Summary of Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

• 00:00:01

  This is a demo showcasing the long context understanding feature in Gemini 1.5 Pro. Using three.js example code, the model was able to find relevant examples for learning about character animation. It could also locate and modify specific code components, such as adding a slider to control animation speed. The model successfully responded to multimodal input and made changes to scenes based on user queries. While not perfect, the model provided tailored solutions and explanations.

• 00:02:34

  Gemini 1.5 Pro's long context understanding feature allows for precise code modification. The model identifies the correct demo and explains how to tweak specific lines to achieve desired effects, such as making text shiny. It can handle a context window of up to 1 million tokens. The model successfully extracts and modifies code for various examples, such as character animation and adding sliders. It can even analyze screenshots and find matching code. Additionally, it provides instructions on modifying code for specific changes, like flattening terrain.

• 00:07:14

  The function called "generate height" was discussed, with a clear explanation on how to make changes to it to achieve a flatter terrain. Another code modification task was demonstrated using a 3D text demo, where the model accurately identified the lines that needed tweaking to change the text to "goldfish" and make the materials metallic and shiny. While the model's responses aren't perfect, it was able to successfully accomplish the task. These examples showcase the capabilities of Gemini 1.5 Pro, which has a context window of up to 1 million multimodal tokens. The model also identified three examples related to character animation and answered questions about animation controls in the "littlest Tokyo" demo, even providing customized code to add a slider for controlling animation speed.

• 00:11:46

  This video showcases the use of Gemini 1.5 Pro in problem solving across a large codebase. It demonstrates the addition of a slider for controlling animation speed, the model's ability to locate code corresponding to a given screenshot, and making code modifications to flatten terrain and change text appearance. While the responses aren't always perfect, Gemini 1.5 Pro proves its capabilities with a context window of up to 1 million multimodal tokens. The video also discusses the extraction and analysis of three JS example code using Google Studio.

• 00:16:12

  The video showcases the process of solving problems related to blending skeletal animations, poses, and morph targets for facial animations. The model takes around 60 seconds to respond to each prompt, but latency times may vary due to the experimental nature of the feature. The model also explains how animations are embedded within the GLT model in the littlest Tokyo demo. The video demonstrates customizing the code to add a slider for controlling animation speed. Multi-modal input is tested by providing a screenshot and asking the model to locate the corresponding code. Lastly, the model successfully identifies the function and line of code needed to make the terrain flatter.

• 00:19:15

  The model successfully identified the specific function and line of code to modify in order to achieve the desired result of a flatter terrain. Similarly, it accurately pinpointed the relevant lines of code to tweak in the Tex Geometry demo to change the text to "goldfish" and create a shiny and metallic effect. Although the model's responses are not always perfect, it successfully completed the tasks and produced the desired outcomes. These examples highlight the capabilities of Gemini 1.5 Pro, which can analyze up to 1 million multimodal tokens in its context window.