As companies increasingly rely on Large Language Models (LLMs) for code-related tasks, optimizing model selection becomes crucial to balancing performance and cost. This study explores whether static code metrics can predict an LLM's ability to generate effective test cases. By analyzing various Java data structures, this project correlates static complexity metrics with test generation success and code coverage.

A way to use Reinforced Learning to generate more efficient stimulus to not only get more coverage per test, but also cover more sooner within each test. At the same time keep some randomness to have the ability to generate an efficient "Smart" Random test suite that can hopefully replace some bulky pure random tests.

This project uses a combination of feature engineering and binary neural network construction to efficiently process ethernet traffic to detect malware. See how using smaller models can assist in reducing the workload of larger ones.

This article goes into my experiment applying Reinforcement Learning to craft digital logic. Discover the surprising effectiveness of translating Q function tables into logic, offering real-world implementation insights.

This articles goes into depth about a method of creating financial networks using Effective Transfer Entropies, and an algorithm that can be run on top of it to gain a measure of future market volatility.

A look into how graph structure can affect the hardware performance of GNN algorithms.

Do you like competition? Do you like Data Science? Do you like Crypto? Well, I have an exciting distraction for you called Numerai. Hopefully, in this guide I can tell you some about this project, alongside giving you a template to get started in Google Colab.

A site for all your social media analysis needs.

A guide walking through the process of creating new frames to upscale or compress video using Keras Tensorflow.