Projects

chrisloy.dev

unthink | remove cognitive noise

identify cognitive biases and logical fallacies

Cognitive bias and logical fallacy cards.

chrisloy.dev

fractavibes | generative art

Generative art algorithms that run in your browser

Procedurally-generated interactive art.

chromavibes.net

chromavibes | color palette generator

AI-powered colour picker for beautiful colour palettes.

Interactive colour palette generator.

vercel.app

Carbon Foodprint

Tool for calculating carbon footprint of recipes, using open data platforms.

anneandchris.info

Anne & Chris

Website with photos and RSVPs that I put together for my wedding.

GitHub

GitHub - scala/scala-xml: The standard Scala XML library

The standard Scala XML library. Contribute to scala/scala-xml development by creating an account on GitHub.

I made some contributions to the Scala standard library.

GitHub

GitHub - chrisloy/akka-ec2: Example setup of an Akka cluster in an Amazon EC2 AutoScaling group

Example setup of an Akka cluster in an Amazon EC2 AutoScaling group - chrisloy/akka-ec2

World's first working implementation for running Akka on EC2, accompanying my slightly viral blog post.

GitHub

GitHub - chrisloy/lisc: List Interpretation in Scala

List Interpretation in Scala. Contribute to chrisloy/lisc development by creating an account on GitHub.

A toy Lisp interpreter that I wrote, implemented in Scala.

GitHub

chrisloy - Repositories

chrisloy has 24 repositories available. Follow their code on GitHub.

Various other open source contributions over the years.

google.com

GB2602138A - Methods and systems for evaluating content - Google Patents

Methods and systems of evaluating content using machine learning models such as neural networks and Bayesian models, to predict the consumer interaction rate of the content, based on historical consumer interaction rates and contextual meta data of historical content. Comprising receiving image and/or text content data and associated contextual metadata, pre-processing the data to determine feature vectors, processing the vectors and metadata in a machine learning model, e.g. a neural network, to rank the content relative to the historical (training) content’s interaction rate, outputting a probability that is based on the new content’s predicted rank, inputting it to a Bayesian model in combination with additionally received metadata relating to the content to calculate and output a posterior probability that represents the predicted consumer interaction rate of the content. Many pieces of content may be analysed, and the result may be used to customise content distributions, such as marketing and advertising material, to target customers with tailored content that is more likely to provoke an interaction. As the result of content use is recorded, this data can be used to update the prediction. Computer vision and/or natural language processing may be utilised for pre-processing content.

Pending patent from Datasine.

google.com

US20240354455A1 - Asset design and generation using digital assistant - Google Patents

As disclosed herein, a computer-implemented method for refining a description of a desired digital asset through interactive conversational exchange is provided. The computer-implemented method may include receiving, via a conversational user interface (UI), a first input from a user including a description of a desired digital asset. The computer-implemented method may include prompting the user to provide a second input including additional details about the desired digital asset. The computer-implemented method may include generating, based on the second input, a first refined description of the desired digital asset. The computer-implemented method may include providing the first refined description to a machine learning (ML) model to generate the desired digital asset. A system and a non-transitory computer-readable storage medium are also disclosed.

Granted patent from Shutterstock.