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Daniel Whitenack: 我认为 OpenAI 发布深度研究产品很有趣，特别是考虑到 Hugging Face 迅速采取行动，在开源代码中复制了类似的功能。这种动态表明，在通用应用层面建立护城河非常困难。虽然在特定领域或垂直领域，或者利用专有数据可能建立护城河，但在通用应用层面则不然。我关注的重点在于，我们是否在构建增强人类能动性的系统，而不是取代它？我们构建的系统是让我们更加信任人类机构，还是更加恐惧和不信任它们？我们构建的系统实际上是将我们更多地推向个人孤立，还是推向社区？ Chris Benson: 我对 OpenAI 的未来不太确定，也不太关心。埃隆·马斯克收购 OpenAI 的努力以及 OpenAI 从非营利组织转变为营利性公司，都让我觉得没有明确的立场可站。开源选项和应用层面功能的出现速度加快，让我对 OpenAI 等公司的未来商业模式感到好奇。我认为，将 AI 集成到企业堆栈中，例如微软的捆绑效应，可能是一种生存方式。但真正的商业价值在于处理敏感数据，这需要垂直领域的 AI 参与者和工具基础设施参与者来解决。

Deep Dive

Chapters
This chapter discusses the potential trajectory of OpenAI, considering Elon Musk's attempted acquisition and the company's evolving business model. The conversation touches upon OpenAI's release of deep research products and the rapid response of open-source alternatives.
  • Elon Musk's bid to acquire OpenAI
  • OpenAI's release of deep research product
  • Rapid response from Hugging Face reproducing OpenAI's functionality with open-source code

Shownotes Transcript

It seems like everyone is uses the term “agent” differently these days. In this episode, Chris and Daniel dig into the details of tool calling and its connection to agents. They help clarify how LLMs can “talk to” and “interact with” other systems like databases, APIs, web apps, etc. Along the way they share related learning resources.

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