Devin vs 秒哒：怎么选？
下面把两款工具的关键信息逐项放在一起对照。 两者同属「AI 聊天」分类，属于直接竞品。
📊 参数逐项对照
| 对比项 | Devin | 秒哒 |
|---|---|---|
| 价格模式 | 免费 | 免费 |
| 来源地区 | 🌍 国外 | 🇨🇳 国内 |
| 所属分类 | AI 聊天 | AI 聊天 |
| 用户评分 | 暂无评分 | 暂无评分 |
| 热度（浏览量） | 59 | 193 |
| 付费说明 | — | — |
| 替代品 | Devin 的替代品 → | 秒哒 的替代品 → |
📖 详细介绍
Devin 是什么？
关于 Devin
A task of this magnitude, with the vast number of variations that it had, was a ripe opportunity for fine-tuning. The Nubank team helped to collect examples of previous migrations their engineers had done manually, some of which were fed to Devin for fine-tuning. The rest were used to create abenchmark evaluation set.Against this evaluation set, we observed adoubling of Devin’s task completion scores after fine-tuning, as well as a 4x improvement in task speed. Roughly 40 minutes per sub-task dropped to 10,which made the whole migration start to look much cheaper and less time-consuming, allowing the company to devote more energy to new business and new value creation instead.
One of Nubank’s most critical, company-wide projects for 2023-2024 was a migration of their core ETL — an 8 year old, multi-million lines of code monolith — to sub-modules. To handle such a large refactor, their only option was a multi-year effort that distributed repetitive refactoring work across over one thousand of their engineers. With Devin, however, this changed: engineers were able to delegate Devin to handle their migrations and achieve a 12x efficiency improvement in terms of engineering hours saved, and over 20x cost savings. Among others, Data, Collections, and Risk business units verified and completed their migrations in weeks instead of months or years.
Nubank’s code migration was filled with the monotonous, repetitive work that engineers dread. Moving each data class implementation from one architecture to another while tracing imports correctly, performing multiple delicate refactoring steps, and accounting for any number of edge cases was highly tedious, even to do just once or twice. At Nubank’s scale, however, the total migration scope involved more than 1,000 engineers moving ~100,000 data class implementations over an expected timeline of 18 months.
秒哒 是什么？
关于 秒哒
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