simdjson is a high-performance C++ JSON parsing library that uses SIMD instructions (AVX2, AVX-512, NEON, RVV, etc.) to parse and validate JSON at multi-gigabyte-per-second speeds. It provides two primary APIs: a DOM API (`simdjson::dom`) that builds a complete in-memory tree, and an On-Demand API (`simdjson::ondemand`) that performs lazy, forward-only parsing for streaming and large-document use cases. The library also includes a builder API for JSON serialization, compile-time JSON parsing via C++26 reflection, JSON Pointer/Path support, NDJSON/JSON-sequence streaming, and a single-header amalgamated distribution. The codebase is organized around a two-stage pipeline: stage 1 performs SIMD-accelerated structural indexing and UTF-8 validation, and stage 2 builds the DOM tape or drives the on-demand iterator. Runtime CPU dispatch selects the best implementation for the host architecture, with fallbacks for non-SIMD platforms. The repository is a mature, production-grade library used by major projects (Node.js, ClickHouse, etc.) and is backed by an extensive test suite, fuzzing harnesses, and cross-architecture CI. It is primarily an application/library rather than a data-research repository, though it contains benchmark and research-oriented components (e.g., performance modeling, static reflection experiments).
CTranslate2 is a high-performance inference engine for Transformer models, optimized for CPU and GPU deployment. It supports a wide range of architectures including encoder-decoder (BART, T5, NLLB, M2M-100, Whisper), decoder-only (LLaMA, GPT-2, Mistral, Gemma, Qwen), and encoder-only (BERT, DistilBERT, XLM-RoBERTa) models. The system provides a C++ library with Python bindings, a command-line tool (ct2-translator), and converters from popular frameworks like HuggingFace Transformers, Fairseq, Marian, OpenNMT-py/tf, and OPUS-MT. Key features include quantized inference (INT8/INT16/FP16/BF16/AWQ), flash attention, multi-GPU tensor parallelism, streaming generation, and token-level penalties. The architecture follows a modular design with device-agnostic operations, layer abstractions, and a model registry that allows plugging in new architectures without modifying the core engine. The codebase is actively maintained with frequent releases, extensive test coverage (C++ and Python), and CI pipelines for multiple platforms and backends.
This repository is a full-stack monorepo template for building SaaS applications using React 19, tRPC, and Cloudflare Workers. It features an edge-native architecture with three Workers (web router, app SPA, and API server) connected via service bindings. The template includes integrated authentication (Better Auth with email OTP, passkey, Google OAuth), multi-tenancy, Stripe billing, transactional emails (via Resend), file uploads to Cloudflare R2, and a Neon PostgreSQL database accessed through Drizzle ORM with Hyperdrive connection pooling. Key components include a reusable shadcn/ui component library, a typed WebSocket protocol package, and isolated database testing utilities.
XLB is a differentiable, massively parallel Lattice Boltzmann Method (LBM) library written in Python, designed for physics-based machine learning and computational fluid dynamics (CFD). It provides a modular operator-based architecture with abstract base classes and backend-specific implementations for JAX (CPU/GPU/TPU), NVIDIA Warp (single-GPU CUDA), and Autodesk Neon (multi-GPU, multi-resolution). The library supports 2D and 3D simulations with multiple velocity sets (D2Q9, D3Q19, D3Q27), collision models (BGK, KBC, Smagorinsky LES), boundary conditions (equilibrium, bounce-back, regularized, Zou-He, hybrid, extrapolation outflow), and immersed boundary method (IBM) for fluid-structure interaction. A key differentiator is its native differentiability via JAX and Warp autodiff, enabling gradient-based optimization of initial conditions, geometry, or control parameters. The codebase also includes experimental out-of-core support for simulations exceeding GPU memory, multi-resolution grid refinement, and distributed computation across multiple devices. While primarily a software library for CFD practitioners and ML researchers, it is accompanied by research validation data and academic publications, giving it a hybrid application/research character.