FunPayVertex is a Telegram-controlled automation bot for the FunPay digital marketplace, written in Python. It scrapes FunPay's internal API to provide auto-delivery of digital goods, auto-reply to chat messages, auto-raise of listings, and inventory management (auto-restore/disable lots). The architecture is event-driven: the `FunPayAPI.Runner` polls FunPay's `/runner/` endpoint and emits typed events (new orders, messages, reviews) which are dispatched to registered handlers in `handlers.py` and through a plugin system. The Telegram bot (`tg_bot/`) serves as the primary user interface, offering menus for configuration, notifications, plugin management, and proxy settings. The codebase is modular with a separate `FunPayAPI` package that could be reused independently. It lacks tests and CI/CD, and relies on scraping HTML/CSS selectors that may break with site updates, contributing to a medium technical drift rating. The plugin system and localization framework are well-designed and potentially reusable.
This repository is a data collection and publishing pipeline for Product Hunt daily launches. It ingests data from the Product Hunt API (via an external consumer app identified by the UTM campaign 'decohack') and produces one markdown file per day, with each file containing structured entries for products: title, tagline, description, keywords, vote counts, comment counts, featured status, and links. The markdown files are the primary data artifact and are consistently formatted, making them parseable for trend analysis, competitive research, or AI training corpora. Supporting Python scripts handle image repair (with exponential backoff for rate limits), WordPress publishing via REST API, and markdown generation. GitHub Actions workflows automate generation, image fixing, and publishing. The repository is best classified as a data pipeline rather than an application: it exposes no API endpoints, UI, or library interface, and its value lies in the accumulated dataset and the automation around it. However, it is a hybrid in that it contains application-like scripts and CI/CD workflows. The data lineage is straightforward: Product Hunt API responses are transformed into daily markdown files, then optionally enriched with images and published to WordPress. The dataset in the analyzed chunk is incomplete (only a subset of dates is present), and there is no schema enforcement, validation, or test suite. The pipeline depends on external services (Product Hunt API, OpenAI, WordPress) that may change or rate-limit, and the hardcoded OpenAI model (gpt-4o-mini) is a drift risk. The markdown format is human-readable but less machine-friendly than JSON/CSV, and no README or data dictionary documents the schema or provenance. Although the last commit is recent, the data files in the analyzed chunk stop at 2025-09-28, suggesting the ingestion pipeline may not be running reliably.
Estela is an open-source web scraping orchestration platform (analogous to Scrapy Cloud) for deploying, scheduling, and monitoring Scrapy-based spider projects on Kubernetes. It consists of a Django REST API backend with Celery for async task management, a React/TypeScript frontend with Ant Design, and a Kafka-based queue consumer for streaming scraped data into MongoDB. Core workflows include project management, spider deployment (Docker image build via Kaniko), job execution with configurable resource tiers, cron scheduling, data persistence settings, and a metering subsystem for usage tracking. The system relies on external dependencies such as Kubernetes, Kafka, MySQL, MongoDB, Redis, and object storage (MinIO/S3). The frontend provides interactive charts, a tour overlay for onboarding, and notification management. While functional and extensible, the codebase shows signs of age (outdated Django/Celery versions, class-heavy React components, duplicate logic in cron expression handling) and has incomplete test coverage.