Projects

### Overview: EMBA is an embedded firmware security analyzer designed for penetration testers, product security teams, developers, and responsible product managers. It supports the entire security analysis process, from firmware extraction to generating a web-based vulnerability report. ### Key Features: - **Static and Dynamic Analysis**: EMBA performs both static and dynamic analysis using emulation, ensuring comprehensive security coverage. - **SBOM Generation**: Automatically generates a Software Bill of Materials (SBOM) to provide a clear overview of the software components used in the firmware. - **Vulnerability Detection**: Identifies weak spots and vulnerabilities such as insecure binaries, outdated software components, potentially vulnerable scripts, and hard-coded passwords. - **Command Line Interface**: EMBA is a command-line tool, making it easy to run and integrate into existing security workflows. - **Web Report Generation**: Generates an easy-to-use web-based vulnerability report for further analysis and sharing. ### Tech Stack: EMBA is built using Shell scripting and relies on various tools for static analysis, dynamic emulation, and SBOM generation. It is designed to be cross-platform and can be run on various Linux distributions. ### Why It Matters: EMBA is a critical tool for identifying and mitigating security risks in embedded firmware, ensuring the security and reliability of embedded devices. Its ability to provide detailed vulnerability reports and automate the security analysis process makes it an indispensable tool for security professionals.

### Overview: LLM Workflow Engine (LWE) is a powerful CLI and workflow manager designed specifically for LLMs (Large Language Models). It allows users to interact with ChatGPT and other LLMs directly from the command line, leveraging their official API endpoints. LWE supports multiple LLM providers and provides a simple plugin architecture for extending functionality. ### Key Features: - **CLI Interface**: Runs in the terminal, making it accessible from any shell environment. - **Official API Support**: Directly calls the OpenAI ChatGPT API, supporting all available models. - **Plugin Architecture**: Extensible with custom plugins for additional functionality. - **Multiple LLM Providers**: Interacts with various LLMs like GPT-3, Cohere, and Huggingface. - **Workflow Integration**: Integrates with Ansible Playbooks to create complex workflows. - **Docker Image**: Available as a Docker container for easier deployment. - **Python API**: Provides a Python library for using ChatGPT/GPT4 in scripts. - **Documentation**: Comprehensive documentation available on ReadTheDocs. ### Tech Stack: - Python ### Why It Matters: LLM Workflow Engine simplifies the interaction with LLMs, making it easier to integrate LLMs into workflows and command-line interfaces. Its CLI and workflow capabilities make it versatile for various applications, including automation and integration with other tools. ### Repository: The repository for LLM Workflow Engine is located at [llm-workflow-engine/llm-workflow-engine](https://github.com/llm-workflow-engine/ll

### Overview: Eagle is a comprehensive vision-language model family developed by NVIDIA, focusing on data-centric strategies across general-purpose multimodal understanding, long-context reasoning, and embodied applications. It serves as a research and development platform for NVIDIA's enterprise intelligence and Physical AI efforts, including flagship models like Llama-Nemotron Nano VLM and Nemotron VLMs. ### Key Features: - **Eagle 2.5**: A significant update with improved performance and efficiency. - **Eagle 2.5 Visual Prompt Fine-Tuning**: A script for fine-tuning models with visual prompts, enhancing their interpretability. - **LocateAnything**: A general-purpose vision-language model that supports batch inference on various GPUs. - **Eagle 2.5 Tech Report**: Detailed technical report on the model. - **Eagle 2.5 Models**: Available on Hugging Face. - **Eagle 2.5 Tech Blog**: Comprehensive tech blog detailing the model's adoption and advancements. ### Tech Stack: Python ### Why It Matters: Eagle is pivotal in advancing the field of multimodal foundation models and serves as a critical research and development platform for NVIDIA's enterprise AI initiatives. Its data-centric strategies and robust performance make it a cornerstone in the development of future AI models.

### Overview: Fine-tuning ChatGLM-6B with PEFT | 基于 PEFT 的高效 ChatGLM 微调 ### Key features: - Fine-tuning ChatGLM-6B model with PEFT - Web UI for training, evaluation, and inference - FastEdit package for efficient editing of factual knowledge - Support for 4-bit LoRA training - Support for using the development set during training - Support for RLHF training - Support for training ChatGLM with checkpoints - Support for training with combined datasets - Support for training quantized models ### Tech stack: Python ### Why it matters: This project provides a comprehensive framework for fine-tuning the ChatGLM-6B model using the PEFT library, making it easier and more efficient to experiment with and deploy language models.

Overview: This curated collection of Python scripts is designed to help developers streamline their work, automate tasks, and enhance their coding experience. The scripts range from basic programming concepts to more advanced automation tasks, ensuring that users can find scripts that can make their lives easier and more enjoyable. Key features: - A variety of scripts for various programming tasks, from basic to advanced. - Scripts for automating common tasks and improving productivity. - Scripts for downloading PDFs from online sources. - Scripts for random Instagram post likes. Tech stack: Jupyter Notebook Why it matters: This repository is a valuable resource for developers looking to enhance their coding skills and streamline their workflow. The scripts provided can help developers save time and increase their efficiency, making their work more enjoyable and fulfilling. Repository: avinashkranjan/Amazing-Python-Scripts GitHub stars: 3706 GitHub description: 🚀 Curated collection of Amazing Python scripts from Basics to Advance with automation task scripts. README excerpt: 🚀 Curated collection of Amazing Python scripts from Basics to Advance with automation task scripts. This is your Personal space to find or add any new script that can make Life Easier for you, as a Developer, and find a utility of coding to burst out of boredom. Get started with coding exhilarating scripts that you can use to download PDFs from an Online Source or just to randomly like everyone's Instagram Post.

Overview: This repository contains articles written by the author on machine learning, originally published on MachineCurve.com between May 2019 and February 2022. The articles are now archived and available for public access. Key features: Articles, machine learning, machine learning articles, MachineCurve.com Tech stack: Markdown, HTML Why it matters: These articles provide insights into the author's expertise in machine learning, which can be valuable for developers looking to learn about the field.

### Overview: 随着大模型时代的到来，AIQ致力于整理和分享来自国内外知名互联网公司技术博客、开源项目官网、综合技术社区以及知名技术公众号的高质量AI/ML/DATA技术资料。这些内容不仅涵盖了大厂的落地实践、顶尖实验室的最新论文，还包含了工业界的真实踩坑记录，为AI/ML工程师们提供宝贵的实战经验和前沿知识。 ### Key features: - 🏢 AI 业界实践：来自FAANG、阿里、美团、字节等大厂的实战经验分享。 - 📰 AI 动态资讯：涵盖论文速览、新模型发布、行业动态、AI领袖推文精选。 - 🧭 AI 精选产品导航：精选的AI工具与产品导航，帮助快速找到好用的东西。 ### Tech stack: 基于Markdown和GitHub Pages构建。 ### Why it matters: 在大模型时代，真正珍贵的增量信息来自于一线实战经验——大厂落地实践、顶尖实验室最新论文、工业界真实踩坑记录。这些宝贵的知识增量正变得愈发稀缺且不可替代，AIQ致力于提供这些有价值的内容，助力AI/ML工程师们在智能时代飞得更高更远。 --- Repository: cbamls/AI_Tutorial GitHub stars: 3699

### Overview: Sumy is a Python library and command-line utility designed for automatic summarization of text documents and HTML pages. It provides a simple and efficient way to extract summaries from various sources, including HTML pages and plain text files. The library is widely used for evaluating text summarization methods and includes a comprehensive documentation on supported languages and evaluation frameworks. ### Key Features: - **Automatic Summarization**: Sumy can extract summaries from HTML pages and plain text documents. - **Tokenizer Support**: Supports various natural language processing (NLP) languages, including English, Ukrainian, Czech, and more. - **Evaluation Framework**: Provides a simple evaluation framework for text summaries. - **Command Line Utility**: A command-line tool for quick summarization of documents. - **Alternative Implementations**: A list of alternative implementations of summarizers in various programming languages. ### Tech Stack: - Python 3.8+ - uv (Modern, fast Python package installer and resolver) - Pip ### Why It Matters: Sumy is a valuable tool for anyone working on text summarization, providing a straightforward way to evaluate and enhance summarization techniques. Its support for multiple languages and easy installation make it a versatile choice for developers and researchers alike. ### Installation: - **Using uv**: ```sh uv pip install sumy uv pip install git+https://github.com/miso-belica/sumy.git # for the fresh version ``` - **Using pip**: ```sh pip install sumy pip install git+git://github.com/miso

Overview: Mirage is the world's first virtual terminal designed for AI agents, providing broad data context, flexible tool use, cost efficiency, and enhanced security through a unified virtualized terminal. Key features: - Virtual filesystem for broad data context - Virtualized CLIs for flexible tool use - Dynamic runtimes for cost efficiency and token efficiency - Fine-grained control over agent actions and visibility Tech stack: TypeScript Why it matters: Mirage offers a unified virtual terminal that optimizes agent performance, cost efficiency, and security by integrating a virtual filesystem, virtualized CLIs, dynamic runtimes, and a policy engine.

Overview: This repository is a curated collection of papers and resources focused on enhancing the reasoning capabilities of Large Language Models (LLMs) and Multi-Modal Language Models (MMLMs). It serves as a valuable resource for developers and researchers interested in understanding and improving the reasoning abilities of these models. Key features: - 🤒 Chain-of-Thought Reasoning - 🧠 Multimodal Reasoning - 🕋 Scaling Smaller Language Models for Reasoning Tech stack: The repository uses Markdown and GitHub Pages for organization and documentation. Why it matters: This collection provides a comprehensive overview of the latest research in the field, helping developers to stay updated with the latest advancements in LLMs and MMLMs, and to implement these techniques effectively in their projects. Repository: atfortes/Awesome-LLM-Reasoning GitHub stars: 3695 GitHub description: From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓 README excerpt: <div align="center"> <a href="https://github.com/atfortes/Awesome-LLM-Reasoning/stargazers"><img src="https://img.shields.io/github/stars/atfortes/Awesome-LLM-Reasoning?style=for-the-badge" alt="Stargazers"></a> <a href="https://github.com/atfortes/Awesome-LLM-Reasoning/network/members"><img src="https://img.shields.io/github/forks/atfortes/Awesome

### Overview: Acontext is an open-source skill memory layer designed to enhance the learning and reuse capabilities of AI agents. It automatically captures and stores agent learnings as agent skill files, allowing agents to access and modify these files across different environments. ### Key Features: - **Skill Memory Files**: Agent skill files are Markdown files that can be read, edited, and shared across agents, LLMs, and frameworks. - **Automatic Learning**: Skills can evolve over time as the agent learns from its mistakes and reuses what worked. - **Cross-Environment Compatibility**: Skills can be used with LangGraph, Claude, AI SDK, or any framework that supports file-based memory. - **Progressive Disclosure**: Skills are designed to be used in a progressive manner, allowing the agent to retrieve only what it needs without semantic top-k search. ### Tech Stack: - JavaScript ### Why It Matters: - Simplifies agent memory management, making it easier to understand, debug, and correct. - Enables agents to learn from their mistakes and reuse successful strategies. - Supports a wide range of frameworks and environments, including LangGraph, Claude, and AI SDKs. ### Repository: - Repository: memodb-io/Acontext - GitHub Stars: 3696 - GitHub Description: Agent Skills as a Memory Layer ### Primary Language: - Primary Language: JavaScript ### GitHub Actions: - Actions: - Core Test: Check for core functionalities. - API Test: Validate API endpoints. - CLI Test: Ensure command-line interface works correctly. ### Discord: - Discord:

### Overview: DBHub is a token-efficient, zero-dependency MCP server designed for Postgres, MySQL, SQL Server, Oracle, MariaDB, and SQLite. It simplifies database exploration with a minimal set of tools, allowing multiple database connections and guardrails to prevent unauthorized access. ### Key Features: - **Minimal**: Zero dependency, token-efficient with two default tools. - **Multi-Database**: Supports PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, and SQLite. - **Multi-Connection**: Connects to multiple databases via TOML configuration. - **Guardrails**: Provides read-only mode, row limiting, and query timeouts. - **Secure Access**: Supports SSH tunneling and SSL/TLS encryption. ### Tech Stack: TypeScript ### Why It Matters: DBHub is the official example in the Claude Code docs for connecting to PostgreSQL via MCP, making it an essential tool for local development and non-technical access to databases.

### Overview: Quantum machine learning is a rapidly evolving field that combines the principles of quantum mechanics with machine learning algorithms. This repository offers a curated list of resources, including algorithms, study materials, libraries, and projects, all related to quantum machine learning. ### Key Features: - **Quantum Mechanics Basics**: Understanding the fundamental concepts of quantum mechanics. - **Quantum Computing Overview**: Exploring the basics of quantum computing, including topological quantum computing. - **Quantum Computing vs Classical Computing**: Comparing the differences between quantum and classical computing. - **Quantum Computing Algorithms**: Implementing and understanding quantum algorithms like Quantum Fourier Transform, Variational Quantum Eigensolver, Grover's Algorithm, and Shor's Algorithm. - **Quantum Machine Learning Bridge**: Exploring the integration of quantum computing with machine learning, focusing on complex numbers, tensors, and quantum gates. ### Tech Stack: - **Python**: The primary programming language used for implementing quantum algorithms and machine learning models. ### Why It Matters: Quantum machine learning has the potential to revolutionize fields such as cryptography, optimization, and quantum chemistry by leveraging the principles of quantum mechanics. This technology holds the promise of solving problems that are currently intractable with classical computers. ### Repository: Repository: krishnakumarsekar/awesome-quantum-machine-learning GitHub stars: 3690 GitHub description: A curated list of awesome quantum machine learning algorithms, study materials, libraries, and projects. ### Table of Contents: <!-- MarkdownTOC depth=4 --> - [Introduction](#introduction)

Overview: This repository contains the improved PyTorch version of the paper "Face Alignment in Full Pose Range: A 3D Total Solution" by Jianzhu Guo and the CBSR group. It includes pre-trained models, training datasets, and codebase, providing a comprehensive solution for face alignment in full pose range. Key features: - Pre-trained models and code for the ECCV-2020 paper - Real-time training strategies - Depth image estimation - Pose estimation feature - Object serialization with sampled texture - Support for landmark-free face cropping - Fast face detector recommendations Tech stack: Python Why it matters: This repository offers a practical, improved solution for face alignment in full pose range, with real-time training capabilities and pre-trained models, making it a valuable resource for developers working in computer vision and facial recognition. Repository: cleardusk/3DDFA GitHub stars: 3679 GitHub description: The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

Overview: This repository offers a collection of easy-to-read code examples for various reinforcement learning algorithms, including Grid World, CartPole, and Atari games. It provides benchmarks for training performance on a MacBook Pro. Key features: - Grid World algorithms: Policy Iteration, Value Iteration, SARSA, Q-Learning, Deep SARSA, and REINFORCE - CartPole algorithm: DQN, A2C, and PPO - Atari algorithms: DQN and PPO Tech stack: Python 3.11, PyTorch 2.11 with MPS backend Why it matters: This repository serves as a valuable resource for developers looking to understand and implement reinforcement learning algorithms in a clean and accessible manner. It provides practical examples and benchmarks to help developers evaluate and optimize their models.

### Overview: Twinny is an open-source AI coding assistant for Visual Studio Code, designed to work seamlessly with your existing infrastructure. It offers advanced features such as code completion, inline edits, chat, and code reviews, all powered by models hosted on your local or remote servers. ### Key Features: - **Code Completion:** Provides suggestions as you type, with context from open files, imports, and recent edits. - **Inline Edit:** Allows you to describe and review changes directly in the editor. - **Chat:** Facilitates conversations with your code, including file, symbol, and terminal access. - **Agent Mode:** Experimental feature that lets the model read, search, and edit files. - **Workspace Index:** Hybrid keyword and vector search for the workspace, updated on save. - **Code Review:** Reviews working tree, branch, or GitHub pull request commit messages. - **Terminal:** Runs commands from a description, showing before execution. - **Prompt Templates:** Editable templates for various features. ### Tech Stack: TypeScript ### Why It Matters: Twinny is designed to be a self-contained, open-source solution that integrates seamlessly with your existing development environment. It eliminates the need for external models, making it highly flexible and customizable to fit your specific needs.

Overview: LIT, formerly known as the Language Interpretability Tool, is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It answers questions such as what kind of examples a model performs poorly on, why it made specific predictions, and whether it behaves consistently with changes in input. Key features: - Local explanations via salience maps and rich visualization of model predictions. - Aggregate analysis including custom metrics, slicing and binning, and visualization of embedding spaces. - Counterfactual generation via manual edits or generator plug-ins to dynamically create and evaluate new examples. - Side-by-side mode to compare two or more models, or one model on a pair of examples. - Highly extensible to new model types, including classification, regression, span labeling, seq2seq, and language modeling, supporting multi-head models and multiple input features out of the box. - Framework-agnostic and compatible with TensorFlow, PyTorch, and more. Tech stack: TypeScript Why it matters: LIT is a powerful tool for developers and researchers to understand and debug machine learning models, providing insights into model behavior and making it easier to identify and fix issues in complex models.

Overview: This repository contains a gentle guide to Python features that the author didn't know existed or was too afraid to use. The guide is updated as the author learns more and becomes less lazy. Key features: - Lambda, map, filter, reduce - Python >= 3.6 - Jupyter Notebook - GitHub stars: 3669 Tech stack: Jupyter Notebook Why it matters: The guide provides practical examples of Python features that can be used to enhance machine learning projects, making the author's code more efficient and readable.

Overview: NVIDIA Agent Skills is a comprehensive collection of skills designed to enhance the capabilities of AI coding agents like Claude Code, Codex, and others. These skills include Physical AI, robotics workflows, simulation, CUDA libraries, RAG and AI Blueprints, and platform tools, all optimized for NVIDIA products. Key features: - Physical AI and robotics workflows - Simulation tools - CUDA libraries - RAG and AI Blueprints - Platform tools Tech stack: Python Why it matters: These skills provide a standardized way to integrate NVIDIA's cutting-edge technologies into AI agents, ensuring optimal performance and compatibility across different platforms and environments. Repository: NVIDIA/skills Primary language: Python GitHub stars: 3665 GitHub description: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end.

### Overview: GoClaw is an AI agent platform built in Go, offering multi-tenant isolation, 5-layer security, and native concurrency. It supports 20+ language models and is production-tested, ensuring agents orchestrate for you without compromising safety. ### Key Features: - **8-Stage Agent Pipeline** — Context → History → Prompt → Think → Act → Observe → Memory → Summarize. Pluggable stages, always-on execution. - **4-Mode Prompt System** — Full / Task / Minimal / None with section gating, cache boundary optimization, and per-session mode resolution. - **3-Tier Memory** — Working (conversation) → Episodic (session summaries) → Semantic (knowledge graph). Progressive loading L0/L1/L2. - **Knowledge Vault** — Document registry with [[wikilinks]], hybrid search (FTS + pgvector), filesystem sync. - **Agent Teams & Orchestration** — Shared task boards, inter-agent delegation (sync/async), 3 orchestration modes (auto/explicit/manual). - **Self-Evolution** — Metrics → Suggestions → Auto-Adapt with guardrails. Agents refine their own communication style. - **Multi-Tenant PostgreSQL** — Per-user workspaces, per-user context files, encrypted API keys (AES-256-GCM), RBAC, isolated sessions. ### Tech Stack: Go ### Why It Matters: GoClaw is designed for deploying AI agent teams at scale while maintaining safety and security. Its multi-tenant isolation and 5-layer security ensure