#python #ai_art #huggingface #huggingface_diffusers #machine_learning #stable_diffusion

https://github.com/nateraw/stable-diffusion-videos

#jupyter_notebook #data_analysis #data_science #data_visualization #pandas #python

This curriculum is designed to help beginners learn data science over 10 weeks with 20 detailed lessons. Each lesson includes pre- and post-lesson quizzes, step-by-step guides, knowledge checks, and assignments to ensure you retain the information. You'll learn about data ethics, statistics, working with different types of data, data visualization, and the entire data science lifecycle. The project-based approach helps you build practical skills while learning. Additionally, there are resources for students and teachers to make the learning process flexible and engaging. This curriculum is beneficial because it provides a structured and interactive way to gain hands-on experience in data science, making it easier to understand and apply these skills in real-world scenarios.

https://github.com/microsoft/Data-Science-For-Beginners

#python #evaluation_framework #evaluation_metrics #llm_evaluation #llm_evaluation_framework #llm_evaluation_metrics

DeepEval is an open-source tool that makes it easy to test and improve large language model (LLM) applications, much like how Pytest works for regular software, but focused on LLM outputs. It offers over 30 ready-to-use metrics—such as answer relevancy, faithfulness, and hallucination—to check if your LLM is accurate, safe, and reliable. You can test your whole application or just parts of it, and even generate synthetic data for better testing. DeepEval works locally or in the cloud, letting you compare results, share reports, and keep improving your models. This helps you build better, safer, and more trustworthy LLM apps with less effort[1][2][3].

https://github.com/confident-ai/deepeval

#python #agents #ai #anthropic #llm #openai #python

You can use this Cookbook to quickly add ready-made AI code snippets to your projects, saving you time and effort in building AI systems. It offers practical tutorials and resources to help you learn AI development, start freelancing, or get expert help on your AI projects. Joining the free community can support your learning, and the GenAI Launchpad helps you build AI applications faster. This means you can develop real-world AI solutions more easily and grow your skills or business with guidance from an experienced AI engineer.

https://github.com/daveebbelaar/ai-cookbook

#python #agentic_coding #llm_agent

DeepCode is an AI-powered platform that automates turning research papers, text, and URLs into high-quality, production-ready code for algorithms, front-end, and back-end development. It uses a multi-agent system to analyze complex documents, plan code structure, find relevant code references, and generate tested, documented code efficiently. You can interact with it via a user-friendly web interface or a professional command-line interface. This saves you time and effort by automating complex coding tasks, speeding up development, and reducing errors, letting you focus on innovation rather than manual coding details. It supports integration with popular tools and offers real-time progress tracking.

https://github.com/HKUDS/DeepCode

#python #agents #ai #llm #mcp

You can access a large collection of ready-to-use AI agent projects and tutorials that help you build smart applications like chatbots, research assistants, and automation tools using popular AI frameworks such as LangChain, OpenAI Agents SDK, and Agno. This collection includes simple starter agents, advanced multi-agent workflows, and tools with memory and document understanding. It also offers step-by-step setup instructions and video tutorials to help you learn quickly. Using these resources saves you time and effort in creating powerful AI apps, making it easier to develop, test, and deploy AI solutions even if you are new to AI programming.

https://github.com/Arindam200/awesome-ai-apps

#python #agents #artificial_intelligence #cybersecurity #generative_ai #llm #penetration_testing

Strix is a free, open-source tool that uses AI agents to automatically find and fix security problems in your apps by acting like real hackers—running your code, hunting for vulnerabilities, and proving they’re real by actually exploiting them, not just guessing[1][2]. It works fast, gives clear reports, and can even suggest fixes or create pull requests to help you secure your code quickly. You can run it on your own computer, in your development pipeline, or use a cloud version for easier setup. The main benefit is that you get thorough, real-world security testing without the slow pace and high cost of manual checks, helping you catch and fix issues before they become serious problems.

https://github.com/usestrix/strix

#python

You can use Tinker and Tinker Cookbook to easily fine-tune large language models (LLMs) for your specific needs without managing complex training infrastructure. Tinker handles distributed training and uses efficient LoRA adapters to reduce costs and speed up customization. The Cookbook offers ready-made examples and tools for tasks like chat, math reasoning, and reinforcement learning, helping you quickly build and improve AI models. This means you can create AI that better fits your domain, runs faster, and follows your rules, all while saving time and computing resources. It’s great for researchers, developers, and teams wanting powerful, flexible AI customization.

https://github.com/thinking-machines-lab/tinker-cookbook

#python

This project teaches you how to build a real-world AI research assistant that automatically finds, reads, and answers questions about academic papers using a technique called Retrieval-Augmented Generation (RAG)[1][2][3]. RAG works by first searching for the most relevant information from a large collection of documents, then using a language model to generate clear, accurate answers based on that information—this means you get answers that are up-to-date and grounded in real sources, not just what the AI remembers from its training[1][2][3]. The course is hands-on: each week, you add a new piece, starting with setting up the technical infrastructure, then building automated data pipelines to fetch and process papers, adding powerful search tools (first with keywords, then with AI-powered semantic search), and finally connecting everything to a local AI model that can chat with you and explain complex topics in simple language. By the end, you’ll have a working system you can use to quickly find and understand research papers, and you’ll gain the skills to build similar AI tools for any field—all while learning the best practices used by professional engineers. The main benefit is that you get practical, production-ready AI skills and a tool that makes research faster and more reliable, with answers you can trust because they come directly from the latest papers.

https://github.com/jamwithai/arxiv-paper-curator

#python #data_analysis #dingtalk_robot #docker #feishu_robot #hot_news #mail #mcp #mcp_server #news #ntfy #python #telegram_bot #trending_topics #wechat_robot

TrendRadar is a lightweight, easy-to-deploy tool that gathers trending topics from 11+ major platforms like Zhihu, Douyin, and Baidu in just 30 seconds. It lets you set custom keywords to filter only news you care about, eliminating information overload. The tool offers three smart notification modes—daily summaries, current rankings, or incremental alerts—and supports multiple channels including WeChat Work, Feishu, DingTalk, Telegram, and email. You can customize how trends are ranked using a personalized algorithm that weighs ranking position, frequency, and hotness. With GitHub Pages for web reports, Docker support, and AI-powered analysis through MCP protocol, TrendRadar transforms scattered platform algorithms into one unified, user-controlled news feed tailored to your interests.

https://github.com/sansan0/TrendRadar

#python #ai #faiss #gpt_oss #langchain #llama_index #llm #localstorage #offline_first #ollama #privacy #python #rag #retrieval_augmented_generation #vector_database #vector_search #vectors

LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop.

https://github.com/yichuan-w/LEANN

#python #agent #ai #aiagent #awesome #chatgpt #hacktoberfest #hacktoberfest2025 #llm #long_short_term_memory #memori_ai #memory #memory_management #python #rag #state_management

Memori is an open-source memory engine that gives AI language models human-like memory using standard SQL databases like PostgreSQL, MySQL, or SQLite.[1][2] With just one line of code, you can enable any LLM to remember conversations, learn from interactions, and maintain context across sessions.[1] The key benefits are significant cost savings of 80-90% compared to expensive vector databases, complete data ownership and transparency since memories are stored in SQL databases you control, and zero vendor lock-in allowing you to export and move your data anywhere.[1][3] Memori works with popular frameworks like OpenAI, Anthropic, and LangChain, making it easy to integrate into existing projects without complex setup.[1]

https://github.com/GibsonAI/Memori

#python #ai #assistant #language_model #machine_learning #python #speech #speech_synthesis #speech_to_text #speech_translation

I can help you build a fast, modular voice agent that turns speech into text, sends it to a language model, then speaks the answer back. It works with open-source or hosted models, can run fully local on your own hardware, and supports live transcription and low-latency conversation, so you get a flexible voice app that you can customize for speed, privacy, and different devices.

https://github.com/huggingface/speech-to-speech

#python #audiobook #faster_whisper #gradio #karaoke #podcasts #speech_recognition #speech_synthesis #speech_to_text #subtitles #text_to_speech #transcription #translator #tts #voice_cloning #voice_conversion #webui #whisper #whisperx #yt_dlp

Voice-Pro is a free, open-source Windows app that lets you download YouTube videos, separate voices, turn speech into text, translate into 100+ languages, and create new speech or cloned voices. It gives you one tool for subtitles, dubbing, and voice work, so you can save time and make multilingual content more easily.

https://github.com/abus-aikorea/voice-pro