diff --git a/.vscode/settings.json b/.vscode/settings.json index 61a4ec6..a086a80 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -9,6 +9,7 @@ "decla", "EEPROM", "favicons", + "favourite", "forex", "froms", "Glsl", diff --git a/src/data/portfolio.ts b/src/data/portfolio.ts index 6dcba26..2925241 100644 --- a/src/data/portfolio.ts +++ b/src/data/portfolio.ts @@ -34,7 +34,7 @@ export const portfolio: Array = [ "I'm passionate about architecting and building large-scale systems, especially in the context of AI/ML. However, in my free time, I also enjoy working with shaders, data visualisation, and sometimes even microcontrollers.", - "Discover some of my more exciting projects below. If you'd like to reach out to me, my contact details are at the bottom of the page.", + "Discover some of my more exciting projects below. And if you'd like to reach out to me, you can find my contact details at the bottom of the page.", ], }), diff --git a/src/data/projects/declared.ts b/src/data/projects/declared.ts index 0a5754c..2b1839e 100644 --- a/src/data/projects/declared.ts +++ b/src/data/projects/declared.ts @@ -7,19 +7,17 @@ import { GitHub, Open, Thesis } from '../shared'; export const declared: TimelineElementParameters = { title: 'Multiplayer game', date: '2020 autumn', - figure: new Preview( - declaredPoster, - 'https://decla.red', - 'The website of the video game' - ), + figure: new Preview(declaredPoster, 'https://decla.red', 'The UI of the video game'), description: - 'Using SDF-2D (my ray tracing graphics library), I created a conquest-style multiplayer browser game. It even runs on mobiles.', + 'I created a conquest-style online multiplayer browser game using my ray-tracing library (see below). It even runs on mobiles.', more: [ - 'The scene is set in space, two teams have to conquer small planets, while they can also shoot at the other team. Points are given based on the number of planets controlled, and the first team which reaches a predefined score wins.', + 'The scene is set in space. Two large teams have to conquer tiny planets, while they can also shoot at the other team. Points are given based on the number of planets controlled, and the first team which reaches a predefined score wins.', - 'As for the communication, a server-client architecture is used. Messaging is provided by Socket.IO and a custom serialisation solution.', + "The architecture consists of multiple servers and clients (communicating over WebSockets); Firebase is used to reach consensus on the active servers. The project uses Typescript compiled into a website and a Node application. There is a shared library which contains the game logic. This way, both the client and server can link to this library, allowing to use of the same code for calculating the actual next state on the server and client-side-predicting it on the users' devices.", - 'This (along with SDF-2D) was my BSc thesis project, so more in-depth information about them can be found in my thesis linked below.', + 'My favourite part of the project was handling the increasingly complex and heavy-weight game logic. To tackle the former, I decided to borrow inspiration from Smalltalk\'s message passing, including the concept of "messageNotUnderstood". To improve the performance, I implemented k-d trees to decrease spatial operations\' complexity.', + + 'This game (along with SDF-2D) was my BSc thesis project, so more in-depth information about them can be found in my thesis linked below.', ], links: [ GitHub('https://github.com/schmelczer/decla.red'), diff --git a/src/data/projects/great-ai.ts b/src/data/projects/great-ai.ts index 66a1231..ab1d17d 100644 --- a/src/data/projects/great-ai.ts +++ b/src/data/projects/great-ai.ts @@ -13,15 +13,15 @@ export const greatAi: TimelineElementParameters = { container: true, }), description: - 'I investigated an approach to increase the adoption rate of ML deployment libraries and hence the overall quality of industrial deployments. I did this by simultaneously focusing on providing robust, automated implementations of best practices and an accessible API. One of the outcomes of the research is the GreatAI framework.', + 'I investigated an approach for increasing the adoption rate of ML deployment libraries and hence the overall quality of industrial deployments. I did this by simultaneously focusing on providing robust, automated implementations of best practices and an accessible API. One of the outcomes of my research is the GreatAI framework.', more: [ 'Applying AI is becoming increasingly more accessible, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and the introduction of unintended biases.', - 'The research presents 33 AI/ML deployment best practices (while introducing 6 new), the difficulties of implementing them, and ways to overcome these challenges. These target the transition from prototype AI code into production-ready software. GreatAI helps implement these best practices through an accessible interface.', + 'My work presents 33 AI/ML deployment best practices (while introducing six new ones), the difficulties of implementing them, and ways to overcome these challenges. GreatAI helps implement these through an accessible interface.', 'Feedback from professional data scientists and software engineers showed that ease of use and functionality are equally important in deciding to adopt deployment technologies, and the proposed framework was rated positively in both dimensions.', - 'For more details, checkout the GitHub page or the paper.', + 'For more details, visit the GitHub page or the paper.', ], links: [ PyPi('https://pypi.org/project/great-ai/'),