Podcasts
This podcast explores the complexities and challenges of Large Language Model (LLM)-driven AI Agents from theoretical research to practical applications. The guest begins by clearly defining and categorizing Agents, including Coding Agents, Search Agents, Tool-Use Agents, and Computer Use Agents, and highlights their core capabilities of perception and action. The conversation compares the advantages and disadvantages of In-Context Learning and End-to-End Training, two mainstream technical approaches, highlighting that even with a powerful foundation model, translating research results into stable, high-quality Agent products remains a significant System Engineering task. The podcast focuses on analyzing the key aspects of Agent training, including large-scale data synthesis (Knowledge Rewrite, MCP Tool Generation, User Simulation) and Reinforcement Learning (RL) paradigms (reward design, task difficulty control, complex instruction following). Agent safety is also discussed, especially the irreversible impacts that may arise when interacting with the physical world, emphasizing the necessity of establishing safety mechanisms and human-machine collaboration. The program analyzes the core contributions of Kimi K2, ChatGPT Agent, Qwen3-Coder, highlighting Kimi K2's innovations in data generation pipelines and RL frameworks, and ChatGPT Agent's progress in browsing and search. Finally, the podcast explores the future potential of AI Agents in achieving self-improvement, becoming new data engines, and forming symbiotic networks with humans, emphasizing the core role of engineering capabilities in driving AI development.
This podcast features Li Yifan, co-founder and CEO of Hesai Technology, recounting his story of 11 years of hardcore technology entrepreneurship in LiDAR. He provides a detailed account of the revolution of LiDAR, from being expensive and rare to a 99.5% cost reduction (from 200,000-300,000 RMB to $200 USD), with the core driven by innovations in chip-based design and automated production. Li Yifan shares the exploration and transformation in the early stages of entrepreneurship, from the failed attempt to detect natural gas leaks with lasers to ultimately focusing on LiDAR, and emphasizes the importance for hardcore technology entrepreneurs to shift from technology-led to market-driven. The conversation explores Hesai's experiences with technology selection (such as 905nm lasers, optical coding, waveform analysis engine), teamwork, financing strategies, and responses to China-US trade friction. He proposes a non-traditional decision-making process of equal share distribution among founders and emphasizes that commercialization is the most authentic feedback of product value. The podcast also looks forward to the future of LiDAR in autonomous driving and robotics, and discusses how companies in China can achieve a win-win strategy in global competition through technological advantages, brand building, and cultural influence output.