In this talk, we'll explore why some technologies end up inexplicably dominating our field while others fade away despite their obvious merits. What will it take for the languages we care about to succeed, and what are the consequences of success? Let's apply a broad perspective to the mess we've made of modern computer science, and explore our options for getting the situation back on track through a careful examination of history, science and ponies.

Code used during the talk: https://github.com/shashi/fuconf-talk
Slides: https://docs.google.com/presentation/d/16Xfqd-xU8y2JEN0TIcacDoYnp0b5-W7ESDB5v1SmcXs/edit#slide=id.p

Elm is a strongly typed functional reactive programming (FRP) language that compiles to HTML, CSS, and Javascript. In Elm, the Signal type represents a time-varying value--things like mouse position, keys pressed, current time are signals. With Signals, one can write terse code that is isomorphic to a dataflow diagram of the app. The code hence feels natural and is 100% callback free. All this, with powerful type inference.

This talk is an introduction to FRP. It explores functionally composing graphics and UIs, and creating interactions and animations with the Signal type. There will also be an overview of Elm’s execution mechanism and the time traveling debugger: a consequence of Elm's purely functional approach.

While instructive, it will be good fun too, in the spirit of Elm.

Manual testing is something that's error prone, incomplete and impossible to replicate on a large scale. We have instead been using xUnit style of testing for quite some time now. This approach has a number of drawbacks like (a) We need to write test cases by hand which again doesn't scale for large systems (b) We may miss out some of the edge cases (c) Safeguarding missing cases with coverage metrics doesn't help, since metrics are mostly based on heuristics (d) maintaining test cases and test data is a real pain.

In property based testing we write properties and not low level test cases. And let the system generate test cases which validate such properties. There are 2 main advantages with this approach:

1. You think in terms of properties (or specifications) of the domain model which is the right granularity to think about
2. You don't need to manage test cases, which is completely done by the system that generates a large collection of test data

This approach is ideal for the functional programming paradigm, which focuses on pure functions. Using functional programming it's easier to reason about your model - hence it's easier to test functional programs using properties. In this talk I will take some real world examples of property validation and verification using scalacheck (the property based testing library for Scala) and a real world domain model.

Sync, Async, Blocking, Non-Blocking, Streaming are the buzzwords in the reactive programming world. This talk will attempt to attach some meaning to them. It will also demo the performance and resource consumption patterns for blocking-io, Scala Futures and RxJava Observables for comparable programs. Finally, a command line application that consumes twitter streams API will demo what is possible using the new reactive abstractions.

Traditionally functions return some value. Someone is waiting for that value and does some computation with it. This "someone" is called the continuation of this value. In a normal functional call, the continuation is "implicit". In the "continuation passing style" (hence forth called with the short form, CPS), we make the continuations explicit. In this style, function definitions take an extra argument called "continuation" and it never return. The "return value" of the function 'continues' by passing this value as an argument to the continuation. Continuations are sometimes called "gotos with arguments".

CPS is used as an intermediate stage while compiling a program since it makes the control structure of the program explicit and hence can be converted easily to machine code. Another feature of a CPS-transformed function is that it is tail-recursive even if the original function was not written in a tail-recursive style.

Continuations enable a programmer to build new control operators (if the language's built-in operators does not already provide the control operators the programmer need).

Javascript programmers have had a lot of choices when it comes to programming. There were days of mootools, scriptaculous and jQuery and then there are now days of Angular, Ember, Knockout and the like. As a javascript programmer myself, I find that Clojurescript/React as Om offers a fresh perspective into building performant Javascript UIs that are easy to write.

The talk will introduced concepts of React, immutable datastructures in Clojure and live code an application that demonstrates the concepts.

Immutable, persistent data structures form a big part of the value proposition of most functional programming languages.

It is important to understand why these data structures are useful and how they make it easier to reason about your program. 

It is also instructive to see how these data structures are implemented to get a greater appreciation for the inherent tradeoffs between performance and immutability.

In this talk I will do a walkthrough of some of these data structures drawing from the work of Chris Okasaki[1], and attempt to explain the essential ideas in a simple way. 

[1] Purely Functional Data Structures, Chris Okasaki, School of Computer Science, Carnegie Mellon University 

I learn different languages not to make use of them, but to program in my current languages in a better way. As we adapt functional style of programming in mainstream languages, like Java, C#, and C++, we can learn a great deal from a language that is touted as a purely functional language.

Haskell is statically typed, but not in a way like Java, C#, or C++. Its static typing does not get in the way of productivity. Haskell quietly does lazy evaluation and enforces functional purity for greater good. Everyday programmers, like your humble speaker, who predominantly code in mainstream languages, can greatly benefit from learning the idioms and style of this elegant language. The next time we sit down to crank out some code in just about any language, we can make use of some of those styles, within the confines of the languages, and move towards a better, functional style.

Scala began its life as an experiment to “unify” object-oriented programming and functional programming. Martin Odersky believed that the differences between FP and OO are more cultural than technical, and that there was a room for beautifully unify various ideas from the two into one simple core.

How successful has Scala been in its goals? Is it the like “the grand unified theory of universe” or like the infamous “vegetarian ham”? [1]

In this talk, we will see just how Scala unifies various ideas – such as type-classes, algebraic data types, first-class modules, functions under one simple core comprising of traits, objects, implicits, and open recursion. We will how this unification unintendedly subsumes many concepts that require seprate features in other languages, such as functional dependencies, type families, GADTs in Haskell. We will see how this has given a rise to a new “implicit calculus”, which could lay a foundation for next generation of generic programming techniques.

We will see that this unification comes at a certain cost, wherein it leads to some compromises on both sides. However many of these trade-offs are particular to Scala (largely due to the JVM imposed restrictions). The goal of unification is still noble, and we need not throw the baby out with the bathwater.

[1]: https://twitter.com/bos31337/status/425524860345778176

Functional programming as a programming style and discipline is useful even in languages which are not pure functional languages. By practising programming in a pure functional language like Haskell, programmers can drastically improve the quality of code when coding in other languages as well.

The talk is based on first hand experience of using Haskell in internal courses in our organisation to improve code quality.

This talk will cover Gofer (one of the earliest variants of Haskell) as a teaching tool, including the choice of the language, the features from Haskell that should (and shouldn't) be covered and the obstacles and benefits of the exercise.

Futures and promises are no strangers to most programmers. They have made their way into major mainstream languages, including but not limited to, Java, C#, JavaScript, and C++.

This abstraction however is typically dreaded due to the way they’re typically done. Most manifestations lead to the insidious callback hell, and some, like Java’s, are incredulously limited.

Why can’t we keep our simple, straightforward code, and still have the concurrency benefits of futures and promises?! Turns out we can. Functional programming is here to the rescue! We will see how the seemingly obscure abstractions like monads, delimited continuations allow us to do just that.

We will also go over some syntactic-transformation based approaches (basically, macros), such as C#’s async-await, and see how it compares with the functional approaches discussed earlier.

As Object Oriented programmers, we follow SOLID Design Principles for improving OO design of our applications.

If you take the SOLID principles to their extremes, you arrive at something that makes Functional Programming look quite attractive.

Objects are merely a poor man's closures. And ultimately Closures are a poor man's objects.

This talk explains about how applying SOLID Design principles like Single Responsibility Principle (SRP) leads to many small classes. Furthermore, if you rigorously apply the Interface Segregation Principle (ISP), you'll eventually arrive at the ultimate Role Interface: an interface with a single method.

If you apply the SRP and ISP like that, you're likely to evolve a code base with many fine-grained classes that each have a single method.

Objects are data with behaviour. Closures are too are data with behaviour.

This talk will enlighten how applying Object Oriented Design Principles to their extreme will lead you to Functional Programming.

It's necessary for Object Oriented programmer to understand why Functional programming makes sense.

At the end, this talk will also cover essential basics of Functional programming needed to be known for every Object Oriented Programmer.

This talk is an introduction on lambda calculus and will address the foundations of functional programming languages.

We will learn the building blocks of lambda calculus - syntax, rules, and application.

As Java programmers we have used a number of design patterns and design techniques. With the introduction of lambda expressions, we now have some more sharper tools in our design toolbox. Come to this presentation to learn how we can implement some elegant design ideas with lambda expressions. We will learn about these design techniques, not using diagrams, but by realizing the ideas in code.

Of all the computational abstractions that functional programming has brought to the table, monads seem to be disproportionately more popular than the rest. (Or should I say, notorious?) The web is filled with monad tutorials, ranging from those using pure mathematics to explain the idea, to those using (half-baked) metaphors like burritos and space-suites. One might think this should make it relatively easy to grok monads. Unfortunately, many of these tutorials do a rather poor job, and do not help students gain an intuition for the idea. This has resulted in an unparalleled FUD around monads.

A part of difficulty people have when learning functional programming abstractions such as monads comes from the fact that, this new way of thinking involves looking for abstraction opportunities in places they’re not used to.

In this talk, I will start by presenting some seemingly unrelated day-to-day code examples. I will underline the problems common to all of them, and then we will all together derive a general abstraction applicable to all of them. If you can follow through till the last bit (and in my experience, most of the audience does), you have not only understood but essentially re-invented monads!

Haskell has populairzed typelcasses a principled way to add ad-hoc extensions on existing data types. They allow you to 1) add new operations on existing data types and 2) support new data types on existing operations, and thus solve the famous "expression problem".

There is a lot of similarily between typeclasses and Java good practices of programming to interfaces and preferring composition over inheritance. The missing link is the implicit dictionary passing which allows haskell to be much more concise and expressive.

In this tutorial, we will look at how Scala adopts typeclasses by adding the missing link of implicits.

You have probably heard of this mysterious language called Haskell. The Haskell code typically looks very different from what we are used to from other languages. The Haskell discussions and blogs mainly seem to center around types and some algebra stuff. Many attempting to learn the language find it befuddling to the point that they give up early in exasperation.

In this talk, I will take you into the mind of a typical Haskeller, and show you how she thinks about programs; how the holy trinity of types, functions, and algebras form the basis of all program design; how this leads to natural evolution of computational abstractions such as monads; how types can sometimes even “write” your programs for you; and how Haskellers manage to sleep more peacefully at night than most of us do!

Some people find it hard to understand Monads (like pointers in C). But it is actually a simple concept (like pointers in C). In this session we try to understand Monads and why they are useful through examples. It is purely from a practical point of view and no theory is covered.