Wonders AI

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Q: Wonders vs. Open AI Deep Research and Google Deep Research

I am an entrepreneur seeking to find niche opportunities. Up to this point, I have been using Open AI's Deep Research, Google's Deep Research, and the Genspark.ai tool. How would your took complement or substitute for one or more of these tools?

Along these lines, will your tool help me find potential customers with contact information?

Lastly, I highly value being able to learn new information quickly, even topics I don't have a strong background in, such as the mathematics behind AI. How will Wonders help me accelerate my learning?

Thanks in advance for your responses.

steve9620PLUSMay 20, 2025
Founder Team
Joe_Wonders

Joe_Wonders

May 21, 2025

A: Hi Steve, great questions. Core difference is that Wonders is not a chatbot, but an AI search workspace.

1) The first key difference is that we break the research process into step-by-step workflow with transparent and precise control at each step.

Instead of the agent going off and pulling out stuff for you and then making a response that may not 100% map on your needs, Wonders offers guidance at each step, but leaves the decision-making up to you.

2) Secondly, we draw 100% on published academic papers, technical reports, and patents. This is better for highly credible information; but you won't find information that depend on recency (like how many electric cars were sold in China last quarter).

3) We won't be able to find any customers with contact information.

4) Wonders is great for learning. You can configure your project board with an overarching topic of interest, get AI guidance on the best questions to ask, and get credible peer-reviewed answers to each question... together with features like AI explainer, highlighters, and chat with results each board is a fast track to understanding new topics.

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Posted: May 21, 2025

Thank you, Joe.

Please answer a few follow-up questions:

1) Can I import or paste research from other sources that is based on current information such as books I am reading on Kindle (I have a very large library and have it synched to Readwise so that my highlights flow to Evernote), the New York Times, the Wall Street Journal, and even research I have my Genspark agent obtain?

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Posted: May 22, 2025

Hi Steve, let me address these questions for you.

1) We don't really have any import options at this moment. We will be adding PDF import later in the year, but no plans for synchronization and integrations just yet.

Posted: May 21, 2025

It appears there is a character limit for responses...

2) Regarding learning, I will provide a real-life scenario I am encountering. I am reading the book "How Machines Learn", which gets into multiple mathematical formulas and concepts. While I have had these subjects in the past, they were never intuitive to me. I am looking for a way to accelerate my ability to deeply understand these concepts

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Posted: May 22, 2025

2) Well, Wonders is designed for desk-research and in-depth learning. So it may be suitable for this as it helps explore topics from variety of angles and grounds its responses in found literature. But we do not have explicit tutoring features, and our chat is somewhat limited to retrieving information about specific questions.

I'd say try using it and see if it works for you.

Posted: May 21, 2025

(Cont) as efficiently and effectively as possible.

Currently, I am asking ad hoc questions to ChatGPT and Google Gemini, and taking the responses that make the most sense to me. Even if I cannot do this level of prompting in Wonders, I would like to be able to use Wonders to organize my learning, which would require me to be able to pull in these other inputs.

Please let me know your thoughts.

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Posted: May 22, 2025

2 - Cont.) We're definitely looking into ways of organizing found knowledge. The whole idea of a Wonders board is to go through sort of a learning journey from question and retrieval to analysis and synthesis. It's just that the journey is really focused on postgraduate use-cases where the learning is done for specific research output rather than learning itself. So, I'm not sure how well it fits.