#python #agents #ai_agents #ai_agents_framework #artificial_intelligence #developer_tools #devtools #generative_ai #knowledge_graph #memory #rag

Potpie is an open-source platform that helps you automate code analysis, testing, and development tasks. It creates AI agents that understand your codebase deeply, allowing them to assist with debugging, feature development, and more. You can use pre-built agents for common tasks like debugging and testing, or create custom agents to handle specific needs. Potpie integrates seamlessly into your existing development workflow and works with codebases of any size or language. This makes it easier for developers to understand the codebase quickly, review code changes, and generate tests, saving time and improving efficiency.

https://github.com/potpie-ai/potpie

#lua #ai #ai_gateway #api_gateway #api_management #apis #artificial_intelligence #cloud_native #consul #devops #docker #kong #kubernetes #kubernetes_ingress #kubernetes_ingress_controller #luajit #microservice #microservices #nginx #reverse_proxy #serverless

Kong API Gateway is a powerful tool that helps manage and secure your APIs. It offers features like advanced routing, load balancing, health checking, and authentication, making it easier to handle API traffic. Kong is highly scalable and can run on various platforms, including Kubernetes. It also supports plugins for additional functionalities such as AI traffic management and custom extensions. By using Kong, you can centralize your API management, focus on other important tasks, and ensure your APIs are secure and perform well. You can get started quickly with a free trial or by following the easy installation steps.

https://github.com/Kong/kong

#jupyter_notebook #agents #artificial_intelligence #generative_ai #llms #rag

This repository helps you learn and build Generative AI applications using MongoDB. It includes many examples and sample apps for different AI tasks, such as Retrieval-Augmented Generation (RAG) and AI Agents. You can find Jupyter notebooks, JavaScript and Python apps, and contributions from AI partners. To get started, you need to create a free MongoDB Atlas account, set up a database cluster, and get the connection string. This resource benefits you by providing step-by-step guides and support, making it easier to integrate MongoDB into your AI projects and learn from community resources.

https://github.com/mongodb-developer/GenAI-Showcase

#typescript #ai #artificial_intelligence #browser #browser_automation #gpt #gpt_4 #langchain #llama #llm #openai #playwright #puppeteer #scraper

LLM Scraper is a tool that helps you get structured data from any webpage using large language models (LLMs). It supports different AI providers like OpenAI and Ollama, and it uses the Playwright framework to work with web pages. You can define what data you want to extract using schemas, which makes sure everything is organized correctly. This tool also allows you to generate code automatically for scraping tasks, making it easier to reuse scripts. The benefit is that you can easily collect data from websites in a structured way, which is helpful for projects that need specific information from the internet.

https://github.com/mishushakov/llm-scraper

#python #artificial_intelligence #llm #python #real_time #speech_to_text #text_to_speech

FastRTC is a Python library that helps you create real-time audio and video streams using WebRTC or WebSockets. It allows you to turn any Python function into a live stream, making it useful for applications like voice chats or video conferencing. Key features include automatic voice detection, built-in UI support with Gradio, and integration with FastAPI for custom frontends. This library simplifies the process of handling real-time communication, allowing developers to focus on their application logic rather than complex streaming setups.

https://github.com/freddyaboulton/fastrtc

#python #ai #artificial_intelligence #cython #data_science #deep_learning #entity_linking #machine_learning #named_entity_recognition #natural_language_processing #neural_network #neural_networks #nlp #nlp_library #python #spacy #text_classification #tokenization

spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.

https://github.com/explosion/spaCy

#python #agent #ai_societies #artificial_intelligence #communicative_ai #cooperative_ai #deep_learning #large_language_models #multi_agent_systems #natural_language_processing

CAMEL-AI is a community-driven project focused on multi-agent systems. It helps researchers study how AI agents interact and behave in large-scale environments. This platform supports tasks like data generation, task automation, and world simulation. By using CAMEL-AI, users can create complex scenarios where multiple agents collaborate to solve problems or generate synthetic data. The benefits include gaining insights into agent behaviors, improving decision-making processes, and enhancing collaboration among AI entities. It's open-source and easy to install via PyPI.

https://github.com/camel-ai/camel

#csharp #ai #artificial_intelligence #llm #openai #sdk

Semantic Kernel is a tool that helps developers build and manage AI systems easily. It supports multiple programming languages like C#, Python, and Java, making it versatile for different projects. This tool allows you to connect your AI models to various services and databases, which helps in automating tasks and making decisions based on user inputs. It's especially useful for businesses because it's reliable, secure, and can handle complex workflows. By using Semantic Kernel, developers can create intelligent AI agents that can interact with users and perform tasks efficiently.

https://github.com/microsoft/semantic-kernel

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#jupyter_notebook #ai #artificial_intelligence #chatgpt #deep_learning #from_scratch #gpt #language_model #large_language_models #llm #machine_learning #python #pytorch #transformer

You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4].

https://github.com/rasbt/LLMs-from-scratch

#other #artificial_intelligence #artificial_intelligence_projects #awesome #computer_vision #computer_vision_project #data_science #deep_learning #deep_learning_project #machine_learning #machine_learning_projects #nlp #nlp_projects #python

You can access a huge, constantly updated list of over 500 artificial intelligence projects with ready-to-use code covering machine learning, deep learning, computer vision, and natural language processing. This collection includes projects for beginners and advanced users, with links to tutorials, datasets, and real-world applications like chatbots, healthcare, and time series forecasting. Using this resource helps you learn AI by doing practical projects, speeding up your coding skills, and building a strong portfolio for jobs or research. It saves you time searching for quality projects and gives you tested, working code to study and modify.

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#jupyter_notebook #artificial_intelligence #book #large_language_models #llm #llms #oreilly #oreilly_books

You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs.

https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

#python #agent #alibaba #artificial_intelligence #information_seeking #llm #multi_agent #rag #web_agent

You can use advanced AI models like WebSailor and WebDancer from Alibaba's Tongyi Lab to perform complex web tasks such as searching, browsing, and answering questions automatically. These models are trained to think deeply and handle difficult information-seeking tasks that were hard before. WebSailor excels in reasoning and can solve very challenging problems, while WebDancer learns to search and reason on its own through a special training process. Using these tools helps you get accurate, multi-step answers from the web quickly and efficiently, saving you time and effort in research or information gathering. They are open-source and come with demos to try out easily[3].

https://github.com/Alibaba-NLP/WebAgent

#cplusplus #artificial_intelligence #cloud #cloud_native #cncf #container #docker #edge_computing #ewasm #hacktoberfest #hacktoberfest2023 #kubernetes #rust_lang #serverless #wasm #webassembly

WasmEdge is a fast, lightweight, and secure WebAssembly runtime that lets you run programs safely on your devices, servers, or the cloud. It supports many programming languages like C++, Rust, and JavaScript, and can run AI models, microservices, and smart contracts efficiently. WasmEdge offers strong security by isolating programs, making it great for extending software safely. It works well on edge devices, smart devices, and cloud environments, and supports easy integration with tools like Kubernetes and Docker. Using WasmEdge helps you run powerful applications faster, safer, and more flexibly on various platforms[1][2][3][4][5].

https://github.com/WasmEdge/WasmEdge

#typescript #agent_workflow #agentic_workflow #agents #ai #aiagents #anthropic #artificial_intelligence #automation #chatbot #deepseek #gemini #low_code #nextjs #no_code #openai #rag #react #typescript

Sim Studio is an easy-to-use, open-source platform that lets you build AI workflows visually without coding by dragging and connecting blocks on a canvas. It supports many AI models and integrates with over 60 popular tools like Gmail, Slack, and Google Sheets. You can run workflows via chat, APIs, or scheduled jobs and deploy them as APIs or plugins. It also offers real-time collaboration and built-in monitoring. This helps you quickly create, test, and deploy AI-powered applications or automation, saving time and effort while allowing flexibility and control over your AI projects[1][2][3][4].

https://github.com/simstudioai/sim

#other #agent #ai #artificial_intelligence #autogpt #autonomous_agents #awesome #babyagi #copilot #gpt #gpt_4 #gpt_engineer #openai #python

Codeium is a free AI-powered coding assistant that helps you write code faster and better by providing real-time autocomplete suggestions, generating code from natural language, explaining code, and assisting with refactoring. It supports over 70 programming languages and integrates with many popular IDEs like Visual Studio Code. Codeium learns from your coding style and project context to offer relevant suggestions, saving you time and reducing errors. It also includes a chat feature to answer coding questions instantly, so you don’t need to switch to a browser for help. This boosts your productivity and code quality efficiently.

https://github.com/e2b-dev/awesome-ai-agents

#python #artificial_intelligence #cybersecurity #generative_ai #llm #pentesting

Cybersecurity AI (CAI) is an open-source, lightweight framework that helps you build AI agents to find and fix security vulnerabilities efficiently. It supports many AI models and tools, works on multiple operating systems, and allows human control during tasks. CAI automates complex security testing steps like scanning, exploiting, and validating bugs, making bug bounty hunting easier and faster. It also logs detailed traces for better analysis and supports teamwork among AI agents. Using CAI can boost your cybersecurity skills, save time, and improve your ability to protect systems from attacks by combining AI power with your expertise.

https://github.com/aliasrobotics/cai

#rust #artificial_intelligence #big_data #data_engineering #distributed_computing #machine_learning #multimodal #python #rust

Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns.

https://github.com/Eventual-Inc/Daft

#python #artificial_intelligence #cloud_ml #computer_systems #courseware #deep_learning #edge_machine_learning #embedded_ml #machine_learning #machine_learning_systems #mobile_ml #textbook #tinyml

You can learn how to build real-world AI systems from start to finish with an open-source textbook originally from Harvard University. It teaches you not just how to train AI models but how to design scalable systems, manage data pipelines, deploy models in production, monitor them continuously, and optimize for devices like phones or IoT gadgets. This helps you become an engineer who can create efficient, reliable, and sustainable AI systems that work well in practice. The book offers hands-on labs, community support, and free online access, making it easier to gain practical skills in machine learning systems engineering.

https://github.com/harvard-edge/cs249r_book

#python #ant_colony_algorithm #artificial_intelligence #fish_swarms #genetic_algorithm #heuristic_algorithms #immune #immune_algorithm #optimization #particle_swarm_optimization #pso #simulated_annealing #travelling_salesman_problem #tsp

You can use scikit-opt, a Python library offering many heuristic optimization algorithms like Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony, Immune Algorithm, and Artificial Fish Swarm Algorithm. It supports user-defined functions to customize operators, allows continuing runs from previous iterations, and accelerates computations via vectorization, multithreading, multiprocessing, and caching. GPU support is in development. It helps solve complex optimization problems such as function minimization and the Traveling Salesman Problem efficiently, with easy installation and rich examples. This saves you time and effort in implementing and tuning optimization algorithms yourself.

https://github.com/guofei9987/scikit-opt