cover of episode How to Design an AI-Native Engineering Organization
People
N
NLW
知名播客主持人和分析师，专注于加密货币和宏观经济分析。
S
Sid Pardeshi
Topics
Sid Pardeshi: 从简单的用例来看，像GitHub Copilot这样的工具，你只需提供一点意图，例如创建一个登录屏幕的函数，它就能返回几百行代码。这对于开发者来说非常有用，因为他们不需要记住特定语言的语法，或者希望它能与特定的品牌指南一起工作。如果涉及到前端，你可以将品牌指南提供给Copilot。这样，开发者在他们的IDE中就能提高20%到30%的效率。对于一个年薪10万、20万、30万甚至40万美元的工程师来说，这种效率的提升在整个企业范围内都是非常有意义的。 Brian Elliott: 在另一方面，你可以考虑代理系统在提供代码之前需要思考和验证多久。在最初的系统中，我们只有Copilot，它会立即响应IDE，为你提供可能有效或能让你更快速的东西。而像Deep Research这样的工具，则能够以代理的方式提高代码的质量。这些更具代理性的解决方案，比如Devin或Factory，可能需要30分钟才能返回一个包含数百或数千行代码的工作块，这些代码考虑到了所有相关的关系。 Brian Elliott: 像我们Blitzy，可能需要12小时或几天，甚至几周的时间，来处理数千万行的代码，并交付巨大的工作量。因此，从少量意图、少量代码到大量的基础设施和代码，最终都能提高生产力，无论是30%还是300%，这都取决于企业所采取的方法。

Deep Dive

Chapters
This chapter explores the current applications of AI in coding, ranging from simple code generation tools like GitHub Copilot to more complex, agentic systems like Blitzy. It highlights the varying levels of speed and complexity involved, from immediate code suggestions to large-scale code generation projects spanning millions of lines of code.
  • AI coding tools range from simple code suggestions to complex, agentic systems.
  • Enterprise adoption is impacted by factors like model trust and integration challenges.
  • Different tools cater to various coding tasks, from prototyping to large-scale modernization.

Shownotes Transcript

NLW is joined by Sid Pardeshi and Brian Elliot from Blitzy.com to discuss the radically changes coming to AI engineering organizations. From copilots to agent swarms, this is a conversation about the opportunities and challenges facing all enterprise engineering groups as they look towards the future.

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