Coding & Development

Awesome-GPTs is a comprehensive, open-source GitHub repository featuring a vast collection of over 1000 GPTs, categorized into 10 distinct groups. This resource also includes more than 80 leaked prompts, offering valuable insights and examples for users interested in GPT applications. The project aims to provide a centralized hub for discovering and understanding diverse GPT implementations, making it a useful tool for developers, researchers, and AI enthusiasts. Its community-driven nature encourages contributions and continuous expansion of the collection, fostering an environment for shared knowledge and exploration within the AI community.

awesome-graph-classification is a comprehensive collection of graph classification methods, encompassing embedding, deep learning, graph kernel, and factorization papers. This resource provides researchers and practitioners with a curated list of important papers, often accompanied by their reference implementations. It serves as a valuable starting point for exploring various techniques in graph-based machine learning, offering insights into areas like network embedding, graph convolutional networks, and graph attention networks. The repository also links to relevant graph classification benchmark datasets, making it a practical tool for academic research and development in the field.

awesome-uncertainty-deeplearning is an extensive open-source repository dedicated to predictive uncertainty estimation in deep learning models. It compiles a wide range of resources including surveys, academic papers, datasets, and code implementations. The collection covers various methodologies such as Bayesian methods, ensemble techniques, sampling/dropout-based approaches, post-hoc methods, data augmentation, and evidential deep learning. It also addresses applications in classification, regression, object detection, natural language processing, and more. This repository is an invaluable resource for researchers and practitioners looking to explore, understand, and implement uncertainty quantification in their deep learning projects.

Awesome-diffusion-model-for-image-processing is a comprehensive, open-source GitHub repository that serves as a summary of diffusion model-based image processing techniques. It covers a wide array of applications such as image restoration, enhancement, coding, and quality assessment. The repository is continuously updated with new related works and includes detailed sections on image super-resolution, video restoration, inpainting, denoising, dehazing, deblurring, and medical image restoration. It also features benchmarks, datasets, and models for image/video compression and quality assessment, making it an invaluable resource for researchers and practitioners in the field.

Awesome-CV-MasterHub is an open-source repository providing a curated list of recent Computer Vision (CV) papers. It serves as a valuable resource for researchers and practitioners looking to stay abreast of the latest developments in the field. The platform organizes papers by various CV sub-domains such as Image Classification, Object Detection, Semantic Segmentation, Image Generation, and Vision-LLMs. Users can easily browse through the list and find links to papers, with code links provided where available. The repository is actively maintained, with updates to ensure the most recent and relevant articles are included, typically retaining up to 200 papers per area. It encourages community contributions through issues and pull requests for any overlooked papers.

Automatic_Speech_Recognition is an open-source, end-to-end automatic speech recognition system built with TensorFlow. It provides comprehensive support for both Mandarin and English, enabling users to develop and fine-tune their own speech recognition models. The tool includes various acoustic modeling techniques such as RNN, BRNN, LSTM, BLSTM, GRU, BGRU, Dynamic RNN, and Deep Residual Networks. It also features Seq2Seq with attention decoder, CTC decoding, and robust data preprocessing for TIMIT and LibriSpeech corpora. Users can train models with CPU/GPU, manage logging, and leverage features like dropout for dynamic RNNs and shell script execution.

autotab-starter is an open-source project designed to simplify the creation of auditable browser automations using AI. It enables users to record point-and-click demonstrations in a browser and instantly generate live Python code for those actions. The tool is currently in an alpha release phase, with active development and regular updates. It requires Chrome browser and Python, and offers a quick setup process including virtual environment installation and credential configuration. Users can record automations by launching a Chrome session, logging in, and using the autotab extension to record clicks, typing, or element selections. The generated Python code can then be run to play back the automation, making it ideal for developers looking to automate repetitive web tasks.

The aws-machine-learning-university-accelerated-cv repository offers comprehensive educational materials for the Machine Learning University (MLU) Computer Vision class. This open-source resource is designed to make machine learning accessible to everyone, providing a structured path to learn about widely used ML techniques and apply them to real-world problems in computer vision. The class includes three lectures covering topics such as Intro to Computer Vision, Neural Networks, Convolutional Neural Networks, Image Datasets, and advanced CNN architectures like VGGNet and ResNet. It also features a final project where students practice working with a real-world computer vision dataset. The repository contains slides, Jupyter notebooks for hands-on practice, and datasets, making it a valuable tool for self-paced learning and experimentation.

awesome-claude-code is a meticulously curated collection of resources designed to enhance the Claude Code workflow. This open-source repository features a wide array of skills, hooks, slash-commands, agent orchestrators, applications, and plugins specifically tailored for Anthropic's Claude Code. It includes tools for various development needs, from agent skills for specialized tasks like workflow automation and security auditing to comprehensive workflows and knowledge guides for project management and documentation. The list also covers IDE integrations, usage monitors, status lines, and version control tools, making it an invaluable resource for developers looking to optimize their use of Claude Code.

CatVTON is an innovative virtual try-on diffusion model designed for efficiency and accessibility. It boasts a lightweight network with 899.06M total parameters and parameter-efficient training, utilizing only 49.57M trainable parameters. This optimization allows for simplified inference, requiring less than 8GB VRAM for high-resolution outputs of 1024x768. CatVTON supports deployment via Gradio App and ComfyUI, with automatic checkpoint downloads from HuggingFace. It also provides evaluation code for calculating metrics on datasets like VITON-HD and DressCode, making it a comprehensive solution for virtual try-on research and application development. The project is open-source and was accepted to ICLR 2025.

Brilliant Labs is dedicated to fostering an open-source ecosystem, providing resources and tools for developers and creatives to innovate and shape the future. Their flagship product, Halo, is an open-source glasses platform designed for curious and creative individuals. Halo features a color microOLED display, bone conduction speakers, and an ultra low-power Alif B1 processor with a NPU for on-device AI. It includes an optical sensor for AI inference, microphones with audio activity detection, and a 6-axis IMU. Running on ZephyrOS with a Lua interface, Halo offers cross-platform mobile app connectivity and a cloud-based AI agent named Noa, which handles real-time, multimodal conversations and remembers past interactions to personalize experiences.

browser-agent is an open-source, vision-first browser agent developed by magnitudedev, designed to automate web tasks using natural language. It leverages vision AI to understand and interact with web interfaces, allowing users to control their browser with high-level commands. Key capabilities include navigating web pages, executing precise actions with mouse and keyboard, and intelligently extracting structured data based on DOM content and Zod schemas. The tool also features a built-in test runner with powerful visual assertions, making it suitable for web app testing and integration into CI/CD pipelines. Magnitude emphasizes a vision-first architecture to overcome the limitations of traditional browser agents that rely on numbered boxes, ensuring better generalization across complex modern sites and future-proofing for desktop applications.

BitBLAS is an open-source library designed to facilitate efficient mixed-precision DNN model deployment on GPUs. It specializes in mixed-precision BLAS operations, particularly for $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs). Key features include high-performance matrix multiplication for both GEMV and GEMM, supporting various mixed-precision types like FP16xFP8/FP4/INT4/2/1 and INT8xINT4/2/1. BitBLAS also offers auto-tensorization for TensorCore-like hardware instructions and provides integrations with popular frameworks such as PyTorch, GPTQModel, AutoGPTQ, vLLM, and BitNet-b1.58. Based on techniques from the "Ladder" paper, it allows for customizing mixed-precision DNN operations via a flexible DSL (TIR Script).

bitsandbytes is a powerful library designed to make large language models (LLMs) more accessible through k-bit quantization for PyTorch. It significantly reduces memory consumption during both inference and training, allowing for more efficient use of computational resources. The library provides three core features: 8-bit optimizers that use block-wise quantization to maintain 32-bit performance with reduced memory, LLM.int8() for 8-bit quantization enabling large language model inference with half the memory and no performance degradation, and QLoRA for 4-bit quantization, which facilitates LLM training with memory-saving techniques without compromising performance. It includes quantization primitives for 8-bit and 4-bit operations, along with 8-bit optimizers, making it an essential tool for developers working with large-scale AI models.

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. It integrates cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling scientists to dramatically enhance research productivity and generate testable hypotheses. Biomni supports various LLM providers like Anthropic, OpenAI, Azure OpenAI, Gemini, and Groq, and can be configured via environment variables or a .env file. It features a data lake for biomedical information, a Gradio interface for interactive use, and configuration management for consistent settings. Additionally, Biomni can generate PDF reports of execution traces, supports Model Context Protocol (MCP) for external tool integration, and includes a Know-How Library of best practices. It also offers Biomni-R0, a specialized reasoning model for biology, and Biomni-Eval1, a comprehensive evaluation benchmark.

bert-extractive-summarizer is an open-source Python library designed for extractive text summarization, building upon the HuggingFace Pytorch transformers library. The tool operates by first embedding sentences from the input text and then employing a clustering algorithm to identify and extract sentences closest to the cluster centroids, forming a concise summary. It also incorporates coreference resolution techniques, utilizing the neuralcoref library, to enhance the coherence and context of the generated summaries. Users can customize various parameters, including the number of sentences or ratio for the summary, and integrate custom models or Sentence-BERT for diverse summarization needs. The library supports GPU acceleration via CUDA by default if available, and offers a Flask service with Docker support for easy deployment.

CCSR is an open-source tool designed to enhance image quality through content-consistent super-resolution, leveraging diffusion models. It provides official code for both CCSRv1 and the upgraded CCSRv2, which is built on Diffusers. CCSRv2 introduces significant improvements, including flexible diffusion step selection without retraining, allowing users to adjust steps to their specific needs. It boasts high efficiency, supporting inference with as few as 1 or 2 diffusion steps, drastically reducing computation time. The tool also delivers enhanced clarity with crisper details and improved stability in synthesizing fine image details, ensuring higher-quality outputs. CCSR streamlines the restoration process with a one-step diffusion workflow in its second stage.

Change-Detection-Review is an open-source resource offering a detailed review of artificial intelligence-based change detection methods, particularly within the domain of remote sensing. This GitHub repository compiles available codes and open datasets essential for deep learning applications in this field. It is based on the paper "Change detection based on artificial intelligence: state-of-the-art and challenges," providing insights into the implementation processes, data types (optical RS, SAR, street view, heterogeneous data), and general frameworks of AI-based change detection. The review also covers commonly used networks, application domains, and discusses major challenges and future prospects, making it a valuable resource for researchers.

Baichuan-7B is a large-scale 7B parameter pre-training language model developed by BaiChuan-Inc. Based on the Transformer structure, it was trained on approximately 1.2 trillion tokens and supports both Chinese and English languages. The model features a context window length of 4096 and has demonstrated strong performance on standard Chinese and English benchmarks like C-Eval and MMLU. It includes optimizations for training stability and throughput, such as efficient operators, operator splitting, mixed precision, and communication optimizations, achieving high GPU peak compute utilization. The model also features an optimized tokenizer for Chinese language compression and improved mathematical capabilities.

Claude Coder is an autonomous coding agent integrated as a VS Code extension, designed to streamline the development process for both experienced developers and coding newcomers. It acts as a 24/7 AI-powered software developer, capable of transforming concepts into code, converting designs into functional applications, and intuitively debugging issues. The tool accelerates development by automating repetitive tasks and generating boilerplate code. Additionally, it aids in learning by providing explanations and best practices, can search the web for inspiration or research, and assists with project deployment and publishing. Claude Coder aims to make coding more accessible and efficient, bridging the gap between imagination and implementation.

claude-mem is a powerful plugin designed for Claude Code, enhancing coding sessions by providing persistent memory. It automatically captures all actions performed by Claude during development, then intelligently compresses this information using AI, specifically Claude's agent-sdk. This compressed context is then seamlessly injected back into future sessions, allowing Claude to maintain a continuous understanding of projects even after sessions end or are reconnected. Key features include progressive disclosure of memory, skill-based search for project history, a web viewer UI for real-time memory streams, and privacy controls to exclude sensitive content. It supports multiple languages and workflow modes, making it a versatile tool for developers seeking to optimize their AI-assisted coding workflows.

Chrome-GPT is an experimental AutoGPT agent designed to take control of an entire Chrome session on your desktop. Utilizing Langchain and Selenium, it allows for interactive scrolling, clicking, and text input on web pages, enabling the AutoGPT agent to navigate and manipulate web content. Key features include Google search capabilities, long-term and short-term memory management, and various Chrome actions such as describing webpages, interacting with elements, and switching tabs. It supports multiple agent types, including Zero-shot, BabyAGI, and Auto-GPT, with planned support for Chrome plugins. Users should be aware of its experimental nature, potential for incorrect actions, and current limitations like slow response times and occasional parsing issues.

Chronos is a groundbreaking debugging-first language model developed by Kodezi, specifically engineered for repository-scale code understanding. It boasts state-of-the-art results on SWE-bench Lite (80.33%) and achieves an impressive 67% real-world fix accuracy, significantly outperforming general-purpose models like GPT-4. Chronos is built upon key innovations including a debugging-first architecture trained on 42.5M examples, Persistent Debug Memory (PDM) for repository-specific learning, and Adaptive Graph-Guided Retrieval (AGR) for intelligent multi-file context handling. Its seven-layer system design incorporates an execution sandbox and an explainability layer, making it a comprehensive solution for autonomous debugging. The model is slated for general availability in Q1 2026 via Kodezi OS, with limited enterprise beta access in Q4 2025.

SPAICE OS is an advanced operating system designed to bring reliable spatial-AI autonomy to aircraft and satellites, even in challenging environments where GNSS or communications may fail. It transforms any aircraft or satellite into a Spatial Agent capable of understanding and operating autonomously using only onboard cognitive sensors. The system focuses on three core technological pillars: Perception, which turns raw sensor data into situational awareness; Planning, for computing optimal trajectories in real-time onboard; and Control, for executing smooth, reliable, and collision-free maneuvers. SPAICE is ideal for applications such as Intelligence, Surveillance & Reconnaissance, Command & Control, Distributed Intelligence, Target Detection, Classification and Tracking, Self-Localization in GPS-Denied Environments, and Terrain Mapping.