Local Deep Research is a self-hosted, privacy-focused AI research assistant that performs deep, agentic research using multiple LLMs and web searches. It provides a full-stack web application (FastAPI + JavaScript), a programmatic API, and a Model Context Protocol (MCP) server. The system ingests user queries, decomposes them, executes parallel searches across web and academic sources (SearXNG, Google, arXiv, PubMed, Semantic Scholar, etc.), fetches and extracts content, evaluates evidence, and synthesizes reports with citations. It features per-user encrypted SQLCipher databases, a settings system with environment variable overrides, a RAG library with FAISS vector store, journal quality scoring, news subscriptions, and extensive security controls (egress policy, SSRF protection, credential scrubbing). The codebase is heavily tested with unit, integration, UI, accessibility, and security tests. It is a hybrid of a web application and a research data pipeline, but the dominant character is a production-grade application.
This repository is a static resource list providing curated links to academics on Mastodon. It organizes resources by discipline, institution, groups, servers, and other categories, all presented in a single README.md file. The project does not contain any code, data processing logic, or interactive components; it serves as a reference directory for academics seeking to engage with the Mastodon federated social network. The repository is maintained, with the most recent commit indicating recent updates to the resource list. There is no technical drift as the project contains no executable code or dependencies that could become outdated. While the resource list is useful, it lacks reusable software components or novel engineering patterns, representing a straightforward compilation of externally hosted resources.