Coding & Development

Agenty is an innovative AI platform designed to empower users to create and manage their own AI teams. This tool facilitates the rapid deployment of AI workers, each tailored to specific requirements, in just a few minutes. By enabling users to build a custom AI team, Agenty aims to streamline various tasks and processes, providing a flexible and adaptable solution for integrating artificial intelligence into operations. The platform is currently in a closed beta phase, indicating ongoing development and refinement to deliver a robust and user-centric experience.

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.

PICLY leverages artificial intelligence to automatically generate 'spot the difference' puzzles from various images. This tool enables users to create engaging visual comparison games or identify subtle alterations between two pictures. It serves purposes ranging from entertainment and educational content to visual inspection tasks. The AI analyzes uploaded images to create differences, providing a unique and customizable experience for game developers, content creators, and educators. While the website content is currently limited, the core functionality focuses on simplifying the creation of interactive visual puzzles.

Semantic Kernel (SK) is an open-source SDK developed by Microsoft, designed to help developers build intelligent applications by integrating large language models (LLMs) and AI capabilities. It offers a flexible framework for orchestrating AI plugins, managing prompts, and seamlessly connecting AI services with existing code and data. SK supports multiple programming languages like .NET and Python, providing tools for creating agents, defining skills, and handling complex conversational flows. Its features include chat history storage patterns, CodeAct for faster agent execution, and various ways to author agent skills, making it suitable for enterprise-grade multi-agent orchestration and cross-runtime interoperability.

AgentKit offers a unified framework for explicitly constructing complex human "thought processes" from simple natural language prompts. It utilizes a graph-based approach, where users connect nodes like LEGO pieces to design and enforce structured thought processes. This allows for the integration of various functionalities to build multifunctional agents. A basic agent can be implemented as a list of prompts for subtasks, making it accessible for users without programming experience. The framework supports dynamic modification of the Directed Acyclic Graph (DAG) at inference time, enabling advanced capabilities like branching based on LLM responses. AgentKit provides built-in LLM API support for OpenAI, Anthropic, and Ollama models, with options for token usage tracking and error handling.

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.

The llama_index_starter_pack is a repository offering foundational examples for integrating the llama_index package with Flask, Streamlit, and Docker. It's designed for developers looking to rapidly build proof-of-concept applications. The repository includes a basic demo featuring the classic "Paul Graham Essay" from the original llama_index repository, providing a familiar starting point. Examples cover a Flask API with a React frontend for vector store indexing, a Streamlit UI for vector search, a Streamlit app for SQL database interaction with Llama Index agents, and a Streamlit app for extracting and querying terms/definitions. Each example comes with a Dockerfile for easy containerization, making deployment straightforward.

Auto-evaluator is a lightweight, open-source evaluation tool designed for question-answering systems utilizing Langchain. It streamlines the process of assessing LLM QA chains by allowing users to input documents, then automatically generating question-answer pairs using GPT-3.5-turbo. The tool then uses a specified QA chain to generate responses to these questions and employs GPT-3.5-turbo again to score the responses against the generated answers. This enables users to explore and compare scoring across various chain configurations, making it an invaluable resource for developers and researchers working on improving the accuracy and performance of their LLM-powered QA applications. It can be run as a Streamlit app and offers configurable inputs for evaluation parameters.

Leaked-GPTs is an Open Source repository offering a collection of leaked GPT prompts. This tool is designed for users who wish to bypass the 25-message limit often imposed on GPT models or to experiment with various GPTs without requiring a paid Plus subscription. The repository includes a diverse range of prompts, from practical applications like generating memes and negotiating assistance to creative tasks such as writing coaching and image modification. It also features specialized GPTs for coding, health, finance, and entertainment, making it a versatile resource for developers, researchers, and enthusiasts looking to explore the capabilities of different GPT models.

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.

lumen is a command-line interface (CLI) tool designed to enhance the developer's workflow by offering a beautiful and ergonomic git diff viewer. It supports syntax highlighting and allows for in-line commenting during code reviews. Beyond visualization, lumen integrates AI capabilities to generate smart, conventional commit messages for staged changes, understand and explain code modifications, and even generate git commands from natural language queries. It supports multiple AI providers like OpenAI, Claude, Groq, and Ollama, offering flexibility in configuration. The tool also features interactive commit selection, stacked diff mode for reviewing multiple commits, and customizable themes, making it a comprehensive solution for modern code review and git operations.

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.