The ai-boost/awesome-prompts repository is a curated collection of prompt templates designed to configure AI agents for specific roles and tasks. It contains hundreds of specialized prompts for agent architectures (e.g., personal agent brains, multi-agent orchestrators), agent skills (e.g., coding agents, security auditors), and domain-specific agents (e.g., healthcare operations, legal analysts). Each prompt encapsulates non-obvious design patterns for agent systems, such as three-layer retrieval for memory, plan-execute loops, declarative UIs, and skill-based architectures. The repository serves as a reference for prompt engineering and agent design, enabling users to build reliable, safe, and effective LLM-based agents without starting from scratch.
OneFileLLM is a versatile CLI tool and web application designed to process diverse inputs (local files, GitHub repositories, YouTube transcripts, arXiv papers, web pages, etc.) into a single structured XML-like output optimized for LLM consumption. The system features format-specific processors that extract and convert content (e.g., Excel to markdown, PDF text extraction, Jupyter notebook to code), token counting via tiktoken with optional NLTK-based compression, and metadata reporting. It includes a Rich-powered terminal interface with colored output and progress bars, a Flask-based web app with Server-Sent Events for real-time progress updates, and an alias system for JSON-stored shortcuts with placeholder substitution. Security is prioritized through SSRF protection, local path validation, and offline mode support. The architecture separates core processing logic (onefilellm.py), CLI handling (cli.py), utilities (utils.py), and web components (extras/web_app.py), enabling modular reuse.
This repository is a curated bibliography and roadmap for Quantum Machine Learning (QML). It contains chronological lists of papers, books, and reviews from 2007 to 2026, organized into yearly markdown files. The content is entirely reference links with basic metadata. While there is no executable code, data pipelines, or analytical workflows, the repository serves as an authoritative, up-to-date resource for researchers entering or tracking the QML field. It also includes a workforce section and a separate reviews page. The repository is actively maintained with recent updates and follows a consistent structural pattern across years.
This repository is a large, curated collection of technical PDF documents primarily focused on systems programming, computer architecture, operating systems, algorithms, and low-level software engineering. It contains thousands of papers, manuals, specifications, and tutorials from sources like Intel, AMD, NVIDIA, Microsoft, and academic researchers. The repo also includes a small static site generator in `.site/` that produces a browsable web interface for the collection. The primary value is the curated set of high-quality reference materials, not the minimal code. The repository is a powerful resource for engineers and researchers looking for deep technical knowledge, especially in areas like x86 assembly, GPU computing, memory management, and concurrent programming.
This repository is a curated, community-driven directory of academic computer science papers, organized by topic (e.g., distributed systems, cryptography, machine learning). It primarily consists of README files listing papers with links to hosted PDFs or external sources, along with a subset of PDF files stored directly in the repo. The only executable code is a simple bash script (`scripts/download.sh`) that scrapes markdown files for PDF URLs and downloads them. There are no data pipelines, models, or analytics workflows. The value lies in the curated bibliography, not in any software implementation. As such, it is not a typical software application or data research project; it is a reference resource for practitioners and researchers.