Kristian Gaylord's repositories
MelodyGenerator
We wanted to create a song using an AI generated melody and lyric. Herein lies the enabling code.
simple-rlp
Simple RLP (Recursive Length Prefix) - Encode the and decode data structures simple and fast
TurbulencePrediction
Clear air turbulence (CAT) costs airlines over $500m in maintenance and medical bills. This repository contains code that explores the application of modern deep learning for CAT localization and prediction.
actix-web
Actix Web is a powerful, pragmatic, and extremely fast web framework for Rust.
core
High-performance, well-tested & documented core libraries for Ethereum, in Rust
OptimalPortfolio
This program collects and analyzes financial data in order to suggest an optimal portfolio given a desired return.
PaymentProtectionTool
Helps small businesses calculate how much they can apply to borrow under the new Payment Protection Plan.
RobinhoodRebalance
Automating trades in Robinhood. Designed specifically for rebalancing allocations.
dydx-v3-python
Python client for dYdX (API v3)
ethers-rs
Complete Ethereum & Celo library and wallet implementation in Rust. https://docs.rs/ethers
foundry
Foundry is a blazing fast, portable and modular toolkit for Ethereum application development written in Rust.
hummingbot
Hummingbot is open source software that helps you build trading bots that run on any exchange or blockchain
kristian1108.github.io
I'm just messing around with Tensorflow JS to see how to host models online.
ledger-eth-lib
Library to interface with Ethereum app on Ledger hardware wallets
local-ip-address
Retrieve system's local IP address and Network Interfaces/Adapters on FreeBSD, Linux, macOS and Windows
sewerninjaforellen
This is Ellen McDermott's homemade Game Maker Studio 2 game.
trezor-firmware
:lock: Trezor Firmware Monorepo
YT8M
Drawing inspiration from recent advancements in video labeling and recommender systems, this project aims to develop a multi-staged approach to large-scale facial recognition in videos. In pursuit of this, models were developed and tested to understand context from videos, which in turn can be used to narrow down the likely set of faces appearing therein. In doing so, this model architecture could be useful in improving the effectiveness and efficiency of facial recognition software. The data used to develop these models came from the YouTube-8M and CelebA datasets. In order to support the ability to iterate and experiment quickly, a wrapper class for the YouTube-8M dataset was developed and shared publicly for others to use in their own research. The results of this project indicate promise for the concepts being tested, but further development is required to bring it to full fruition.