From the latest announcement of OpenAI, ChatGPT agent seems like it can perform a really wide range of web tasks such as booking flights or making spreadsheets. And it specially relates the Agent product with the Connector ability, which allows you to connect apps like Gmail and Github so ChatGPT can find information relevant to your prompts and use them in its responses. This innovation are really promising.

Very interesting demo of how Agent in the OpenAI Operator agent is acting in the Virtual Machine Environment and browser. It's very promising to release the burden of navigate through multiple webpages to finish your goal.

I am very excited to watched the whole live of OpenAI Operator Agent release. And not surprisingly, Operator is a GUI agent or specifically a Web Browser using AI Agent as a research preview version. Some Key Takeaways from the review: OpenAI Operator can perform "wide variety of repetitive browser tasks such as filling out forms, ordering groceries, and even creating memes". Model: Computer-Using Agent (CUA), CUA is trained to interact with graphical user interfaces (GUIs)—the buttons, menus, and text fields people see on a screen. CUA is still in early stages and has limitations, it sets new state-of-the-art benchmark results in WebArena and WebVoyager, two key browser use benchmarks. https://openai.com/index/introducing-operator/ Research Preview: https://openai.com/index/computer-using-agent/

I just saw the news about the OpenAI AI agent news and prompted ChatGPT about their latest Task feature "Can you tell me more about ChatGPT Task and what are the features of Task. Is it a special kind of AI Agent?". And I got the response: "ChatGPT's Task is like a smart, context-aware assistant capable of performing specific actions that enhance its conversational abilities. It’s more than just responding to queries—it's about providing real-time, functional solutions that meet user needs across various domains. ChatGPT's Task refers to an extended functionality where specific tasks or actions can be performed through integration with external tools or specialized capabilities. It's a way to extend the basic conversational abilities of ChatGPT by allowing it to perform specific, predefined operations." It also mentions Task-Driven Actions, Autonomous Functionality, Complexity and Customization, and also gives some examples of Tasks, such as Scheduling and Reminders,Legal Assistance, Personalized Shopping, etc. Overall, I am not that enthusiastic about OpenAI's agent "rumors" yet, because there are already many players in this field focusing on automating things, such as Microsoft and Google. And even anthropic released their autonomous agents plan of computer and mobile use, which is extremely focused and ambitious.

ChatGPT answered the questions about the difference between OpenAI Operator AI Agent and Microsoft AI Agents. And here are the results. Let's see weather OpenAI can break the boundaries of their own ecosystems and increase the productivity of broader scope. In summary, OpenAI’s agents excel in natural language generation and user-focused tasks, while Microsoft’s agents are tightly integrated into their productivity and cloud services, catering to enterprise needs. The choice between them would depend on the specific use case and the ecosystem you're already invested in.

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.