GitHub Copilot is more effective if you provide it with more context. This means giving Copilot full visibility into projects, adding top-level comments, having meaningful names, and providing example code. Devs should also take advantage of Copilot’s built-in features, such as its inline chat.
Thursday, April 4, 2024GitHub Copilot analyzes code in your editor to understand what you’re working on and then sends gathered context to a backend service that sanitizes the input by removing harmful content and irrelevant prompts. The cleaned prompt is run through OpenAI’s ChatGPT API and then a final suggestion is presented in your editor.
Augment, a startup backed by Eric Schmidt (former CEO of Google), has launched to rival GitHub Copilot with a new AI-powered coding platform.
The recent hype around "AI developers" replacing software engineers is a marketing strategy by startups trying to break into a market dominated by Microsoft's GitHub Copilot. AI tools are still far from replacing human developers, but these startups need to make bold claims to get attention. While AI is changing how software is created, fully replacing engineers will either never happen or is a long time away.
To become a better developer in 2024, get better at using AI tools like GitHub Copilot, use shortcuts often, and focus on soft skills. It's best to use AI in a way that makes you more productive, for example, by automating boilerplate code and finding bugs early.
GitHub Copilot Extensions are a new way to enhance VS Code by integrating AI directly into the developer workflow. These extensions utilize LLMs to provide context-specific answers, generate code, and offer insights within VSCode itself. Developers can build custom chat participants using the Chat API and access LLMs directly through the Language Model API.
A US judge has dismissed a lawsuit alleging that GitHub Copilot violated open-source licenses by using code samples for AI training.
AI tools like GitHub Copilot enhance programming productivity but risk eroding essential coding skills. Over-reliance on AI-generated code can lead to quality, security, and maintainability issues and reduce learning opportunities. These tools may also limit creative problem-solving and foster a false sense of expertise among developers.