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The tailored AI model trained on our own codebase is a great feature. It improves relevance in suggestions over time.
The initial setup was quite complex, requiring a lot of configuration before we could start using it effectively.
It significantly reduces the time spent on debugging and code reviews, allowing our team to focus on new feature development.
The integration with our existing toolchain has been seamless after the initial setup, and the AI does a great job of learning from our code.
I wish it had more customizable features to adapt to our specific workflows better.
It reduces the time our developers spend on mundane tasks, allowing them to focus on creative problem-solving.
I appreciate the focus on security and the fact that it's self-hosted. This gives us more control over our codebase.
The AI model often lacks context about our specific codebase, leading to suggestions that can be off-target.
It helps to streamline some coding tasks, but the benefits are not as significant as I expected. It occasionally speeds up development, but not consistently.
I really like the security aspect. Being self-hosted means we can keep our source code safe from third-party access.
Sometimes it feels like the AI struggles to keep up with the latest programming trends and languages.
It helps in maintaining code quality by suggesting improvements and identifying potential bugs early in the development process.
The concept of a self-hosted AI coding assistant is promising, especially for enterprise security. However, the implementation has not been as smooth as I hoped.
The user interface feels outdated, and the integration with our existing tools is clunky. It has a steep learning curve.
It aims to help with code suggestions and automating repetitive tasks, but in practice, the suggestions are not always relevant.
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