Fabric is an open-source framework for augmenting humans using AI, centered on a large library of crowdsourced prompt templates called 'patterns'. The system provides a CLI tool, a REST API server, and a SvelteKit web UI, all sharing a common pattern engine that applies structured prompts to user input (text, URLs, YouTube videos, PDFs, images). The architecture is modular: a plugin registry manages AI vendor adapters (OpenAI, Anthropic, Gemini, Ollama, Azure, Bedrock, Vertex AI, and 25+ OpenAI-compatible providers), a template engine supports variable substitution and executable extensions, and a filesystem-backed storage layer handles patterns, sessions, and contexts with path-traversal protection. Key entry points include the Go CLI (`cmd/fabric`), the Gin-based REST API (`internal/server`), and the SvelteKit web app (`web/`). External dependencies span multiple AI SDKs, web scraping libraries (go-readability), YouTube transcription (yt-dlp), and SQLite for caching. The pattern library itself is a data asset with over 100 markdown files, each defining identity, steps, and output instructions for tasks like summarization, analysis, code review, and creative writing.
AlbumentationsX is a production-grade Python library for fast, flexible image augmentation in deep learning and computer vision pipelines. It provides a comprehensive catalog of 70+ transforms covering pixel-level (blur, noise, color, weather effects), geometric (flip, rotate, crop, resize, distortion, perspective), dropout (coarse, grid, mask, pixel), mixing (mosaic, copy-paste, overlay), and domain adaptation (FDA, histogram matching, stain normalization). The library supports 2D and 3D data, including images, masks, bounding boxes (HBB and OBB), keypoints, and volumes, with seamless integration into PyTorch via tensor transforms. Its architecture is built on a clean inheritance hierarchy (BasicTransform → DualTransform/ImageOnlyTransform/Transform3D) with Pydantic-based parameter validation, serialization to JSON/YAML, and deterministic replay via ReplayCompose. The codebase is backed by an extensive test suite (property-based, regression, contract, and golden vector tests), ASV benchmarks, and a sophisticated CI/CD system with path-aware planning, performance budgets, and legal integrity checks. Telemetry (opt-out) collects anonymous usage statistics to guide development. While primarily a software library, it incorporates research-level transforms (e.g., PlanckianJitter, HEStain, dithering) and is used in both academic and production settings.
nlpaug is a Python library for data augmentation across text (character, word, sentence), audio, and spectrogram modalities. It provides a wide array of augmentation techniques including random insertion/substitution/deletion, synonym replacement via WordNet/PPDB, contextual word embeddings (BERT, GPT-2, RoBERTa), back-translation, TF-IDF-based augmentation, keyboard/OCR error simulation, and audio effects (noise, pitch shift, speed, VTLP). The library follows a consistent pattern: each augmenter inherits from a base class, accepts data via a unified `augment()` method, and returns augmented samples. Composition is supported through `Sequential` and `Sometimes` pipeline classes. It relies heavily on external deep learning models (HuggingFace Transformers, word2vec, GloVe, fastText) and audio processing (librosa). The codebase is well-structured with clear separation of concerns (augmenters, models, utilities, flows), extensive unit tests, and documentation. However, it exhibits significant technical drift due to aggressively pinned dependencies (e.g., `transformers>=5.9.0`, `numpy>=2.4.6`) that may not exist or conflict with current stable releases, and some integration code (conda setup for Python 3.5–3.9) is outdated. Despite this, the core augmentation logic and utility modules are sturdy and reusable.