ChatGPT Search gives a pretty accurate answers to my questions prompt "What are AI Agents and please help list 20 AI Agents examples". It firsts give a comprehensive summary of the definition of "AI Agents" as "AI agents are software systems that autonomously perform tasks or make decisions based on input from their environment or users.". Then it lists 20 AI agents examples, such as Chatbots, Autonomous Vehicles, Virtual Assistants, Fraud Detection Systems, etc. Overall, the results are helpful and relevant to my prompts.

I just got the email invite to use ChatGPT Search. Can't wait to share my experience with the community. I tried the SearchGPT a few months back and it was only a prototype which is very similar to Perplexity AI. And hopefully this time it will be a totally new experience. Will come back to revise the review once I tested prompts.

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.