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Corinne Riley
Corinne Riley: 我认为AI在代码生成和工程流程中具有巨大的潜力，能够增强甚至完全自动化工程工作，从而开启比以往更大的市场。目前我们看到了三种主要的AI编码工具方法：AI 协同工具、AI 代理和代码特定模型。虽然每种方法都有其优势，但同时也面临着技术挑战，例如如何提取相关上下文、如何使 AI 代理更好地完成端到端编码任务，以及是否拥有代码特定模型能够带来长期差异化。我相信，解决这些挑战将解锁具有低延迟和良好用户体验的可靠代码生成工具，为开发者带来更高的生产力和创新。
Deep Dive
This chapter explores the current landscape of AI-powered code generation tools, focusing on three approaches: AI co-pilots enhancing existing workflows, AI agents replacing workflows entirely, and code-specific models. It highlights the challenges and opportunities within each approach, particularly the need for relevant context within a company's codebase.
- AI augmentation of engineering tasks is feasible due to the nature of coding work, availability of training data, and testability of results.
- Three approaches in the startup ecosystem: AI co-pilots, AI agents, and code-specific models.
- Challenges include accessing relevant context, end-to-end task completion for AI agents, and the long-term viability of code-specific models compared to base model improvements.
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