Anima today revealed is has added an ability to personalize the code its generative artificial intelligence (AI) platform creates based on how an application is described using tools from Figma and Adobe.
That capability makes it possible for the code being generated to resemble the coding style of an organization or specific developer more closely.
Anima CEO Avishay Cohen said the company developed this capability to reduce the editing time that is otherwise required when trying to embed code generated by a large language model (LLM) within an application that has a well-defined coding methodology.
The platform that Anima provides is based on a mix of LLMs and heuristics that the company developed to generate code. In this case, the code being generated is written in React or HTML to make it simpler to build an application’s front end. In contrast, a general-purpose LLM such as ChatGPT is not optimized for creating these types of graphical applications.
Developers no longer need to worry about, for example, making sure each pixel is perfectly placed because the Anima platform automates that part of the application development process, including adding responsive behavior, animations and logic, noted Cohen. In addition, customized instructions may be applied to an entire project or even a specific component.
In effect, the platform enables backend developers to automatically create a front-end for their application from a description in tools provided by Figma or Adobe. As a result, developers can allocate more time to writing business logic and optimizing application performance, said Cohen.
Later this year, Anima will integrate its platform with GitHub and other continuous integration/continuous delivery (CI/CD) platforms as part of an effort to extend the reach of the platform across an entire codebase, he added.
There is, of course, already no shortage of generative AI tools for writing code, but the degree to which they have been incorporated into DevOps workflows varies from one organization to the next. Much of the code created using platforms such as ChatGPT can vary widely in terms of quality. These platforms were trained using code collected from across the web so it’s not uncommon for the code generated to include known vulnerabilities that DevOps teams need to scan before incorporating into a build. In other cases, the code generated might simply break when installed.
Regardless of these issues, however, the amount of code being generated by machines is only going to increase, especially as more LLMs that are specifically trained to create code based on examples that have been closely vetted become more widely available.
In the meantime, developers who never especially enjoyed building the visual front ends of applications can now focus more of their time on tasks they prefer. Alternatively, developers who specialize in the front ends of applications will find that much of the toil involved will soon be automated in a way that should provide more time for them to create customized experiences.
One way or another, the overall pace at which applications are built is about to exponentially increase. The challenge now will be finding ways to take advantage of AI to enable DevOps teams to keep pace.
