Computes the L2 norm (Euclidean norm) of an array of values.
[Optimism] L1 and L2 smart contracts for Optimism
L2 AWS CDK Constructs for Amazon Verified Permissions
Build, sign, broadcast, batch, and manage Stacks L2 transactions
Deploy, verify, and manage Stacks L2 smart contracts
A CDK L2 Construct Library for VPCLattice
Contract read/write wrappers and ABI helpers for Stacks L2
The archiver fetches onchain data from L1 and stores it locally in a queryable form. It pulls: - **Checkpoints** (containing L2 blocks) from `CheckpointProposed` events on the Rollup contract - **L1-to-L2 messages** from `MessageSent` events on the Inbox
React bindings for l2-table
StacksRank SDK — Decentralized ranking, analytics, and smart contract toolkit for the Stacks Bitcoin L2 ecosystem
Deploy, verify, and manage Stacks L2 smart contracts
Achievement POAP SDK on Stacks (Bitcoin L2)
Wallet connection, signing, and session management for Stacks L2
[Mantle] L1 and L2 smart contracts for Mantle
Contract read/write wrappers and ABI helpers for Stacks L2
Contract read/write wrappers and ABI helpers for Stacks L2
Type definitions, constants, and network configs for Stacks L2 development
JavaScript/TypeScript SDK for POSVault — Treasury Vault & DAO on Stacks (Bitcoin L2)
Build, sign, broadcast, batch, and manage Stacks L2 transactions
Wallet connection, signing, and session management for Stacks L2
Type definitions, constants, and network configs for Celo EVM and Stacks L2 development
Contract read/write wrappers and ABI helpers for Stacks L2
Hop is unique in that it's one of the only non-native bridges that uses the hub&spoke model. Because of this it basically has 3 types of transactions: 1. L2 to L1 1. L1 to L2 1. L2 to L2
Four-layer local memory system plugin for OpenClaw — auto-captures, structures, and profiles conversational knowledge using local LLM + SQLite vector search (L0→L1→L2→L3 pipeline)
L2 is a Pytorch-style Tensor+Autograd library written in Rust
Find the first Err in Iterator<Item = Result<T, E>> and allow iterating continuously.
Multi-tier caching system (L1/L2/L3) for LLM Edge Agent
Command-line scaffolding tool for Ethereum L2 (Base, Optimism, Arbitrum) developers with Account Abstraction support
LD Score Regression — fast Rust reimplementation of Bulik-Sullivan et al. LDSC
Rust SDK for async 0xArchive market data clients
Customizable multi-tier cache with L1 (Moka in-memory) + L2 (Redis distributed) defaults, expandable to L3/L4+, cross-instance invalidation via Pub/Sub, stampede protection, and flexible TTL scaling
A high-performance multi-level cache library for Rust with L1 (memory) and L2 (Redis) caching.
Rust SDK for the Tensora L2 AI Network built with OP Stack and BSC settlement.
A behavioral, adaptive sched_ext scheduler with three-tier classification, L2 affinity, and process learning
Rust driver for the Unitree L2 LiDAR — wire format, parser, and point reconstruction
App-data schema, validation, CID encoding and hook metadata for the CoW Protocol SDK.
Message utilities for the Ruby console.
A Ruby lib for Singleton L2 support
Multiple caching levels for Rails. Kinda like your CPU's L1/L2 caches.
Rumale::Preprocessing provides preprocessing techniques, such as L2 normalization, standard scaling, and one-hot encoding, with Rumale interface.
jpzip は日本の郵便番号を CDN 配信の JSON データから引く Ruby SDK。L1 LRU メモリキャッシュを内蔵し、任意の L2 永続キャッシュを差し込める。
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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