Coding & Development

a.i. Authenticator is a platform designed for instant AI content authentication, allowing users to verify if images and videos were created by AI or humans. It boasts a 99.2% accuracy rate by employing advanced AI detection technology combined with human expertise. The tool analyzes over 50 data points, including pixels, metadata, GAN fingerprints, and content inconsistencies, for comprehensive multi-layer detection. Users receive instant results, typically under 30 seconds, along with an official, shareable certificate of authenticity that includes a verification ID and timestamp. It supports major image formats like JPG, PNG, GIF, WEBP, and video content, including deepfakes. The service ensures security and privacy by encrypting uploads and automatically deleting them after verification. It is currently accepting cryptocurrency payments, with card payments coming soon.

aifh, or Artificial Intelligence for Humans, offers a comprehensive collection of code examples for various AI algorithms. This open-source project is designed to accompany a series of books, providing practical implementations for theoretical concepts. The examples cover fundamental algorithms, nature-inspired algorithms, and neural networks, making it a valuable resource for anyone studying or working with AI. It supports multiple programming languages such as Java, C#, C/C++, Python, and R, ensuring broad applicability. Users can download a single ZIP file containing all examples or clone the Git repository to stay updated with the latest versions and community contributions. The project is released under the Apache 2 License, allowing free reuse in both commercial and non-commercial projects.

android-speech is an open-source library designed to make Android speech recognition and text-to-speech functionality easy for developers. It allows for seamless integration of voice input and output into Android applications. Key features include starting and stopping speech recognition, handling partial and final speech results, and converting text to speech with optional callbacks. The library also provides a customizable progress animation for speech recognition and allows for configuration of various parameters like locale and voice. Developers can enable debug logging and redirect logs to custom outputs. It supports getting current and supported languages and voices for both speech-to-text and text-to-speech.

99-ML-Learning-Projects offers a curated repository of 99 machine learning projects designed for individuals eager to learn machine learning by actively coding and building. The platform emphasizes a hands-on approach, providing exercises and solutions that are useful for learners at various stages. It encourages community contributions, allowing users to propose new exercises and solutions. The project aims to foster an open and friendly open-source collaboration environment, with current offerings including projects in General-Purpose Machine Learning, Computer Vision, Natural Language Processing, and Bayesian Naive Bayes Classification. It also provides refreshers and cheatsheets for essential libraries like Numpy and Pandas, and lists required dependencies for project execution.

a-PyTorch-Tutorial-to-Super-Resolution offers a comprehensive PyTorch tutorial focused on implementing photo-realistic single image super-resolution using Generative Adversarial Networks (GANs). It serves as an educational resource for understanding GANs and their application in image enhancement, specifically for quadrupling image dimensions. The tutorial covers concepts like residual connections, sub-pixel convolution, and perceptual loss, guiding users through the implementation of both SRResNet and SRGAN models. It assumes basic knowledge of PyTorch and convolutional neural networks, making it suitable for those looking to deepen their understanding of advanced deep learning techniques for image processing.

AI-Expert-Roadmap is a comprehensive, open-source resource designed to guide individuals on their journey to becoming an Artificial Intelligence expert. Hosted on GitHub, it provides detailed charts and recommended technologies for various AI-related fields, including data science, machine learning, deep learning, data engineering, and big data engineering. The roadmap was initially created for AMAI GmbH's new employees to accelerate their AI expertise but is openly shared with the community. It emphasizes understanding why certain tools are better suited for specific cases rather than just following trends. An interactive version with links for each bullet point is available, and users can star and watch the GitHub repository for updates and new content.

llm.c is an open-source project designed for training Large Language Models (LLMs) using simple, raw C/CUDA, aiming to provide a lightweight and efficient alternative to frameworks like PyTorch. The project's primary focus is on pretraining, specifically reproducing the GPT-2 and GPT-3 miniseries. It includes a parallel PyTorch reference implementation (a tweaked nanoGPT) for comparison, and currently boasts a performance edge over PyTorch Nightly. The repository offers a clean, ~1,000-line CPU fp32 implementation in C, alongside bleeding-edge CUDA code. It supports single and multi-GPU training, multi-node training, and integrates with libraries like cuBLAS, cuBLASLt, CUTLASS, and cuDNN for optimized performance. The project also serves an educational purpose, providing documented kernels and tutorials for understanding LLM layer implementations.

awesome-persian-nlp-ir is a comprehensive, curated list dedicated to Persian Natural Language Processing (NLP) and Information Retrieval (IR) tools and resources. This GitHub repository serves as a central hub for researchers, developers, and enthusiasts interested in the field, segmenting its content into five main categories: Tools, Datasets, Models, Repositories, and Papers and Books. It aims to consolidate various research efforts and practical applications related to Persian NLP, making it easier for users to discover and utilize relevant resources. The repository encourages community contributions to ensure its continued growth and relevance, providing guidelines for new submissions.

awesome-quantum-machine-learning is a comprehensive, curated list designed to provide a deep dive into the world of quantum machine learning. It covers fundamental concepts such as quantum mechanics and quantum computing, alongside advanced topics like quantum algorithms, quantum neural networks, and quantum statistical data analysis. The resource includes detailed descriptions of various quantum machine learning algorithms, study materials, and a collection of relevant libraries and software. It also features sections on quantum programming languages, tools, and hot topics in the field, making it an invaluable resource for anyone looking to explore or advance their knowledge in quantum machine learning, from basic principles to cutting-edge research.

Awesome-Efficient-LLM is a comprehensive, curated list of resources focused on efficient large language models (LLMs). This open-source project provides researchers and engineers with a centralized hub for papers and projects related to optimizing LLMs. The list is organized into various sub-areas, including Network Pruning / Sparsity, Knowledge Distillation, Quantization, Inference Acceleration, Efficient MOE, Efficient Architecture of LLM, KV Cache Compression, Text Compression, Low-Rank Decomposition, Hardware / System / Serving, Efficient Fine-tuning, Efficient Training, Survey or Benchmark, and Reasoning Model. Users can easily navigate through these categories to find relevant papers, with recent additions highlighted on the main page. The project also encourages community contributions, allowing users to submit new papers or update existing details via pull requests or email, ensuring the list remains current and comprehensive.

LongBench is an open-source evaluation tool designed to rigorously assess the capabilities of Large Language Models (LLMs) in processing and reasoning over extensive contexts. LongBench v2, the latest iteration, features context lengths ranging from 8k to 2M words, presenting a significant challenge even for human experts. It covers six major task categories including single-document QA, multi-document QA, long in-context learning, long-dialogue history understanding, code repo understanding, and long structured data understanding. The benchmark consists of 503 challenging multiple-choice questions, ensuring reliable evaluation. Data is collected from nearly 100 highly educated individuals, undergoing both automated and manual review to maintain high quality and difficulty. LongBench aims to provide a reliable standard for developing future superhuman long-context AI systems.

awesome-2vec is a comprehensive, curated list of 2vec-type embedding models, hosted as an open-source project on GitHub. This repository serves as a central hub for researchers and developers to discover and explore a wide array of embedding models, including popular ones like word2vec, doc2vec, and node2vec, as well as more specialized models such as tweet2vec, image2vec, and mol2vec. Each entry typically includes links to the original research paper and available code implementations in languages like Python, Java, and C++. It's an invaluable resource for anyone working with embeddings in natural language processing, graph analysis, and other machine learning domains, facilitating the discovery of relevant models and their implementations.

awesome-adversarial-machine-learning is a curated list of resources focused on adversarial machine learning, hosted on GitHub. It serves as a valuable starting point for individuals interested in this specialized area of AI. The repository organizes information into categories such as blogs, academic papers, and talks, covering topics like general adversarial examples, attacks on image classification, reinforcement learning, and speech recognition, as well as defense mechanisms. While the maintainer notes that the list is no longer updated with the latest papers, it remains a strong reference for foundational knowledge in adversarial machine learning. This open-source project is ideal for researchers and students looking to explore the field.

awesome-claude-skills is a comprehensive, curated list of Claude Skills, resources, and tools designed to customize and enhance Claude AI workflows, with a particular focus on Claude Code. Claude Skills are specialized folders containing instructions, scripts, and resources that Claude dynamically discovers and loads when relevant to tasks. This open-source GitHub repository details how Skills work, their progressive disclosure architecture for efficiency, and provides guides for getting started via the Claude.ai web interface, Claude Code CLI, or Claude API. It features official skills for document processing (docx, pdf, pptx, xlsx), design (algorithmic-art, canvas-design), development (frontend-design, web-artifacts-builder), communication, and skill creation. The repository also highlights community-contributed skills, tools for skill creation, best practices, and security guidelines, emphasizing the importance of vetting skills due to arbitrary code execution capabilities.

Awesome-LLM-KG is a comprehensive collection of academic papers and resources dedicated to the integration of Large Language Models (LLMs) and Knowledge Graphs (KGs). This repository aims to provide researchers and practitioners with a clear roadmap and understanding of how to leverage the strengths of both LLMs, known for their generalizability, and KGs, valued for their structured factual knowledge. It categorizes research into three main frameworks: KG-enhanced LLMs, LLM-augmented KGs, and Synergized LLMs + KGs, detailing involved techniques and applications. The project is actively updated with new research, including recent papers accepted at major conferences like ICML, NeurIPS, and ACL, making it a valuable resource for staying current in the field.

Awesome-LLMs-for-Video-Understanding is a comprehensive, open-source GitHub repository dedicated to the rapidly evolving field of video understanding using Large Language Models (Vid-LLMs). It serves as a vital resource for researchers, academics, and engineers by curating the latest papers, associated code, and relevant datasets. The repository features a detailed survey on Vid-LLMs, covering various techniques, training strategies, tasks, datasets, benchmarks, and evaluation methods. It also introduces novel taxonomies for Vid-LLMs based on video representation and LLM functionality, making it easier to navigate the complex landscape of this domain. Regular updates ensure the content remains current, including new models, benchmarks, and redesigned figures and tables for clarity.

AI Box is a no-code platform designed for building powerful multimodal AI tools. Users can visually create complex AI workflows using a drag-and-drop interface, chaining together various text, image, and audio models. This platform simplifies the development and deployment of AI applications, making it accessible to individuals without extensive coding knowledge. It aims to streamline the process of integrating different AI capabilities into custom tools, offering instant deployment for developed solutions. AI Box provides a flexible environment for experimenting with and combining diverse AI models to achieve specific functionalities.

MiniGPT-4 is an open-source initiative dedicated to advancing vision-language understanding by integrating advanced large language models. The project offers open-sourced code for both MiniGPT-4 and its successor, MiniGPT-v2, enabling researchers and developers to explore and build upon state-of-the-art vision-language capabilities. It functions as a unified interface, facilitating multi-task learning across various vision and language domains. The project provides detailed instructions for installation, preparation of pretrained LLM weights (including Llama2 Chat and Vicuna), and model checkpoints. Users can launch local demos for both MiniGPT-v2 and MiniGPT-4, with options to optimize GPU memory usage. Training and finetuning details are also provided, making it a comprehensive resource for those working with vision-language models.

Mini-Omni2 is an open-source, omni-interactive AI model designed to provide capabilities similar to GPT-4o, including vision, speech, and duplex interactions. It can understand image, audio, and text inputs, facilitating end-to-end voice conversations with users. A key feature is its real-time voice output and an interruption mechanism during speech, allowing for flexible interaction. The model leverages multimodal modeling by concatenating image, audio, and text features for comprehensive task performance, and uses text-guided delayed parallel output for real-time speech responses. It employs a multi-stage training approach, including encoder adaptation, modal alignment, and multimodal fine-tuning. The model is currently trained on English, though it can understand other languages supported by Whisper for audio encoding, with output remaining in English.

The Marketing Strategy Generator is an open-source project built on the CrewAI framework, designed to automate the creation of detailed marketing strategies. It orchestrates autonomous AI agents to collaborate on complex tasks, from analyzing market trends to developing compelling marketing content. Users can configure environment variables, install dependencies, and customize agent inputs and tasks through YAML files. The tool uses GPT-4o by default, allowing for advanced AI capabilities in generating strategic insights. It provides a structured approach to marketing strategy development, making it a valuable resource for those looking to leverage AI for efficient planning.

Mergoo is an open-source Python library designed to simplify the process of merging multiple Large Language Model (LLM) experts and then efficiently training the resulting merged LLM. It enables users to integrate knowledge from different generic or domain-specific LLM experts, supporting methods such as Mixture-of-Experts (MoE) and Mixture-of-Adapters (MoA). The library offers flexible merging for each layer and supports popular base models like Llama (including LLaMa3), Mistral, Phi3, and BERT. It is compatible with various trainers including Hugging Face Trainer, SFTrainer, and PEFT, and can run on CPU, MPS, and GPU devices. Mergoo allows for training choices ranging from only the Router of MoE layers to fully fine-tuning the merged LLM.

Mallet is an open-source, Java-based package designed for statistical natural language processing and machine learning applications to text. It provides sophisticated tools for document classification, including efficient text-to-feature conversion, various algorithms like Naïve Bayes and Maximum Entropy, and performance evaluation metrics. Beyond classification, Mallet supports sequence tagging for tasks such as named-entity extraction using algorithms like Hidden Markov Models and Conditional Random Fields. Its topic modeling toolkit offers efficient, sampling-based implementations of Latent Dirichlet Allocation and Hierarchical LDA. The package also includes routines for transforming text documents into numerical representations through a flexible system of "pipes" for tokenizing, stopword removal, and count vector conversion. Mallet is ideal for researchers and practitioners working with large text datasets.

DetGPT is an innovative AI tool designed for object detection through advanced reasoning capabilities. Unlike traditional object detection systems, DetGPT not only identifies objects but also understands complex instructions, allowing it to locate targets based on abstract concepts. For instance, it can identify "blood pressure-reducing foods" in an image by recognizing potassium-rich items like bananas. This ability to provide answers beyond human common sense, such as identifying unfamiliar fruits rich in potassium, makes it a powerful tool for various applications. The project is built upon the open-vocabulary detector GroundingDino and the multimodal conversation model MiniGPT-4, leveraging large language models (LLMs) for its reasoning prowess. It is available as an open-source project on GitHub, providing installation instructions and an online demo for users to explore its features.

DGL-LifeSci is an open-source Python package built on DGL (Deep Graph Library) specifically designed for deep learning applications in life sciences using graph neural networks. It provides a comprehensive suite of tools for researchers and developers, including methods for constructing and featurizing molecular graphs and biological networks, evaluating models, and offering various model architectures. The package also includes training scripts and pre-trained models to accelerate research and development. DGL-LifeSci supports applications such as molecular property prediction and reaction prediction, making it a valuable resource for advancing drug discovery and bioinformatics.