Haskell in Leipzipg

Detailed Program

Invited talk

Preserving Privacy with Monads

by Alejandro Russo

In a all-connected society, users consciously (or unconsciously) value their privacy. Even skeptical people will recognize its importance; if they do not, ask them to unlock their smartphone and hand it out to someone else—they will most probably refuse! Users want to have control on how their data gets disseminated, specially today when private information gets handled by software with heterogeneous trustworthiness—consider, for example, the various smartphones apps with access to users’ private photos, messages, and contacts that exists today. Unfortunately, current software practices are insufficient to protect privacy: users who wish to benefit from software functionality are often forced to grant access to their private data with no guarantees how it gets handled. The key insight to guarantee privacy is not about granting or denying access to private data, but ensuring that information only flows into the appropriated places.

Information-Flow Control (IFC) is a research area dedicated to protect privacy of data. Based on programming languages techniques, IFC scrutinizes source code to track how data of different sensitivity levels (e.g., public or private) flows within a program, where alarms are raised when privacy might be at stake. IFC tools often provide specially designed compilers to build privacy-preserving apps. Rather than building a compiler from scratch (a major task on its own), Haskell plays a unique privileged role in this scenario: it can provide IFC security via libraries. As long as developers program against the libraries’ API, code is secure by construction. This talk shows how to build such libraries by specially designing monads capable to restrict the propagation of private data. The presentation explores the different techniques used in a wide range of libraries, namely LIO, MAC, and HLIO, where IFC is enforced dynamically (in the form of an execution monitor), statically (by leveraging Haskell’s type-system), and as a combination of both.

Short bio: [Alejandro Russo] is an associate professor at Chalmers University of Technology working on the intersection of functional languages, security, and systems. He is the recipient of a Google Research Awards and several grants from the Swedish research agencies Vetenskapsrådet, STINT, and Barbro Osher foundation. Internationally, Prof. Russo worked on prestigious research institutions like Stanford University, where he was appointed visiting associate professor. His research ranges from foundational aspects of security to developing tools to secure software written in Haskell, Python, and JavaScript.

Alejandro Russo

Contributed Talks

Automated Performance Measurements

by Johannes Waldmann

We reify the API of a library, so we can represent nested calls as terms. Using a smallcheck-like enumeration of terms and contexts, we find problematic API usages that take a lot of time.

As an example, we investigate the standard pretty library, and find quadratic behaviour.

(extended abstract)

Generalized Algebraic Dynamic Programming: Theory and Applications in Bioinformatics and Linguistics

by Sarah J. Berkemer, Peter F. Stadler and Christian Hoener Zu Siederdissen:

Dynamic programming (DP) algorithms are pervasive in bioinformatics and linguistics (and more exotic domains) but typically implemented as “one-shot” solutions.

In addition to just a growing number of applications, there seems to be a trend towards more complex algorithms as well. We recognize at least these degrees of increasing complexity and certain desiderata to control this complexity:

More refined search spaces which require larger sets of rules to describe. Less ad-hoc ways to design these improve confidence in construction. The desire to calculate more than just a single optimal result, be it ensemble-style posterior probabilities or classified dynamic programming benefits from automatic code generation. Many DP algorithms operate on sets, trees, or other non-string data structures. For these more complex structures, both theory and support for implementations are required. On a more basic level, developers have to deal with writing code that should be free of bugs, maintainable, and sufficiently efficient.

In the talk, we will provide an introduction to generalized Algebraic Dynamic Programming which simplifies design and implementation of dynamic programming algorithms considerably. Users of our system can combine individual algorithms like building blocks, have outside algorithms be derived fully automatically, design algorithms on a multitude of input structures (we currently provide implementations for strings, trees, and sets), and even go beyond context-free grammars.

We will focus on recent work on dynamic programming for tree-like data structures. Dynamic programming for tree structures is arguably less well known compared to “canonical text book examples” but has interesting applications in bioinformatics and linguistics, as well as providing easy-to-understand but non-trivial to implement algorithms.

Compared to our earlier work on dynamic programming on strings, or dynamic programming on sets, we now have to deal with input structures that are more complex. Strings are inherently one-dimensional, and recursively decomposed by removing the first or last element, or splitting the string in two. Sets on the other hand are completely unstructured and recursively decomposed by the removal of subsets, or individual vertices. Trees and forests on the other hand are two-dimensional structures, with decompositions into both, subforests of siblings, and subforests induced by the removal of local root nodes during recursive decomposition.

The talk will feature an introduction to a small number of dynamic programming problems on trees and forests with applications in alignment of structuresd RNA, as well as alignment of well-formed grammatical structures (sentences) in human languages.

We discuss single, optimal solutions as well as Inside-Outside solutions which provide answers for the ensemble of all possible solutions for a given problem instance, for example the probability of individual nodes of two trees to be aligned.

We will cover the extension of ADPfusion , which was developed for string-type algorithms, to generalized Algebraic Dynamic Programming and the underlying implementation as an embedded domain-specific language directly in Haskell.

The talk will feature an introduction into the topic of Dynamic Programming based on string distance problems.

HGamer3D - a toolset for developing games with Haskell

by Peter Althainz (also see the corresponding tutorial.)

HGamer3D is a toolset for programming 3D games with Haskell. From the early stages and through some experimental times the project has made progress to a point where some stability has been reached and real games can be created with it. To be useful for a developer such a toolset needs to provide solutions for such seemingly trivial topics as:

bindings to C++ libraries are needed

threading needs to be encapsulated between Haskell and underlying libraries

everything should run on multiple platforms, for example Linux, Windows, Mac

install procedure should be easy and should work flawlessly

games should be easy to distribute

API should be easy to use, should provide good semantics and should bridge OO and functional world

API should be comprehensive enough to be useful, but high level enough to be usable

performance should be sufficient

tooling is needed to handle media and 3D scene creation

For all those topics HGamer3D has some answers but in a short talk like the one presented they cannot be addressed in detail. I also think that most of the audience is probably more interested in a quite pragmatic question: what can I do with it, why should I choose HGamer3D for my next game project, are there any running games programmed with it, if not, why not?

The talk will therefore be split into a small introduction, which just gives an overview of HGamer3D including a simple list of solutions to the topics above and afterwards the main body will be about:

an introduction to the API structure

a Demo of a real game, programmed with HGamer3D

API snippets showing how central features are implemented in the Demo

an overview over the feature coverage, what is implemented for 3D game programming, what is missing

Some API examples, to give you a first impression:

-- create a camera eCam <- newE hg3d [ ctCamera #: FullViewCamera , ctPosition #: Vec3 1 1 ( - 30.0 ), ctLight #: Light PointLight 1.0 1000.0 1.0 ] -- create a cube eGeo <- newE hg3d [ ctGeometry #: ShapeGeometry Cube , ctMaterial #: matBlue, ctScale #: Vec3 10.0 10.0 10.0 , ctPosition #: Vec3 0.0 0.0 0.0 , ctOrientation #: unitU ] -- rotate cube rotateCube eGeo = forever $ do updateC eGeo ctOrientation (\u -> (rotU vec3Z 0.02 ) .*. u) sleepFor (msecT 12 )

To not oversell: no there are no big games programmed with HGamer3D, yet and yes, the demo is still a “toy” example. But it is a fully functional example with sound, input, graphics, gui and gameplay.

The talk will close with a summary, suggesting where HGamer3D is a good fit and where not, it will also try to give some reasoning on the state of the Haskell ecosystem with regards to game programming. Finally an outlook will be shown on possible future directions of HGamer3D.

Management at Algorithmic Financial Markets

by Viktor Winschel

The financial industry, as all sectors of the economies, is about to be swamped with new algorithmic solutions and processes. I will walk through an example of a usual insurance company or bank and explain the involved tasks that can be automated by an integrated algoritmic approach to management towards a more or less fully digital company. Along these lines I will show how our foundational research in the last years between economics, game theory and formal semantics of programming languages can help to tailor a domain specific language for the management at algorithmic financial markets.

Our approach to management at financial markets is three folded: macroeconomics, game theory and smart contracts. We are providing a revolutionary approach to macroeconomic modelling via traditional physical modelling techniques based on analytical and synthetical differential geometry. Besides clarifying some century old modelling problems within macroeconomics this approach also lends itself naturally to an implementation in functional programming languages. The formalization within category theory provides the framework for the modelling of financial market participants like one’s own banks or insurances and those of the compatitors. The strategic decision models within the macroeconomic models are to be based on our new higher-order approach to game theory based on programming language semantics. The third part of our approach to financial markets are smart contracts like those underlying ethereum and bitcoins which model the elements of balance sheets but also the elements of organizational contracts. I will dwell into some details of these topics and try to connect to the underlying mathematical machinery that may be more familiar to the Haskell community.

Essentially we are developing a category theory based DSL for our kind of game theory with the compiler being implemented in Haskell in line with the mathematical foundation of Haskell that is at the core of our formalizations. The new features of our games are compositionality with a formal semantics for the composed behaviour, a visual representation of games by string diagrams adapted from quantum computing, a behavioural abstraction by higher-order functions generalizing utility maximization of standard economics as its main model of behaviour and coalgebraic operators for the infinite repetition of games. The direction of our work, based on our compositionality principle, is to establish a scientific base for macroeconomics and money theory underlying the operation of financial markets. We are currently setting up a start-up company in order to market this research. Haskell enthusiasts are welcomed to join our efforts.

Our company emerged from and is still cooperating with a research community based at UK universities, Glasgow, Leicester, London and Oxford and some others in the Netherland and Germany and Switzerland collaborating at the borders of economics and computer science. This movement took off around 2010 with a first contact of Viktor Winschel to the Oxford Quantum compunting group around Samson Abramsky and Bob Coecke. The work so far has been very foundational with papers and PhD thesis on string diagrams and categorical higher-order game theory. By now we have clarified what can actually be done with these tools within economics and push now into industry. The involved researchers are very enthusiatic in that the surprising common foundations of economcis and computer science opens up various interesting possibilities and applications of high end mathematics in very practical fields.

The format of my presentation will be a usual slide show. The indented audience are Haskell programmers who are also interested in the common mathematical structures of programming language semantics and economics and game theory. Even so I will not dwell into very detailed and formal category theory or coalgebras it might be beneficial to see the point of basing economics and computer science on this foundation. Also there might be Haskell programmers who are interested in joining the developing FinTech industry in Frankfurt and possibly colaborate with or join our startup company.

For further information, look at these conferences and papers:

Project report: building a web-application with servant, lucid, and digestive-functors

by Matthias Fischmann and Andor Pénzes:

Servant is a library for writing HTTP routing tables on the type level. It is commonly used for micro-services and for delivering json data to single-page apps, but it is possible to use it for delivering web pages and forms, and build a low- or no-js web application with it.

Aula (code, blog of the pilot project) is such a web application. It is based on servant for request processing, lucid (a sibling package of blaze) for html content rendering, and digestive-functors for web form processing. Sticking these parts together proved both non-trivial and very rewarding. Benefits are clear separation between application logic, html rendering, form data validation, and tedious details like authentication or CSRF token handling.

Aula is AGPL. We plan to refactor parts of the code into general-purpose libraries.

In this talk, we look at pieces of the Aula code base and discus strengths and weeknesses of our approach. We will also give a summary of our experience with Haskell tooling and deployment.

Dependently Typed Heaps

by Lars Brünjes

Using the newest type­level features in GHC, it finally becomes feasible to try some more serious dependently typed programming in Haskell and to even prove mathematical theorems and have their proofs checked by Haskell’s type checker.

My talk is intended to serve as an introduction to such techniques and as an example of how to express reasonably complex invariants on the type level. The talk should be accessible for Haskellers at an intermediate level, who don’t need any special prior knowledge on dependent types.

As an introduction, demonstration and proof of concept, I would like to present my little toy project on Dependently Typed Heaps, where I implement leftists heaps (following Chris Okasaki’s Purely Functional Data Structures), where both the heap invariant and the leftist property are statically ensured by the type system. (In my talk, I will of course first explain what those terms mean and how traditional leftist heaps work.)

To encode those invariants in the Haskell type system, typelevel natural numbers have to be defined, and several simple theorems have to be proved for them.

To this end, instead of directly working with a specific encoding of natural numbers, the abstract concept of “type­level natural number” will be defined by a type class, for which two implementations will be provided:

simple, unary “Peano” numbers, which are easy to define and understand, but which are also unfortunately inefficient, and

binary numbers, which are more efficient, but also a bit more complicated.

We can prove various properties of such natural numbers (decidability of the order relation etc.) in Haskell in order to provide a heap implementation with strong type­level guarantees at compile time.

My benchmarks indicate that the resulting algorithm is reasonably efficient in the sense of complexity, apparently still being of time complexity class O(n log n) like the original algorithm, but they also show that safety comes at a steep price: The “unchecked” algorithm is at least an order of magnitude faster than my “safe” algorithm.

Random access lists, nested data types and numeral systems

by Balázs Kőműves

Random access lists, as introduced by Okasaki , are persistent list-like data structures with faster lookup than linked lists. This talk will be an explanation of these data structures and their connection to numeral systems, with a small twist. I claim no originality at all.

The core idea is that you can take a positional numeral system, like the usual binary, ternary etc. numeral systems (or more esoteric ones, like the skew binary system), and “categorify it”, turning the length of a list (a number) into an actual sequence data type. In place of the digits you will have tuples of (full) trees: For example in the decimal system, if the digit at the hundreds place is 3, that will translate to a triple of 10-way trees, each containing 100 elements at the leaves.

The usual linked list corresponds to the unary system; but with the non-unary systems, you get O(log(k)) lookup, while modifying the left end of the list still remains on average O(1) (worst case is typically O(log(n)), though guaranteed O(1) can be achieved too). They can be also more compact in memory than linked lists (with GHC’s in-memory representation).

Okasaki used the standard tree representation, which is one of the first data types one meets when learning Haskell; for example, a leaf binary tree is represented as

data Tree a = Leaf a | Node ( Tree a) ( Tree a)

However, full binary trees can be also represented by a nested data type:

data Tree' a = Single a | Double ( Tree' (a,a))

The word nested refers to the fact that the type parameter changes during the recursion. Functions operating on such types require polymorphic recursion, an interesting language feature. The nested binary tree has two advantages over the usual representation: First, the type system guarantees that the tree is full (that is, every leaf has the same depth); second, since it lacks the extra indirection at the leaves, it takes less memory (by two machine words per element, with GHC). While the extra indirection can be also eliminated by modifying the original tree definition, that would result in less elegant and more complicated code.

This idea can be applied to our list-like data structures, giving for example the following very simple representation of leaf binary random-access lists:

data Seq a = Nil -- the empty sequence | Zero ( Seq (a,a)) -- a sequence of even length | One a ( Seq (a,a)) -- a sequence of odd length

The implementation of the usual list operations is also very straightforward and simple (a Haskell implementation can be found at ). The same idea can be also applied to the other variations. The resulting data structures are somewhat reminiscent of finger trees , which are also implemented using nested data types; however, these are much simpler (of course, finger trees support more types of efficient operations).

A variation of the above idea uses the so-called skew numeral systems; these allow (modulo implementation details) guaranteed O(1) cons, in exchange for a slightly more complicated implementation. The resulting data structures use trees with data on both the nodes and the leaves (which also results in more compact in-memory representation). Okasaki uses the skew binary number system.

Plugin Architectures in Haskell

by Sebastian Graf

Creativity and discontent gave rise to a number of different approaches and even more libraries for customizing applications written in Haskell. Most of them rely on the GHC API in one way or another, so over time many of said libraries have gone obsolete or superseded by another contender. As evidenced by posts on reddit and Stack Overflow, that led to huge confusion in the community, to a point where no-one has a clear overview of the current possible alternatives for extending a Haskell application after compilation.

This talk will try to give an overview of available means to loading and executing (mostly Haskell) code (mostly) at runtime, discussing its impliciations on type safety and versioning. Packages will be categorized by the taken approach and the most prominent proxy per category will have to serve for some live coding adventures. The pitch:

Embedding a scripting language via lua bindings and hint

XMonad and Yi style configuration via Dyre

Loading separately compilated Haskell code from a shared library with dynamic-loader

The goal is to collect resources in a common place and present the results in a manner fit for a talk, while an accompanying blog post will go into more detail to also separate similar approaches.

Store: An Efficient Binary Serialization Library

by Philipp Kant

Serialization, the process of converting structured data into a sequence of bytes, is a necessary step in many software programs. When designing a serialization library, there are many trade-offs that have to be considered:

Is interoperability with other languages required?

What about interoperability between different hardware architectures?

Backwards compatibility: is it possible to make additions to the serialization format without invalidating previously serialized data?

How important is the size of the resulting serialization

How important is the speed of serialization and deserialization?

How easy is the library to use? How much code has the user to provide in order to support serialization of a custom data type?

The Haskell ecosystem features a selection of serialization libraries, each of which make different choices for the trade-offs above.

We present store, a recent addition to the line-up of serialization libraries that prioritizes heavily on speed. It was designed with the main use-case of distributed high-performance computing in mind. By cutting back on features that are not necessary in that context, and by exploiting assumptions about the typical data payload, it succeeds to provide routines for serialization and deserialization that are faster than those of the libraries that cover the general case.

For example, store assumes that

There is no need to provide compatibility between different architectures and/or versions of the serialization scheme. In particular, store can use the host byte order, instead of converting to a fixed endianness. This allows for code that is both simpler, and more efficient.

The size of the serialized data can easily be determined prior to serialization, and is small enough to fit into memory. Thus, store can allocate the whole memory for serialization in one step, without needing to grow buffers.

Store has pre-defined instances for most common datatypes. For convenience, the library provides methods to implement custom instances using Generics or template Haskell. It also features a thin streaming layer that allows incrementally consuming input from streaming sources, such as a network connection.

This talk will give an overview of the design principles behind store, and demonstrate that they lead to efficient code.

Csound-expression Haskell framework for computer music

by Anton Kholomiov (also see the corresponding tutorial.)

The paper presents modern Haskell framework for creation of computer music. It’s called csound-expression. It’s EDSL for Csound audio programming language. The functional programming paradigm can greatly enhance the process of text-based music production. We are going to look at functional model for computer music. It combines many aspects of computer music. With the library we can create instruments from scratch apply instruments to scores, trigger them with streams of events. We can create UIs to control our music.

The key guiding principle of API design is modularity. Creation of self-contained musical objects that can provide building blocks for the tower of abstractions. The library is designed in such a way that every concept is self contained and the whole program can be built from expressions. This feature allows us to construct music in the interactive style. We can create music in the REPL.

Simple blog engine with shape functors and generic eliminators for ADTs

by Andor Pénzes

Processing complex information via generic eliminators (GE), type holes, algebras and catamorphisms for ADTs are tools in the functional programming toolbox. I would like to present a use case where combined approach of those tools is pushed to the limit. As an experiment, a simple blog engine is implemented. The result shows how a developer can concentrate on solving particular problems, how algebras and catamorphisms support the separation of concerns and readability, how type holes gives extra power to the abstractions.

Besides the current example, we can make comparison between different approaches, using lenses or not using generic eliminators et al.

Tutorials

Efficient signal processing using Haskell and LLVM

by Henning Thielemann

Haskell has so many features that are useful for signal processing: Lazy evaluation allows for infinite lists, simultaneous processing of a sequence of signal transformations and feedback, monads allow for proper handling of coherent and incoherent sources of noise, the type system allows for handling of sample rates via phantom types and for hiding internal filter parameters in opaque types. However, especially when relying on a lot of laziness your Haskell programs will be pretty slow in any Haskell implementation. In order to get reasonable processing speed you need to switch from lazy lists of sampled displacements to lazy lists of chunks of unboxed arrays. This compromise is pretty close to how signals are usually stored in signal processing packages written in machine oriented languages. However, GHC still fails to generate efficient code in cases that are hard to predict. It may be due to missing inlining, too much sharing of functions or too much laziness.

With the package synthesizer-llvm we address the issue of efficient signal processing. We use the LLVM Just-In-Time compiler in order to perform signal processing with maximum speed. LLVM provides optimization passes and vector instructions that enable us to achieve this goal. Our package also provides the programmer precise control over strict evaluation, code and data duplication or sharing.

In the tutorial we examine the examples included in the package, experiment with them and learn about the basic concepts of the package this way. The examples include:

Signal producers like oscillators and noise generator,

Frequency filters and how to work with internal filter parameters beyond the restricting scheme of audio and control rates found in other digital signal processing frameworks,

Sample value types: Stereo sounds, binary logic signals, fixed-size arrays

Causal signal processes with arrows, sharing and feedback with proven absence of deadlocks,

Express arrows by plain functions,

Composing sequences of sounds, composing music from tones,

MIDI control of sounds,

Integration with ALSA and JACK,

Vectorisation.

I already gave a tutorial on LLVM at HaL-9 and chose simple signal processing as application. In this tutorial I will ignore the details of LLVM and just concentrate on the signal processing part. I do not plan to give an extensive introduction to signal processing, the focus is on how to get things done with synthesizer-llvm .

Since LLVM changes slightly with every release my LLVM bindings got a bit out of date. I have only recently updated them. Please follow the instruction hints before attending the tutorial. We will not have time to fix issues in the tutorial. If you want to be notified about short-term updates, please send me your e-mail.

References:

synthesizer-llvm package at Hackage

Paper “Compiling Signal Processing Code embedded in Haskell via LLVM” at arXiv

Demo of a song that is played live over a synthesizer-llvm based software synthesizer.

Workshop: creating computer music with Haskell

by Anton Kholomiov (also see the corresponding talk.)

At the Workshop we are going to learn to create computer music with Haskell. We are going to produce buzzes and blips right in the REPL. Right in the ghci you can create a midi-based instrument and complement it with drums and harmony from your favorite library of samples or create the melody from scratch with scores or event streams. Also we are going to learn how to create the UI-controllers on the fly with applicative style programming.

At the end of the workshop you should become en-armed with set of pro-quality synthesizers, tools to create sample based music, trigger them the music in real time. We should discuss some features unique to text-based programming. Have a glimpse at techniques used in musical generative art.

We are going to use the library csound-expression. It’s a Haskell framework for sound design and music composition. It embeds very powerful audio programming language Csound into Haskell. It’s a Csound code generator. The Csound is 30 years old audio programming language that embodies the wisdom of many researchers. It has active community and it continues to evolve. It has the wide set of features (subtractive, granular, waveguide, sample-based synthesis, physical modeling, spectral processing, emulators for many analog synthesizer components)

The Haskell library complements the Csound with expressive language. We can use the great low-level audio units of Csound and scheduler with higher level features of Haskell like higher order functions, rich data type system, body of libraries for advanced data types.

Prerequisites:

To follow along you need to install several things:

csound-expression. It’s a main library (available on hackage).

Csound compiler. The thing that converts code to audio (available on official site works on all platforms).

other goodies based on core library: csound-catalog and csound-sampler. They contain ready to use high quality patches (available on Hackage) and tiny DSL to compose music with audio-clips.

the archive with audio files (drum loops and pads) that we are going to use. I’ll publish it when closer to the date.

When everything installed check that it works. Open ghci import the module Csound.Base and type:

> dac (testDrone3 220)

Hit Ctrl+C to stop the playback. The library has minimal Haskell dependencies and should be easy to install. If something will go wrong with installation drop me a line on github or on the email that you can find on Hackage page of the project.

The library is really huge though the core is very simple and minimal. So I recommend to read/skim through the user guide of the library. That way you can get more out of the workshop.

Ten example uses of monads

by Philipp Schuster

This is a workshop for Haskell beginners that motivates monads. In this “monad tutorial” we are not going to learn what monads are, how they are implemented or how they work internally. After a quick introduction to the syntax we are going to look at ten practical use cases. We will look at a selection of concurrency, resource management, build dependencies, stream processing, distributed programming, probabilities, server programming, database queries, mutable references, search, test specification and parsing.

I provide a repository on github with a small example for each monad discussed. If you want to follow along you should clone that repository and start from there. To compile the examples you can use either cabal or stack. We will go through each example to motivate the used monad. Then we will extend each example with a small feature.

We will keep selecting examples from the following list until the time is up.

STM: Software transactional memory for concurrency.

Resource: Automatic resource management.

Action (shake): GNU make embedded in Haskell.

Pipe: Stream processing.

Process: Distributed programming.

Probability: Probability distributions.

Scotty: Web server framework.

Relation: SQL embedded in Haskell.

ST: Mutable references.

Logic: Backtracking search.

Spec: Test specification.

Parser: Parse binary and textual data.

Canvas: Draw on HTML 5 Canvas.

Those examples hopefully demonstrate the broad applicability of the monad concept.

HGamer3D - do it yourself

by Peter Althainz (also see the corresponding talk.)

During the tutorial we will install HGamer3D development tools on your computer and go through a number of exercises, along the following path:

Install tooling, including compiler, graphic tools, editor and HGamer3D (15 min)

The 3D coordinate system and API (10 min)

Item Hierarchies (5 min)

Getting media into your game (10 min)

Break (10 min)

Challenge: “program your own game”: given a vision of a game and essential code snippets to program certain features, you are left on your own to build a running game (30 min)

Distribute your game (10 min)

Prerequisites: Linux, Windows or OS X installed. Internet connection to download the tooling.