PostHog is a comprehensive product analytics and customer data platform (CDP) built as a polyglot monorepo. It combines Python/Django for core application logic and API, Rust for high-performance event ingestion and symbolication, and Node.js for ingestion pipelines and CDP services. The frontend is a React/TypeScript application using Kea for state management. It supports self-hosting and cloud deployment, with data stored in PostgreSQL, ClickHouse, and Redis. Key features include event tracking, session replay, feature flags, experiments, error tracking, and AI observability. The architecture emphasizes high-throughput event processing via Kafka and Temporal workflows for async tasks.
This is a monorepo for SurveyJS, a comprehensive open-source form/survey library that renders dynamic JSON-based forms across multiple frontend frameworks (React, Angular, Vue 3, and plain JavaScript). The core engine (`survey-core`) handles model parsing, validation, expressions, triggers, input masks, drag-and-drop, theming, and localization. It provides 20+ question types (text, checkbox, radio, dropdown, matrix, ranking, file, signature, etc.) with extensible UI components via framework-specific rendering packages. The library also includes a built-in linter for survey JSON static analysis and a tester for programmatic survey test execution. Architecture follows a layered pattern: core logic separate from UI, with each framework package consuming the core via a shared set of exported model classes. The codebase is large (2896 files) but well-structured, featuring a custom expression parser (PEG), a modular action-bar system, advanced popup management with focus trapping, and a generic drag-drop engine. Maintenance is active with frequent releases, but there is some technical drift in the form of commented-out legacy testcafe tests, auto-generated theme files, and cross-framework duplication that requires careful synchronization.
This repository is a comprehensive survey paper on the intersection of knowledge graphs and multi-modal learning. It organizes the literature into two main directions: KG-driven multi-modal learning (KG4MM) and multi-modal knowledge graphs (MM4KG). The survey covers various tasks such as visual question answering, image classification, multi-modal named entity recognition, and knowledge graph completion, among others. Each section includes a list of relevant papers with links to their sources (e.g., arXiv, conference proceedings). The repository is maintained through updates that include adding new papers and fixing links, as evidenced by the recent commit in 2026 and the news items up to 2024-12.