IA WRITER GITHUB IO SOFTWARE
GitHub's survey suggests this may be worth reconsidering: "With the increase of AI tooling being used in software development – which often contributes to code volume – engineering leaders will need to ask whether measuring code volume is still the best way to measure productivity and output." And in a further example of self-interest, the same portion of devs who say they're judged on lines of code written believe that metric should continue, even under the presumed productivity enhancement of an AI helper. Speedier and shoddier seems to be the order of the day. Asked how they are currently judged, these programmers responded: code quality (40 percent) time to complete a task (34 percent) number of production incidents (34 percent) lines of code written (33 percent) and number of bugs or issues resolved (33 percent).Īssuming AI coding tools are used, these devs would prefer to be judged on the basis of: code quality (36 percent) time to complete a task (36 percent) number of production incidents (33 percent) lines of code written (33 percent) and number of pull requests (32 percent). Microsoft would rather spend money on AI than give workers a raiseįittingly, those responding to GitHub's survey questions appear to believe that less weight should be given to code quality as a performance metric.Apple becomes the latest company to ban ChatGPT for internal use.Microsoft can't stop injecting Copilot AI into every corner of its app empire.GitHub, Microsoft, OpenAI fail to wriggle out of Copilot copyright lawsuit.A third study found Copilot produced security bugs about 40 percent of the time. One, for example, found AI helpers like ChatGPT produce code that is " well below minimal security standards applicable in most contexts." Another found that Copilot produced more security vulnerabilities than code created without AI help, while developers mistakenly thought the machine-learning tools produced fewer mistakes. Respondents may not be aware of academic studies to the contrary. By placing reference points strategically, you can control how the DragGAN AI model transforms the image."Developers say AI coding tools can help them meet existing performance standards with improved code quality, faster outputs, and fewer production-level incidents," the survey says. These points act as markers for indicating the desired modifications. To guide the DragGAN AI tool in making specific changes to the image, you can add reference points. Experimenting with different seed values can yield diverse and interesting outcomes.
Adjust this value to generate an initial image that serves as the basis for the subsequent transformations. The “ Seed” value determines the starting point for image generation. Each model offers unique behaviors and characteristics that will influence the transformation process of the DragGAN AI tool. Start by selecting a pre-trained model from the available options in the settings section. The left section contains the settings, while the right section displays the image. Upon accessing the DragGAN AI demo page, you’ll notice that the interface is divided into two sections. Enjoy experimenting with DragGAN AI and creating unique stylized images! This interface enables you to specify the desired style using a reference image and generate stylized images based on the DragGAN model.īy following these steps, you can run DragGAN AI in Google Colab and explore its capabilities to transform images using different visual styles.
Click on the link to access the DragGAN AI interface. After executing the previous step, you will see a link generated in the output.
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