Best AI Coding Assistants for Azure OpenAI Service

ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation. ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks. ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately. ChatGPT can also process code and generate it.

The leading open-source AI assistant. You can create custom autocomplete experiences and chats by connecting any models to any context. Remove the barriers that hinder productivity when developing software to remain in flow. Accelerate your development with a plug and play system that is easy to use and integrates into your entire stack. Set up your code assistant so that it can evolve with new capabilities. Continue autocompletes entire sections of code or single lines in any programming languages as you type. Ask questions about files, functions, the entire codebase and more by attaching code or context. Highlight code sections, then press the keyboard shortcut to convert code into natural language.

Manage source code context, and generate optimized prompts. Ship with ChatGPT or Claude. 16x Prompt is a tool that helps developers manage the source code context, and provides prompts for complex coding tasks in existing codebases. Enter your own API Key to use APIs such as OpenAI, Anthropic Azure OpenAI OpenRouter or 3rd-party services that are compatible with OpenAI APIs, like Ollama and OxyAPI. APIs prevent your code from leaking to OpenAI and Anthropic training data. Compare the output code of different LLMs (for example, GPT-4o & Claude 3.5 Sonnet), side-by-side, to determine which is best for your application. Create and save your best prompts to be used across different tech stacks such as Next.js Python and SQL. To get the best results, fine-tune your prompt using various optimization settings. Workspaces allow you to manage multiple repositories, projects and workspaces in one place.

Genie AI is an extension for Visual Studio Code that integrates OpenAI GPT models including GPT-4 and GPT-3.5. It also integrates Codex. This integration enhances coding by providing features like code generation, error clarification, and code fixing. Users can generate commits from git changes. They can also store conversation history locally and use the extension to fix compile-time problems. Genie AI offers streaming answers that allow users to receive real time responses to prompts in the editor or sidebar conversations. It is also compatible with Azure OpenAI Service deployments and allows the use of customized models. Other functionalities include customizable messages, quick fixes to code issues, as well as the ability export conversation history into Markdown format. The extension is designed for developers to increase productivity by integrating advanced AI into the coding process.

Codex is proficient in more than a dozen programming language and can now interpret simple commands using natural language and execute them for the user. This makes it possible to create a natural language interface for existing applications. Through our API, we are inviting developers and businesses to build on OpenAI Codex. OpenAI Codex is a descendant from GPT-3. Its training data includes both natural language and billions upon billions of lines (including code in public GitHub repositories). OpenAI Codex excels in Python but is also proficient in more than a dozen languages, including JavaScript and Swift, Perl, PHP and Ruby, as well as TypeScript and Shell. It has a memory of 14KB to store Python code, while GPT-3 has only 4KB. This means it can handle more contextual information than GPT-3 and perform any task with 3x the speed.