Deploy your project through SSH, qiniu or ali-oss, using your local machine.
Utilities for creating robust overlay components
JSON.parse with context information on error
JSON.parse with context information on error
The most comprehensive authentication framework for TypeScript.
A better opn. Reuse the same tab on Chrome for 👨💻.
Human-friendly JSON Schema validation for APIs
The fastest and simplest library for SQLite in Node.js.
A better path.resolve() that normalizes paths on Windows
The most comprehensive authentication framework for TypeScript.
Advanced fetch wrapper for typescript with zod schema validations, pre-defined routes, hooks, plugins and more. Works on the browser, node (version 18+), workers, deno and bun.
Telemetry package for Better Auth
Kysely adapter for Better Auth
auto deploy
Set of deployment tasks for Shipit based on git and rsync commands. [upgrade for large project]
Prisma adapter for Better Auth
Memory adapter for Better Auth
Drizzle adapter for Better Auth
Mongo adapter for Better Auth
TypeScript definitions for better-sqlite3
auto-wraps tailwind classes after a certain print width or class count into multiple lines to improve readability.
JSON Schema validation for Human
JSON Schema validation for Human
Lightweight Result type with generator-based composition
You better stab yourself if you deploy on friday!
Cannery helps you deploy better with JRuby.
This gem modifies capistrano recipes to allow deploys to windows machines. Several nuances such as the lack of symlinks make the deploy a little different, but it's better than doing it by hand. See the github page for instruction on how to set up Windows to get it ready for a deploy.
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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