immudb is an immutable, cryptographically verifiable database system designed for tamper-evident storage. It provides ACID transactions with cryptographic verification via Merkle trees, ensuring data integrity and historical auditability. The system supports multiple data models: key-value, SQL (with PostgreSQL wire protocol compatibility), and document storage. Architecture follows a modular client-server design with separate binaries for server (immudb), admin CLI (immuadmin), client CLI (immuclient), and test tool (immutest). Key entry points include cmd/immudb/immudb.go (server), cmd/immuadmin/immuadmin.go (admin), and cmd/immuclient/immuclient.go (client). Data flows involve clients sending requests via gRPC or PostgreSQL wire protocol to the server, which processes transactions, appends to an immutable log, updates verification structures, and returns verifiable responses. External dependencies include Go modules for gRPC, protobuf, Viper, cryptographic libraries, and optional S3/MinIO for remote storage. The codebase demonstrates strong engineering practices with clear separation of concerns, extensive testing, and active maintenance.
This repository implements a modern Automatic Number Plate Recognition (ANPR) pipeline as a production-ready application. It ingests images (via HTTP upload or WebSocket stream), runs detection using the `fast-alpr` library (YOLO-based ONNX model), performs OCR with `fast-plate-ocr`, applies post-processing (confusion-pair correction, Turkish plate format parsing, temporal majority voting), optionally tracks vehicles via an IoU-based multi-object tracker, and stores only HMAC-SHA256 hashed plate texts for privacy compliance. The system is exposed through a FastAPI REST API with WebSocket support and a HTMX web UI, plus a Typer CLI for inference and server management. It is containerized with Docker and publishes to PyPI and GHCR. The repository also contains a legacy directory (`legacy/`) with a complete YOLOv5 training pipeline for license plate detection, but that code is explicitly unmaintained and serves as historical reference only. The modern codebase is actively maintained, well-tested, and demonstrates strong software engineering practices including protocol-based abstractions, structured logging, Prometheus metrics, and privacy-by-design persistence.
This Laravel package provides GDPR compliance features for applications. It includes an endpoint for users to download their personal data (after re-authentication), traits for automatic encryption and decryption of model attributes, and a mechanism for exporting model data in a portable JSON format. Additionally, it offers a command to clean up inactive users based on a configurable retention period, using a strategy pattern to allow customization of the cleanup logic. The package is designed to be framework-agnostic within Laravel, relying on Eloquent models and standard Laravel features.
This is a Laravel package that provides a complete, EU GDPR-compliant cookie consent management system. It allows developers to register cookies under predefined or custom categories (essential, analytics, optional), define cookie descriptions, durations, and consent callbacks. The package includes Blade directives and a Facade for easy integration into Laravel views and controllers, as well as a JavaScript library for enhanced UX with AJAX-based consent handling. The architecture is a typical Laravel package: service provider, routes, controllers, views, translations, and a compiled assets pipeline. The backend handles cookie registration, consent storage (via a cookie), and response generation (scripts, cookies). The frontend provides a customizable notice modal with toggleable details and buttons. The package is well-structured with clear separation of concerns using traits for shared functionality (attributes, callbacks, translations, cookie collections). It supports multiple languages and allows full customization of views, styles, and scripts. The codebase is actively maintained, supports Laravel 9-13, and includes a comprehensive test suite.
The Open Cookie Database is a community-curated reference dataset cataloging HTTP cookies. The single source of truth is open-cookie-database.csv, which records each cookie's platform, category (Functional, Personalization, Analytics, Marketing, Security), cookie name/pattern, domain, description, retention period, data controller, GDPR/privacy policy URL, and a wildcard-match flag. The dataset is maintained through manual PR-based contributions following a documented research workflow, and it is consumed by downstream privacy tooling (notably the EU EDPB Website Auditing Tool, via a dedicated EDPB JSON export). A static HTML viewer renders the CSV client-side using PapaParse and DataTables for browsing and export. The data lineage is simple and fully automated: on every push touching the CSV, GitHub Actions runs validate.py (UUID format/uniqueness, category whitelist, duplicate detection), then regenerates two derived artifacts - open-cookie-database.json (records grouped by platform) via convert_csv_to_json.py, and open-cookie-database-edpb.json via convert_edpb.py, which maps the flat CSV onto the EDPB tool's nested knowledge-base JSON schema. The pipeline is deterministic, idempotent, and commits the regenerated JSON back to the repo when changed. There is no application runtime, API, or library; the three Python scripts and CI workflows are infrastructure that exists solely to keep the dataset valid and in sync across formats. Craftsmanship is functional but modest. The scripts show reasonable engineering discipline: CLI parameterization, graceful handling of missing/empty files, GitHub Actions error annotations, and non-zero exit codes on failure. The validation script encodes real data-quality rules. However, there are clear signs of aging: the HTML viewer references the retired StackPath Bootstrap CDN and contains a malformed protocol-relative script URL ('//http://...'); the main validation workflow still pins Node 16 (EOL) and uses checkout@v2; convert_edpb.py uses the deprecated datetime.utcnow(); and pandas is installed unpinned. These issues do not break the core CSV-to-JSON pipeline, but they make the supporting surface fragile. The repository is a data asset first and code second - the value lies in the curated cookie catalog and the governance around it, not in the software. It is a solid, genuinely useful open-data project with competent but unremarkable data engineering. An analyst can clone it and reproduce all derived JSON artifacts with just Python and pandas, since the dataset is bundled and the transforms are simple, explicit, and parameterized.
This repository is a curated collection of machine unlearning research, combining a comprehensive survey paper (in the form of a Kaggle notebook) with executable implementations of several unlearning algorithms across different domains. It includes code for graph neural network unlearning (GraphProjector), federated learning unlearning (FedEraser), and approximate nearest neighbor search with unlearning (UnfoldedSelf-ReconstructionHashing, a fork of ann-benchmarks). The repo serves both as a literature index (taxonomy, evaluation metrics, datasets) and a benchmark hub for reproducing and comparing unlearning methods. While the survey is actively maintained, the algorithm codebases are research prototypes with varying levels of polish and portability.