A stateful ui library
type A { f1: String }
f1 animation parsing functions for the dom
React UI animation components built on top of f1-dom and f1.
[](https://github.com/z0mt3c/f1-telemetry-client/actions/workflows/node.js.yml) [![NPM Release](https://img.shields.io/npm/v/@z0mt3c/f1-telemetry-client.sv
f1 base componment
F1 Core - Form specification and types
<img src="https://img.shields.io/npm/v/@racehub-io/f1-telemetry-client.svg"> <img src="https://img.shields.io/github/license/racehub-io/f1-telemetry-client.svg"> <a href="https://snyk.io/test/github/racehub-io/f1-telemetry-client?targetFile=package.json
Typed Methodology SDK -- makes the formal theory (F1-FTH) executable in TypeScript.
Npm package wrapping the jolpica f1 api
Pure scoring algorithms for AI session search: TF-IDF WLCS over tool sequences, resource F1, PageRank, recency.
The F1 series of games support the outputting of key game data via a UDP data stream. This data can be interpreted by external apps or connected peripherals for a range of different uses, including providing additional telemetry information, customised HU
F1 Delta Time Solidity Contracts
This is a TypeScript UDP client and telemetry parser for Codemasters F1 2021 game that enables the consumption of such information.
F1 JWT generate
Ultra-lightweight Korean morphological analyzer for the web (1MB model, WASM, F1 93.7% NIKL MP)
A data-driven browser F1 simulator component for host websites.
A command line utility which retrieves the latest Fantasy F1 results and analyses all possible constructor and driver combinations and suggests an optimum Fantasy F1 Team.
Nodejs F1 score (also called F-score or F-measure)
MCP server for Fastlytics F1 telemetry and historical data
TypeScript definitions for f1
Lego bricks scatter, snap into an F1 car, and drive off — a loading animation with start()/complete()/reset(). Zero dependencies.
Fellowship One (F1) API wrapper for Node.js
NodeJS API to get F1 Stats.
Get historical and live Formula 1 (F1) data
This crate offers procedural macros designed to facilitate the swift implementation of Rust's built-in traits, temporarily used in databend
This crate offers procedural macros designed to facilitate the swift implementation of Rust's built-in traits.
Convert binary data from F1 24, F1 23, and F1 22 UDP telemetry into organised structs.
F1 Nexus CLI - Command-line interface for F1 strategy optimization
F1 Nexus NAPI-RS bindings for Node.js
F1 API is a client library for the telemetry API of the F1 video games by Codemasters. It uses asynchronous networking to decode the incoming UDP packets, and turns them into strongly typed Rust structs.
Model Context Protocol (MCP) server for F1 Nexus
F1 Nexus WASM modules for browser deployment
This crate provides declarative macros to help you implement the `Debug` trait manually.
Next-generation Formula 1 strategy optimization platform with AI-powered race simulation
Provides consolidated access to various sources of Formula 1 information and data.
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A Ruby wrapper for the ergast F1 API
Parses raw events from the Formula1.com live timing stream
Get F1 results from formula1.com
Coin Sorting using the greedy algorithim. Bugs, comments and questions can go here: http://gembugs.forumotion.com/f1-bugs-comments-questions
Allows renumbering non-sequential files sequentially. For example f00.txt, f03.txt, f99.txt -> f0.txt, f1.txt, f2.txt. Usage: renumber directory_name [prefix] [suffix]
Consumes the Fellowship One API in your apps using ActiveResource. Implements 2nd party credentials-based authenticaion and full OAuth implementation.
A cruel mistress that uses the public suffix domain list to dominate URLs by canonicalizing, finding the public suffix, and breaking them into their domain parts.
A gem to show finding results repository github on your terminal
The will_paginate library provides a simple, yet powerful and extensible API for pagination and rendering of page links in web application templates.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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