OpenAI Custom GPTs AI technology Top Builders

Explore the top contributors showcasing the highest number of OpenAI Custom GPTs AI technology app submissions within our community.

Custom GPTs: Redefining AI Customization

Custom GPTs redefine your ChatGPT experience, enabling the creation of personalized AI models tailored to specific tasks. Whether you strive to optimize your project management workflows, simplify complex legal jargon, or generate captivating marketing copy, Custom GPTs offer a versatile solution to meet your needs.

General
Release dateNovember 6, 2023
AuthorOpenAI
TypeCustomizable AI model

Custom GPTs redefine ChatGPT's capabilities, enabling tailored AI models that seamlessly integrate external data sources, catering to personal, corporate, or public requirements without coding skills.

Key Features

  • Tailored Solutions: Customize AI models for various needs.
  • Real-World Integration: Seamlessly incorporate external data sources.
  • Simplified Creation: Build AI models without coding knowledge.

For more details and updates, refer to the OpenAI Blog - Introducing GPTs and explore diverse Use Cases demonstrating the potential of Custom GPTs.

GPTs Tutorials

    👉 Discover more Custom GPTs Tutorials on lablab.ai


    OpenAI Custom GPTs AI technology Hackathon projects

    Discover innovative solutions crafted with OpenAI Custom GPTs AI technology, developed by our community members during our engaging hackathons.

    Edulance-AI

    Edulance-AI

    Edulance is an open-source project that utilizes advanced technologies such as Unstructured, machine learning models, and APIs to transform text documents and PDFs into interactive educational resources. The software accepts user-uploaded files, applies optical character recognition (OCR) for text documents, or extracts valuable content from PDFs. It then generates lessons, quizzes, and lesson plans based on the content using its Lesson Graph model and agents like LessonGenerator, LessonPlanner, OCRAgent, PdfAgent, QuizAgent, and TogetherLLM. Edulance provides an immersive learning experience, enabling effective teaching and interactive knowledge acquisition. Overall this project incorporates the following: TogetherAI's LLM Models Unstructured Partition pdf for making PDFs LLM Ready Agentic AI with state management. Features Feature Description ⚙️ Architecture Edulance is a Python-based project using FastAPI as the web framework and Uvicorn for runtime serving. The application leverages containers with Docker for deployment, installing required dependencies from requirements.txt. It utilizes libraries like LangChain, PikePDF, PyTesseract for OCR, and TogetherAI's LLM models. 🔩 Code Quality The codebase follows a modular structure with clearly defined agents and graph files, ensuring high cohesion and low coupling. Python style guides are followed consistently, including PEP8 and PEP534. There is adequate usage of comments throughout the codebase.🔌 Integrations Key integrations include Docker for deployment, LangChain libraries, TogetherAI's LLM models, Vectara for Chat. 🧩 Modularity 📦 Dependencies Main dependencies include FastAPI, Docker, Python 3.10, requirements.txt, LangChain package, PikePDF, PyTesseract, and related tools.

    Massar team submission

    Massar team submission

    Masaar is described as a personal companion with the following capabilities: 1. Deep Understanding: It claims to surpass your self-knowledge, implying it can analyze your personality, preferences, and strengths better than you can yourself. This could involve using advanced data analysis or machine learning to process information about you from various sources (e.g., your interactions with the companion, your online activity). 2. Personalized Learning: Based on its deep understanding, Masaar would then tailor its approach to teach you new skills. It would consider your unique learning style, pace, and preferences to create an optimal learning experience. This could involve: - Identifying if you learn best by doing, watching, or reading. - Adjusting the difficulty and pace of learning materials based on your progress. - Recommending learning methods that resonate with you (e.g., gamification, interactive exercises). Think of it this way: Imagine a highly knowledgeable and adaptable tutor who can constantly assess your strengths, weaknesses, and learning style. Masaar strives to be that kind of companion, dynamically adjusting its approach to maximize your learning potential. However, it's important to consider some limitations: - Data Privacy: The level of deep understanding Masaar claims might raise concerns about data privacy and how it collects information about you. - Accuracy of Self-Knowledge: The idea of surpassing self-knowledge is debatable. While Masaar might identify patterns you haven't noticed, true self-awareness is a complex process. - Limited Scope: It's unclear how Masaar would define "new skills." Can it teach complex physical skills or require real-world practice? Overall, Masaar presents an intriguing concept for a future personalized learning companion. However, it's essential to be mindful of potential limitations and ensure it operates with transparency and user control over data.