I used Doubao to learn K12 classes with my son. And some of the responses are interesting but most of the answers are not accurate enough.

I just tried to use Coze AI Agent Builder (a similar product to OpenAI GPT store) to build a free AI Agent Chatbot using the default template and publish online. First of all, the process is quite simple and you can use UI interface to easily publish a default Chatbot without any modification and using the default LLM choices. You can also publish your GPT or LLM apps using various new LLM. And this is the address of the Chatbot I just published. Check this out: https://www.coze.cn/store/agent/7451157046348775476?bid=6epujr7no4015&bot_id=true

This AI generated picture of female movie and TV opera star Dilraba Dilmurat is great. The humanoid is realistic and look just the same as Dilraba. And the color of the eyes are correct.

Well, I have to say the picture generated by Doubao doesn't make me feel very comfortable. The reason is the earth is too large compared to the moon base and the perspective and relative size is not correct. Additionally, I think the pits on the earth look terrible to me. It's earth in the background as in the prompt, not the mars. Really Bad.

Overall, the responses from AI boyfriend of Doubao Chatbot are natural and engaging. But some responses are not related to the context of the dialogue. The last question is "I am breaking up with you". The bot respond with some general replies and seems unrelated to the context of the dialogue, which is "the boyfriends doesn't know much about taste of movies.".

The answers are very detailed with trustworthy data sources.

The face of the generated Lady gaga in the rain looks really strange and quite evil

Wrong number of arms of professor fox.

Again, Three Armed Peggy Pig Eating Bacon.

I asked Doubao to generate surreal style picture of a professor fox teaching lessons to animals. 3 arms professor, so not good enough.

Well the picture from Doubao is not satisfying. The prompt is to generate a "Melting ice cream, galaxy background, Multispectral Imaging, aliasing, glow, shaders, vivid, photorealist, hyper realist". But it seems like the ice cream and the galaxy background are from two different layers and not aligned with each other.

The promote in this AI girlfriend agents is "Cool girl not very easy to get along with" I tried to tease the bot and ask what to do on the weekends and I want to break up with her. The replies I got are somehow are consistent with the promote. But the qualities of replies I not very high. Especially is too short and I don't know what to ask next.

good reply from AI girlfriend

I asked Double who is the second NBA scorer in hisyory. The answer is correct and Jabbar and it also provides some details of sources.

Claude Sonnet 4.5 released by Sep 2025 achieve higher score on agentic coding and agentic tool use benchmark which is great improvement over previous Claude Sonnet 4.1 model and GPT 5. The performance is quite impressive.

Price is $3 per million input tokens $15 per million output tokens. Still a little bit expensive in performing complex tasks.

Claude Opus 4 claims that Claude Sonnet 4 achieves strong performance across SWE-bench for coding, TAU-bench for agentic tool use, and more across traditional and agentic benchmarks. It's astonishing what's the performance compared to OpenAI O4 and other models?

Claude 4 is the most exciting model reason I am expecting in 2025 since OpenAI stop release new capable models. Its coding and AI agents capability is the most desirable features of future workflows and AI automation. Hopefully the API price will not increase too much.

Claude gives the correct answer to the math problem of finding the differentiation of a function. The result is very similar to the ones of Gemini for the same question "differentiation of function f(x) = e^x + log(x) + sin(x)?". And it also give the restriction of x>0 because there is a function log(x) in the original function. So it's pretty helpful.

Claude answered my math question of solving a quadratic equation. It uses the quadratic formula and set the coefficient a=1, b= 5, c= 6 to the quadratic formula and gets the exact answer. The proof and the step by step solution is exactly correct and very much helpful!

Claude does an amazing job writing code for the trap water Leetcode. It also provides explanation of the algorithm complexity.

I've tested the AI model implementation ability of multiple AI Code Generators. And for this prompt "Self Attention Layer in Transformer", Claude provides the best overall response. The pytorch implementation of Transformer model's self attention layer is correct. And the detailed explanation is really helpful. For example, it introduce the breakdown of the python code, including the "__init__" method, the forward method, the input tensor, the attention energy function, etc. And I will also attach the detailed text response here: """ Claude Sonnet 3.5 Here's a breakdown of how this code works: The __init__ method initializes the linear layers for the value, key, and query projections, as well as the final linear layer that combines the multi-head outputs. The forward method takes in the values, keys, query, and an optional mask tensor. The input tensors are reshaped to have a separate dimension for the number of heads, so that we can perform multi-head attention in parallel. The value, key, and query tensors are projected using the respective linear layers. The attention energy is computed using the einsum operation, which performs the scaled dot-product attention operation across all heads in parallel. If a mask tensor is provided, the attention energies corresponding to the masked positions are set to a very large negative value, so that they get zeroed out after applying the softmax. The attention probabilities are computed by applying softmax to the attention energies. The values are weighted by the attention probabilities using another einsum operation, and the results are concatenated across the head dimension. Finally, the concatenated tensor is projected using the final linear layer to obtain the output of the self-attention layer. You can use this SelfAttention module as a part of your Transformer architecture by applying it to the input embeddings and optionally using the output as the input to the feed-forward network. """

Claude gives me several reason of hiring personal injury lawyers. But it doesn't give more information on the reasons of not hiring a lawyer, not it's tailed to my specific question "victims of car or truck accident". So I will only gives it an average rating. Not very helpful, and I still need to search for more information after asking Claude this question.