Hotel Review Filtering with Jev
这是 Santiago 公开的项目资料。本站按原始来源展示项目信息，用中文说明适用场景和阅读边界；项目名、源帖与代码保持原样，便于逐项核对。
这条案例记录了什么
Agent 评估
为已记录的输出或轨迹提供分类、分数或复核信号。
社区公开项目或作者演示
原始来源：X post。作者自述，本站未独立复现。
Santiago
记录日期：2026-09-28。日期与身份应以原始资料为准。
原始演示视频
视频来自此案例记录的原始媒体；播放内容和作者声明不等于本站复现。
怎样核对这个项目
- 先打开原始来源，确认作者、日期与 Jev 在项目中的具体用途。
- 如果提供仓库，再检查代码、运行要求和许可证；仓库存在不代表本站已经运行成功。
- 对速度、成本、准确率和规模数字，查看原文的任务、环境和计算口径。
- 是否有可观察的正确答案。
- 评估输入是否完整。
- 分数与人工复核的一致性。
原始文字与技术细节
以下内容保留原语言，供核对事实。中文页的场景说明是阅读提示，不是逐句翻译或实测结论。
展开英文项目摘要与原帖
项目摘要
Santiago demonstrates filtering hotel reviews with Jev. Apify collects the reviews, TypeSafe Jev classifies Wi-Fi and noise mentions, and GPT-5 mini reviews uncertain cases. Python calculates the ranking; GPT explains the result. The public repository documents the pipeline, but we have not independently run it.
来源原文
Jev is incredibly good! If you haven't heard, Jev is a new "System One" model optimized for decision-making. For example, you can give it a set of possible choices, and Jev will classify the input text based on those choices, including a confidence score for each. Best of all: Jev is really fast and cheap, so you can use it in all sorts of applications. The first thing I built with it is a classifier to pick the hotels I want to visit. I use @apify to collect thousands of reviews, then classify them with Jev based on specific criteria I care about. You just couldn't do this reliably, but now you can. Link to the repository: https://github.com/svpino/hotel-reviews…
原记录的限制
- Repository and integration inspected; no independent end-to-end execution or accuracy benchmark.
- No mention of Wi-Fi or noise is not positive evidence. Sparse samples and unresolved classifications affect the result.
- The score is a ranking rule, not the probability that a hotel is good. Price and location are outside this demo.
- Apify and OpenRouter usage are billed separately. The saved-review mode still makes model calls.