_ryanjsalva

Gemini CLI team member here. We'll start rolling out today.

100% - It's on our list!

We started using Gemini CLI to build itself after about week two. If I had to guess, I'd say better than 80% of the code was written with Gemini CLI. Honestly, once we started using the CLI, we started experimenting a lot more and building waaaaay faster.

I also work on the product. You can extend the tools with MCP. https://github.com/google-gemini/gemini-cli/blob/main/docs/t...

I firmly believe that GitHub Copilot isn't a replacement for thinking, breathing, reasoning developers on the other side of the keyboard. Nor is it a replacement for best practices that ensure proper code quality like linting, code reviews, testing, security audits, etc.

All the research suggests that AI-assisted auto-complete merely helps developers go faster with more focus/flow. For example, there's an NYU study that compared security vulnerabilities produced by developers with and without AI-assistend auto-complete. The study found that developers produced the same number of potential vulnerabilities whether they used AI auto-complete or not. In other words, the judgement of the developer was the stronger indicator of code quality.

The bottom line is that your expertise matters. Copilot just frees you up to focus on the more creative work rather than fussing over syntax, boilerplate, etc.

Probably more accurate to say, "LLM service provider." Ultimately, GitHub distributes a derivative of OpenAI's Codex – though, the version in production has been tuned considerably.

Good call. Done. https://twitter.com/docsparse/status/1581461734665367554?s=4...

Howdy, folks. Ryan here from the GitHub Copilot product team. I don’t know how the original poster’s machine was set-up, but I’m gonna throw out a few theories about what could be happening.

If similar code is open in your VS Code project, Copilot can draw context from those adjacent files. This can make it appear that the public model was trained on your private code, when in fact the context is drawn from local files. For example, this is how Copilot includes variable and method names relevant to your project in suggestions.

It’s also possible that your code – or very similar code – appears many times over in public repositories. While Copilot doesn’t suggest code from specific repositories, it does repeat patterns. The OpenAI codex model (from which Copilot is derived) works a lot like a translation tool. When you use Google to translate from English to Spanish, it’s not like the service has ever seen that particular sentence before. Instead, the translation service understands language patterns (i.e. syntax, semantics, common phrases). In the same way, Copilot translates from English to Python, Rust, JavaScript, etc. The model learns language patterns based on vast amounts of public data. Especially when a code fragment appears hundreds or thousands of times, the model can interpret it as a pattern. We’ve found this happens in <1% of suggestions. To ensure every suggestion is unique, Copilot offers a filter to block suggestions >150 characters that match public data. If you’re not already using the filter, I recommend turning it on by visiting the Copilot tab in user settings.

This is a new area of development, and we’re all learning. I’m personally spending a lot of time chatting with developers, copyright experts, and community stakeholders to understand the most responsible way to leverage LLMs. My biggest take-away: LLM maintainers (like GitHub) must transparently discuss the way models are built and implemented. There’s a lot of reverse-engineering happening in the community which leads to skepticism and the occasional misunderstanding. We’ll be working to improve on that front with more blog posts from our engineers and data scientists over the coming months.