The RISC-V journey is not easy. In reality, we have to overcome barriers across the time and space in a sequencial manner. For example, when the end user market lacks RISC-V products, we take the risk of making high profile products like laptops: firstly to prove RISC-V is capable; secondly to speed up our RISC-V ecosystem improvement. Moreover, end user product problems do give us focus on what to fix in the most efficient way. For instance, our first generation RISC-V laptop has a lot of software issues, then we focused and proactively worked with software partners like kernel developers, and OS platforms and Applications for the improvement. In 2024 we successfully convinced Canoncial and Redhat, giving RISC-V more support ever than before on Ubuntu and Fedora. Building an AI PC on RISC-V architecture is both a challenge and an opportunity. This year, while developing our latest AI PC, we faced key questions: What AI applications do users truly need? How can we ensure smooth performance on RISC-V laptops? And how can developers effectively leverage local AI compute? To address these challenges, DeepComputing collaborated with leading open-source communities. We partnered with VLC, one of the most popular media players with over 100 million downloads across Linux distributions, optimizing models to run efficiently on local AI compute. We try to integrated local AI APIs with VS Code, the world’s most-used code editor, and Chrome, the dominant web browser, using technologies like TensorFlow.js to bring AI to developers right in their workflow.