2019 CFP closing: Thursday, January 16, 2020 ### UK 12-15 May No CFP announced yet ### Poland 17-19 June No CFP announced yet ## Kafka Summit https://kafka-summit.org/ ### London April 27 - April 28, 2020 Call for Papers opens:  November 1, 2019 Call for Papers closes:  December 15, 2019  ## GeeCon https://www.geecon.org/ ### Poland 13 - 15 May 2020 no CFP yet, + I was