01.AI's Yi-Coder, an open-source AI coding assistant
Key Features:
- 9B and 1.5B parameter versions
- 52 programming languages
- context length of 128,000 tokens
- code editing, completion, debugging, and mathematical reasoning
Key Features:
- 9B and 1.5B parameter versions
- 52 programming languages
- context length of 128,000 tokens
- code editing, completion, debugging, and mathematical reasoning
Cody is an AI coding assistant that uses advanced search and codebase context to help you understand, write, and fix code faster. It supports autocomplete, code generation, and explanation in various IDEs and code hosts.
Unblocked is an AI tool that augments code with knowledge from systems like GitHub, Slack, Confluence, and Jira to provide quick, accurate answers about your application.
Discover how GitHub engineers leverage GitHub Copilot to automate repetitive tasks, stay focused, and even explore new technologies.
The GitHub Blog article "4 ways GitHub engineers use GitHub Copilot" discusses how different GitHub engineers leverage GitHub Copilot to enhance efficiency and productivity in their work. The main use cases highlighted in the article are:
1. Semi-automating repetitive tasks: A GitHub engineer uses GitHub Copilot to automate the process of incrementing ID numbers in protobuf definitions, significantly reducing manual work.
2. Avoiding distractions: A colleague develops a regular expression to capture a Markdown code block and extract the language identifier. By prompting GitHub Copilot with a code comment, the regular expression is generated swiftly and accurately.
3. Structuring data-related notes: A support engineer at GitHub uses GitHub Copilot Chat to structure and compile their notes into Markdown tables, enabling efficient documentation of a complex troubleshooting process.
4. Exploring and learning: A colleague uses GitHub Copilot to develop a program in Rust, a programming language he was not previously familiar with. In just 23 minutes, he successfully created a functional program that converts numerical input into written English equivalents.
The GitHub Blog article "4 ways GitHub engineers use GitHub Copilot" discusses how different GitHub engineers leverage GitHub Copilot to enhance efficiency and productivity in their work. The main use cases highlighted in the article are:
1. Semi-automating repetitive tasks: A GitHub engineer uses GitHub Copilot to automate the process of incrementing ID numbers in protobuf definitions, significantly reducing manual work.
2. Avoiding distractions: A colleague develops a regular expression to capture a Markdown code block and extract the language identifier. By prompting GitHub Copilot with a code comment, the regular expression is generated swiftly and accurately.
3. Structuring data-related notes: A support engineer at GitHub uses GitHub Copilot Chat to structure and compile their notes into Markdown tables, enabling efficient documentation of a complex troubleshooting process.
4. Exploring and learning: A colleague uses GitHub Copilot to develop a program in Rust, a programming language he was not previously familiar with. In just 23 minutes, he successfully created a functional program that converts numerical input into written English equivalents.
The author tests the new GPT-4o AI from OpenAI on a standard set of coding tests and finds that it delivers good results, but with one surprising issue.
