When I say "Multiplayer online game development in Clojure" 2 questions probably pop right up : WHY? HOW? Why? because you can do it in under 100 lines of code, and it is pure FUN. How? Well that's exactly what we'll talk about in this session. I will present a simple MOG written in Clojure and go through each line of code – so you'll understand how you can do that yourself, even if you've never written a single line of Clojure before. Whether you're a real Clojurian at heart or just interested in hearing a talk about Clojure from a sworn star wars fan – this talk is for you 🙂 PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=f6KL0Kbq-5o https://www.youtube.com/watch?v=PWro7ifA25Q

Wasm was designed to run applications written in compiled languages such as C/C++, Rust, Swift, etc. However, as Wasm gains popularity, there are increasing demands to run Wasm applications in scripting languages such as JavaScript, Python and Ruby. Compared with native interpreters (or dynamic compilers), Wasm offers benefits to both devs and ops. Dev: Wasm is a polyglot environment that supports mixing high-performance compiled languages and easy-to-use scripting languages. For example, with Wasm, devs can safely wrap Rust functions in a JS API. Op: Wasm is a sandbox with OS access. It can be managed as a standalone container or be embedded in a host. Native scripting language VMs need to be wrapped in other runtimes (eg node) and Docker containers. Wasm can achieve significant savings in computing resources. In this talk, Michael will discuss the approaches and latest progress of Wasm support of scripting languages, like JS, Python, and Ruby. He will cover language interoperability, ecosystem (eg packages and modules) support, and performance characteristics. Finally, Michael will also briefly discuss Wasm support status for popular managed languages such as Java and .Net. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=TBs0MYmtgGI https://www.youtube.com/watch?v=y9KD5Ad3tMo

To use our favourite supervised learning models for time series forecasting we first have to convert time series data into a tabular dataset of features and a target variable. In this talk we’ll discuss all the tips, tricks, and pitfalls in transforming time series data into tabular data for forecasting. Slides: https://github.com/KishManani/PyDataLondon2022 PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV https://www.youtube.com/watch?v=KrbV75Mby5E