Talks
Speakers
Events
Topics
Sign in
Home
Talks
Speakers
Events
Topics
Leaderboard
Use
Analytics
Sign in
Suggest modification to this talk
Title
Description
RubyConf AU 2018 | Sydney | Australia March 8th & 9th, 2018 Organisers: Melissa Kaulfuss (@melissakaulfuss), Nicholas Bruning (@thetron), Sharon Vaughan (@Sharon_AV) & Nadia Vu (@nadiavu_) MCs: Melissa Kaulfuss & Nicholas Bruning Sponsored by: Envato, Culture Amp, Lookahead, Reinteractive, Oneflare, Shippit, Twilio, The Conversation, Netflix, Disco, Heroku, REA Group
Date
Summarized using AI?
If this talk's summary was generated by AI, please check this box. A "Summarized using AI" badge will be displayed in the summary tab to indicate that the summary was generated using AI.
Show "Summarized using AI" badge on summary page
Summary
Markdown supported
### Summary of 'High Performance Mario Kart On Ruby' In this engaging presentation at RubyConf AU 2018, Michael Morris explores the intersection of gaming and software development through his team’s creation of a comprehensive leaderboard application for Mario Kart. This project stems from the necessity of software developers to quantify their gaming experiences amidst their serious work environment in banking. **Key Points Discussed:** - **Gaming as a Relaxation Tool:** Michael introduces the relevance of playing Mario Kart as a fun escape from the seriousness of his banking job. - **Development of a Leaderboard Application:** The team aimed to find out who excelled at Mario Kart by building a leaderboard app that posts results on Slack, utilizing a simple Elo rating system to track wins. - **Challenges with Multi-Player Scoring:** Recognizing challenges with four-player games, the team transitioned to the TrueSkill algorithm, allowing for a more accurate ranking between multiple players. - **Enhanced Functionality:** The application was advanced to include the inspection of individual player stats, tracking victories, and plotting performance over time. - **Using Images and Data:** The presentation outlined a three-step system: getting an image from the Nintendo Switch, analyzing it to extract gameplay data, and reconstructing the game's state using this data. - **Technical Implementation:** Michael describes how different devices were used to fetch video streams from the Switch and how Ruby was employed to analyze these images to identify player positions and item information during races. - **Community and Collaboration:** He emphasizes the importance of teamwork and the Ruby open-source community in the project’s success. **Examples and Illustrations:** - A demo with live players showcased the application in action, demonstrating its ability to track ongoing matches and display race results. - The use of Google Chart Image Drawing API allowed for graphical representations of player statistics, adding an engaging visual element to the leaderboard. **Conclusions and Takeaways:** - The project illustrates innovative applications of Ruby outside of traditional uses, inspiring developers to look for creative ways to implement software in recreational contexts. - Michael expresses appreciation for the support from his employer and encourages those interested in similar developments to explore their ideas further, noting that the project’s code is accessible for others to experiment with. - The talk showcases the value of combining passion with technical skills, fostering a culture of creativity and playful competition among developers.
Suggest modifications
Cancel