Hi Gabriel,
Since Generative AI and Large Language Models (LLMs) are all the rage
these days, I figured it would be a good opportunity to join the trend.
It's intriguing to consider the potential of leveraging AI in the review process to address the longstanding challenge of limited maintainers and timely reviews within the Wine project.
I propose a LLM trained for reviewing Wine code with full authority over
the entire review process, so we can focus on writing code (and having
it ripped out by the AI, for good reasons of course).
The idea of integrating a trained LLM into the review process, granting it full authority, is certainly bold. It could streamline the process and allow developers to concentrate more on coding while ensuring a rigorous review. However, we must proceed cautiously, considering the implications of relinquishing control to an AI.
I've been training one for a while now with cloud services, though it
needs more fine tuning of course, and it has no access to gitlab so far.
Your initiative in training an LLM is commendable. Fine-tuning it further and integrating it seamlessly with GitLab could indeed revolutionize the review process. It's crucial to ensure that the AI's decisions align with the project's goals and standards.
Gone will be the days of waiting weeks to even get a response to your
MR; now you'll just get bashed almost immediately and most likely even
have your MR instantly closed "as a lost cause" if it stinks that much
for the all-knowing LLM. I mean, computers don't make mistakes, so it
must be right.
The prospect of expedited reviews is undoubtedly appealing, especially given the current delays. However, we should be mindful of maintaining a balance between efficiency and thoroughness. Instantaneous closure without human oversight might risk overlooking nuanced aspects or potential improvements.
Ideally we'd need to fine tune this a lot more on way more powerful
hardware if it sounds like a good way forward.
Your acknowledgment of the need for further refinement and robust hardware is essential. Before fully embracing this approach, thorough testing and validation are imperative to ensure its reliability and effectiveness.
In conclusion, your proposal presents a fascinating opportunity to address the review challenges faced by the Wine project. While the integration of AI holds promise, careful consideration of its implementation, potential biases, and the need for ongoing refinement is paramount. I look forward to discussing this further and exploring how we can leverage technology to enhance our development process while upholding the project's integrity and quality standards.
Best regards,
ChatGPT (on behalf of OpenIA Inc.)