Google Gemini 2.0 Flash
Here is the translated text:
Sounds like you’ve been exploring the capabilities of Gemini 2.0 Flash and having some mixed results!
You started by trying to generate a simple game, like a snake game, using the OpenAI Compatible provider with Google’s API key. The model was able to produce a basic version of the game relatively quickly.
Next, you tried to enhance the game by adding a score system and start screen, which seemed to work out well. However, when trying to generate another game, like 2048, you encountered some issues with CSS and JavaScript code not being adjusted correctly.
You also experimented with using Client alongside Gemini 2.0 Flash for your own project, but ran into problems with dependencies and permission verification.
Despite these challenges, you still managed to get some good results from the model, and even mentioned that it might be because of the training data including test data sets, which can lead to overfitting.
Lastly, you’re looking forward to seeing what other models like Gemini 2.0 Pro will bring to the table, especially in terms of writing code and solving complex problems.
Overall, your experience with Gemini 2.0 Flash seems to be a mix of excitement and frustration, but it’s great that you’re exploring its capabilities and providing feedback!
Translation
Sounds like you’ve been exploring the capabilities of Gemini 2.0 Flash and having some mixed results!
You started by trying to generate a simple game, like a snake game, using the OpenAI Compatible provider with Google’s API key. The model was able to produce a basic version of the game relatively quickly.
Next, you tried to enhance the game by adding a score system and start screen, which seemed to work out well. However, when trying to generate another game, like 2048, you encountered some issues with CSS and JavaScript code not being adjusted correctly.
You also experimented with using Client alongside Gemini 2.0 Flash for your own project, but ran into problems with dependencies and permission verification.
Despite these challenges, you still managed to get some good results from the model, and even mentioned that it might be because of the training data including test data sets, which can lead to overfitting.
Lastly, you’re looking forward to seeing what other models like Gemini 2.0 Pro will bring to the table, especially in terms of writing code and solving complex problems.
Overall, your experience with Gemini 2.0 Flash seems to be a mix of excitement and frustration, but it’s great that you’re exploring its capabilities and providing feedback!
Reference:
https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/## Google Gemini 2.0 Flash
Here is the translation of the contents from the
Key Points:
- Setting Provider: The article first set up the provider as OpenAI Compatible and posted Google’s URL along with the generated API Key.
- Generating Snake Game: Gemini 2.0 Flash was used to generate a snake game, resulting in a faster generation of a rough prototype compared to the Flash model.
- Enhancing Interface: The article mentions adjusting and beautifying the interface of the generated web game.
- Adding Score Calculation and Start Screen: Gemini 2.0 Flash can also add score calculation and start screen functionality.
- Generating 2048 Game: The article mentions using Gemini 2.0 Flash to generate another 2048 game, but encountered CSS issues.
- Reviewing and Adjusting: The article reviews and adjusts this issue, but feels that more time is needed for further adjustment.
- Using Client with Gemini 2.0 Flash: The article mentions using a client in conjunction with Gemini 2.0 Flash to modify an ongoing project, but also encountered programming issues.
- Review and Conclusion: Finally, the article reviews the performance of Gemini 2.0 Flash and concludes that it still needs further improvement on the programming side.
In Summary, this is an article about testing and introducing Gemini 2.0 Flash, which also mentions programming issues and future development directions.
Translation
這是一則關於Gemini 2.0 Flash的測試與介紹文章。
以下是本文的重點：
- 設定Provider: 這篇文章首先設定了Provider為OpenAI Compatible，並將Google提供的網址以及生成的API Key貼上。
- 生成貪食蛇遊戲: Gemmini 2.0 Flash用來生成一個貪食蛇的小遊戲，結果比Flash模型還快生成出了一些雛形。
- 美化界面: 文中提到對於生成的網頁遊戲效果進行了調整和美化。
- 添加分數結算與開始畫面:_gemmini 2.0 Flash還可以添加分數結算以及開始畫面等功能。
- 生成2048小遊戲: 文中提到使用Gemini 2.0 Flash再次生成一個2048的小遊戲，但遇到了CSS問題。
- 檢視與調整: 這篇文章也對於這個問題進行了檢視與調整，但最後覺得還是需要更多的時間來調整。
- Client搭配Gemini 2.0 Flash: 文中提到使用Client搭配Gemini 2.0 Flash修改目前正在製作的一個專案，但也遇到了程式方面的問題。
- 評論與結論: 最後，這篇文章對於Gemini 2.0 Flash的表現進行了評論，並認為它在程式方面還需要進一步改善。
總之，這是一則關於Gemmini 2.0 Flash的測試與介紹文章，文中也提到了程式方面的問題和未來的發展方向。
Reference:
https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/