We are building AI-Shifu in public.
In this column, we share transparent updates about how AI-Shifu evolves — what we build, what we improve, what we learn, and how real users shape the product. This is our ongoing Product Update Report.
Why We Share Our Progress Publicly
AI-Shifu is not a static product. It is continuously shaped by real usage, real creators, real learners, and real constraints. Features are introduced, refined, and sometimes reworked based on how people actually create, teach, and learn with AI.
Rather than only presenting polished outcomes, we choose to openly share the process behind the product — the progress, the decisions, the trade-offs, and the lessons learned along the way. We believe transparency builds both trust and deeper understanding.
What This Report Covers
This report is not marketing content or a simple changelog. It is a clear record of how an AI product evolves in practice.
In each update, we typically share:
Product Progress
What we shipped, improved, redesigned, or fixed — and why those changes happened. Many decisions are driven by real user behavior rather than assumptions.
Technical Evolution
How the system evolves behind the scenes — models, streaming stability, performance, architecture, and infrastructure. AI products are deeply tied to technical constraints, and understanding them helps clarify what is possible.
Real User Signals
How creators, educators, and learners actually use AI-Shifu — what creates value, what causes friction, and what users are trying to achieve. These signals often drive the most meaningful product changes.
Experiments and Thinking
Not every idea becomes a permanent feature. We test directions, explore possibilities, and sometimes change course. Building an AI product is an ongoing process of learning under uncertainty, and we choose to share that process openly.
What You Can Gain From Following This Report
This column is written for creators, educators, developers, and builders exploring how AI is transforming content, learning, and digital products.
By following these reports, you can:
- Understand how a real AI product evolves from the inside
- See how AI is used in real production environments
- Learn practical lessons about product iteration and user-driven design
- Observe real trade-offs between product vision, technical limits, and user needs
- Discover new capabilities early and apply them in your own work
- See how creators use AI to build courses, knowledge products, and digital businesses
- Understand how personalized AI learning experiences are designed and refined
This report is essentially a living record of building an AI-driven platform in reality, not theory.
Looking Ahead
We are preparing for the upcoming release of AI-Shifu 2.0, which will introduce a fundamentally new learning experience — more immersive, more personalized, and more aligned with how humans learn with AI.
More details will be shared soon.
We invite everyone who is interested in the future of AI-powered creation and education to continue following our journey, explore with us, and help shape what AI-Shifu becomes.
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