#017 - Minduck Wants to Supercharge Your Creativity

I am 32 years old and have experience working for two startup companies. Previously, I spent eight years in China as an industrial innovation consultant, accelerating the zero-to-one early innovation and incubation of new products and services for businesses in travel, office work, and lifestyle sectors. Now, I am an AI entrepreneur co-founding Minduck AI with two ambitious makers, creating a product that diverges from the typical chat-based AI models.

We created Minduck to tackle a significant challenge in today's AI landscape. While AI technologies can enhance many areas of life, the conversational nature of large language models often complicates direct user engagement with AI. Typically, users must grasp machine thinking, prompt engineering, or be adept in articulating and organizing their thoughts to leverage AI fully.

Minduck addresses these issues by allowing users to quickly receive initial responses, regardless of the quality of their input, without needing technical expertise. Thus, Minduck serves as a Creativity and Productivity AI Agent that understands your thinking patterns. It is designed to simplify the AI experience and make it accessible and effortless for intellectual creators, including writers, researchers, educators, and broad thinkers.

Our platform significantly reduces the learning curve associated with AI technologies, enabling users to generate and refine AI-created content seamlessly based on their unique ideas.

I appreciate this question because it touches on the Creation Value Chain! Before discussing my "aha" moment, let me first outline my past experiences, as they represent the initial phase of the Creation Value Chain.

In my previous role at a visionary and passionate innovation technology consultancy, I helped build a simulation platform for scenario-based innovation. This experience trained me to become:

• A keen observer who seriously dissects and decodes problems within the world,

• A designer capable of integrating methodologies from various interdisciplinary fields to tackle a range of challenges,

• A collaborator who could bring together experts from different fields to solve problems, always aiming to systematize the process whenever possible (similar to the simulation products we developed at the time).

My experiences taught me that there is a persistent model of needs:

• In the real world, we identify problems within systems,

• We then design conceptual models by applying various methodologies and theories to address these issues,

• Finally, we program these models to allow machines to solve the problems.

My "aha" moment occurred when I realized that AI agents could automate and run this professional problem-solving system thanks to their capabilities in content generation, automation control, and specialized services. What truly convinced me to dive into the AI entrepreneurship journey was understanding that the core of AI agents is not just the AI algorithms themselves but how they can streamline and automate workflows. These agents can be highly customized, and most importantly, they are designed to deliver value through AI.

I believe that this technology will be the next step in democratizing high-end technology, making it accessible and affordable for everyone. This insight was not merely about the technology itself but about transforming professional systems to make sophisticated technology available to a broader audience, focusing more on workflows than on algorithms alone.

Personal Learning Scenario: During our earliest user testing phases, we encountered scenarios centered around personal learning. This learning wasn't aimed at students but at individuals facing life's various stages and challenges that require highly personalized solutions. For example, a young couple busy with their careers needed help planning to expand their family to include a baby but didn't know where to start. Another instance involved a young mother crafting an educational plan for her son and a professional looking to shift career paths.

I refer to this scenario as "personal enhancement." I believe it is a profoundly meaningful use case that exceeded our initial expectations. However, it has also led us to consider the product's form and commercial value further for the future. While there hasn't been a request for the quirkiest features, I think this is because we have introduced a novel way of using AI, and most user feedback remains focused on the product experience. Regardless of the specifics of their feedback, we understand and appreciate their insights.

What excites me most is the anticipation of the market's response to these innovative applications of our product.

Text Handling: When I need quick, deliverable, and complete content, I use Minduck. I prefer it because it saves me the hassle of engaging in lengthy dialogues with AI. Minduck offers me options for creative content types, and its outputs are more professional and comprehensive compared to conversational AI. However, when I require extensive language translation, I still rely on conversational AI because it can act as both a consultant and a translation expert.

Image Handling: For visual design tasks, I exclusively use Minduck instead of conversational AI. I find that Minduck more accurately translates textual concepts into visual representations.

Design and Functionality Balance: I believe our approach has adopted a 'form follows function' strategy, prioritizing user efficiency and intuitiveness in the design. Any additional emotional design elements are merely seasoning. The primary goal for our AI product team is to ensure practical implementation and usability.

I often use the famous case of the "12 AI agents living in Stanford Village" to explain the concept of Minduck. Imagine Minduck as a village where many little creation AI agents reside, each specializing in different aspects of creative work. These agents handle tasks like needs analysis, content type design, and content creation. Essentially, there's a professional Minduck team operating behind the scenes that you interact with.

Previously, we delved into detailed technical explanations, discussing algorithms and how they function. However, I've realized that most users aren't concerned with these technical details, and investors often find them difficult to grasp. In my view, they need a straightforward way to understand these processes, which I aim to provide through simple and relatable analogies, making the complex workings of AI more accessible and understandable.

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I'm Kevin, a former marketing technology director who loves AI apps. Thanks for reading.