Devin is a dedicated, capable partner that is equally willing to work independently to finish projects for you to examine or to build with you. With Devin, engineering teams can aim higher and engineers can concentrate on more fascinating issues.
The Skills Of Devin
Devin is now capable of organizing and carrying out intricate engineering jobs involving hundreds of decisions thanks to our advancements in long-term thinking and planning.
Devin is able to learn over time, correct errors, and remember essential information at every turn. Devin now has access to all of the standard developer tools in a sandboxed computing environment, such as the shell, code editor, and browser—everything a person could possibly need.
At last, Devin may now actively participate in the user’s experience. Devin provides real-time updates on its development, welcomes criticism, and collaborates with you on design decisions as needed.
This is an example of Devin’s abilities:
Devin is capable of picking up new technology skills. Devin uses ControlNet on Modal to generate pictures for Sara that contain hidden messages after reading a blog post.
Devin can create and launch apps from start to finish. Devin creates a dynamic website that mimics the Game of Life! It delivers the application to Netlify after gradually adding the functionality that the user has requested.
Devin is able to locate and address flaws in codebases on his own. Andrew’s open source competitive programming book is maintained and debugged with Devin’s assistance. Devin has its own AI models that it can train and optimize.
Devin is provided with a link to a research repository on GitHub, and he sets up fine tuning for a huge language model. Devin is able to respond to feature requests and defects in public source repositories. Devin completes all necessary setup and context collection with simply a link to a GitHub issue.
Devin’s Function
Devin was assessed using SWE-bench, a demanding benchmark that requires agents to fix actual GitHub issues from open source projects such as scikit- learn and Django. Devin significantly outperforms the previous state-of-the-art, which was 1.96%, by correctly resolving 13.86%* of the problems end-to-end. The best previous models are only able to fix 4.80% of problems, even when provided the same files to alter.
