Stirling PDF is a comprehensive, open-source PDF processing platform that provides 50+ document manipulation tools (merge, split, compress, OCR, sign, redact, convert, etc.) via a web UI, REST API, and desktop client (Tauri). The backend is a modular Java Spring Boot application split into core, common, proprietary, and SaaS modules, while the frontend is a React/TypeScript app with Mantine UI and Storybook. The system also includes a Python FastAPI engine for AI-powered features such as document classification, math auditing, and question answering, and an enterprise policy engine for automated document workflows with triggers, pipelines, and output sinks (local, S3, vector DB). It supports self-hosted, SaaS (Supabase/Stripe), and desktop deployment modes with clustering via Valkey/Redis backplane, SSO/OAuth2/SAML2 authentication, and comprehensive billing/licensing (PAYG, subscriptions, enterprise). The architecture is multi-module with clear separation of concerns: `app/common` provides shared utilities (PDF parsing, security, cluster abstractions, temp file management), `app/core` implements core PDF operations, `app/proprietary` adds enterprise features (encrypted storage, policy engine, audit, hardware signing), and `app/saas` handles SaaS-specific billing and team management. The frontend uses a shadowing pattern to swap implementations per build variant (core, proprietary, desktop, SaaS) without conditional imports. The AI engine runs as a separate Python service with streaming progress events and two-round protocols for complex tasks. Key data flows include: PDF upload → tool execution (Java PDFBox/iText) → output download; policy-triggered processing (folder watch, schedule) → pipeline execution → output delivery; AI agent workflows (classification, auditing) with progress streaming. External dependencies include LibreOffice (document conversion), Tesseract (OCR), PDFBox, iText, and various cloud services (Supabase, Stripe, PostHog). The codebase demonstrates high craftsmanship with extensive unit/integration/E2E tests, reusable component patterns (createToolFlow, useBaseTool), and well-abstracted interfaces for storage, signing, licensing, and clustering.
PDFsam Basic is a mature, open-source JavaFX desktop application for splitting, merging, rotating, extracting, and mixing PDF files. It is built as a multi-module Maven project using JPMS modules, with the Sejda SDK as the PDF processing engine, EventStudio for event-driven communication, and a custom dependency injection framework. The application provides a rich GUI with tool panels, workspace persistence, preferences, theming, internationalization, update/news services, and premium tool integration. This is purely an application repository; there are no data research or analytics components.
zotero-pdf2zh is an application consisting of a Zotero plugin (TypeScript) and a companion Python Flask server that together enable in-place PDF translation within Zotero. The plugin adds a right-click menu to Zotero items — Translate, Crop, Compare, Crop-Compare — and communicates with the local server via HTTP. The server manages a dedicated Python virtual environment (using uv or conda) that runs either the legacy `pdf2zh` or the recommended `pdf2zh_next` CLI translation engine. The server exposes REST endpoints for translation, cropping, comparison, file download, and SSE-based progress tracking, and supports over 20 LLM translation services (OpenAI, DeepSeek, Gemini, etc.) via user-configured API keys. The system includes Docker deployment, Windows/macOS automation (monitors, right-click scripts), auto-updates, and a bilingual VitePress documentation site. Despite being primarily a software application, it has a strong data-processing character in its PDF manipulation and LLM orchestration layers, but the overall architecture is clearly a client-server addon, not a data analytics project.
This repository provides a server application for interacting with COMSOL Multiphysics models via a programmable interface. It features a modular architecture separating concerns into COMSOL model manipulation tools (geometry, mesh, physics, etc.), a knowledge base built from COMSOL documentation PDFs for retrieval-augmented generation, and utility functions for versioning and async handling. The system enables users to load, modify, and simulate COMSOL models through a server interface (likely MCP - Model Context Protocol), while leveraging a vector database of processed PDF documentation to provide contextual assistance. Key entry points include src/server.py and various domain-specific scripts in comsol_models/ directories for building and simulating specific models (e.g., solenoids, micromixers).
DeepReviewer-v2 is a backend-only application designed for automated academic paper review. It processes PDF papers through a pipeline involving MinerU for markdown conversion, an LLM-based review agent with tool usage (e.g., paper search, annotation), and PDF report generation with annotated source overlays. The system operates asynchronously with job persistence, featuring a CLI for job submission, status tracking, and result retrieval. Optional integration with PASA (via vLLM) enables external paper search. The architecture is modular, separating concerns into adapters, tools, state management, and report generation components, all coordinated via an async runner.
pdfcpu is a comprehensive PDF processing library and command-line tool. It provides a full suite of operations including validation, optimization, encryption, digital signing (PAdES/CAdES), form filling, watermarking, page imposition (booklet, n-up), content extraction, and PDF creation from JSON. The architecture is a monolithic library with a clear separation between a public API layer (`pkg/api/`), CLI command definitions (`pkg/cli/`), core processing logic (`pkg/pdfcpu/`), and a robust validation engine (`pkg/pdfcpu/validate/`). It implements a staged output pattern for safe file writes and a context-based cancellation system. While the feature set and test coverage are exceptional, the codebase is written in a non-standard Go dialect (using `func`, `type`, `var` keywords and a `go 1.25.0` module version), which represents a significant technical drift risk and limits its maintainability by the broader Go ecosystem. Despite this, it contains several genuinely reusable, well-crafted components such as its LZW codec, safe math utilities, path sanitizer, and BER/DER parser.
This repository is a curated knowledge base and learning guide for embodied AI and robotics. It aggregates documentation, paper summaries, interview notes, and learning roadmaps rather than providing executable software. The content is organized into sections covering foundations, classical methods, state-of-the-art techniques, simulation tools, and real-world robotics. Helper scripts for PDF processing support the documentation workflow by converting research papers into text for summarization. The repository serves as an educational resource and reference guide for practitioners and researchers in the field.