I like to cook, and somewhere along the way that turned into an obsession with high-end chef's knives. So I scrape the Reddit threads where people argue about them and pull out every brand, model and steel they mention, to see what is getting bought and argued about.
Picking product names out of text is a job called named-entity recognition, and small models have done it for a decade. I was doing it with Gemini 3.1 Pro, one paid API call per comment. Overkill, but it worked: from "picked up a Mazaki in white #2, way better than my old Fibrox" it returned Mazaki as a brand, Fibrox as a model and white #2 as a steel, and nothing else. But the scraper pulls every new comment, so the bill grew with how much people posted, and the only way to cap it was to skip comments.
The obvious replacement (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.petervijeh.com/projects/reddit-ner)获取最准确的信息。

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🔗 **原文链接**: [I had Gemini train its own replacement for $9](https://www.petervijeh.com/projects/reddit-ner)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 13票 · 👤 p-s-v

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-09-17 22:01