#Code Generation

Discover the capabilities of AI in code generation with GPT Pilot, where LLMs can produce nearly complete production-ready applications, complemented by developer adjustments. Seamlessly integrate with platforms like Docker and PostgreSQL. Engage with the Discord community and access insightful blogs. Compatible with Python 3.9+, utilizing advanced AI like OpenAI, this tool ensures efficient workflows via CLI and Docker. Streamline your development process with AI-driven solutions.

Agentok Studio provides a user-friendly platform for developing and managing complex Multi-Agent Applications with drag-and-drop tools. It uses the AutoGen framework to simplify Python-based workflow creation. Key features include conversation relationships and a developing RAG content management system. The platform supports both local deployment with and without Docker. Community contributions are welcome to enhance the development experience.

Access a rich collection of prompts tailored for a variety of large language models such as Siri and GPT-4o. This repository offers practical solutions for text manipulation, medical queries, programmatic support, and prompt creation. Ideal for developers and researchers interested in enhancing AI model interactions. Connect with a thriving Discord community for collaboration and support, and explore detailed use cases for improved AI outcomes.

LangChain Coder AI utilizes models from OpenAI and Google Vertex AI to generate versatile, high-quality code snippets. It is tailored for developers needing modular and optimized code across various languages. The advanced code editor allows customization of the development environment with settings such as font size and themes. Supports both offline and online compilation, and integrates AI models like GPT-4 and Code Gecko, ensuring precise code completion. It's a robust tool for working with PALM and GEMINI AI models, meeting diverse programming requirements.

This research investigates the potential of deep learning to automatically convert graphical user interface (GUI) screenshots into functional code for iOS, Android, and web platforms. With over 77% accuracy, this experimental project highlights the role of machine intelligence in streamlining software development processes. While it may not be suitable for real-world production, the project encourages innovation by providing source code and datasets for educational purposes and explores implications for designer-developer collaboration and automating repetitive coding tasks.

Nuxt-GraphQL-Client is a streamlined GraphQL client offering an uncomplicated setup and code generation specifically for Nuxt 3. It supports TypeScript and features hot module reload for GraphQL documents, alongside integration with the GraphQL Code Generator. Authored by GitHub user @diizzayy, it enhances the coding process by efficiently handling both the configuration and execution of GraphQL queries, making it a valuable tool for developers seeking simplicity and performance in their Nuxt projects.

This objective survey examines the intersection of NLP and software engineering via language models for code. It presents a chronological categorization of research papers, providing insights into basic language models, their adaptations for code, and pretraining methods. Key topics covered include reinforcement learning on code, analysis of AI-generated code, low-resource languages, and practical tasks such as code translation and program repair. Additionally, the survey includes recommended readings for those new to NLP, and updates on notable papers, serving as a valuable resource for understanding developments and uses of large language models in code-related fields.