> For the complete documentation index, see [llms.txt](https://docs.posherva.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.posherva.com/features/send-bulk-offers/send-offers-for-recent-like-from-the-news-feed.md).

# Send offers for recent like from the News Feed

If you don't want to send offers for the entire closet, and only for items that were like in recent days or hours. Follow the below instruction.

{% hint style="info" %}
PosherVA fetches news feed details and items in the background, you don't need to open the Poshmark page.
{% endhint %}

1\) Open the PosherVA popup window, by click the icon on the Chrome toolbar. And navigate to the **ACTIONS** tab.

![](/files/-M_jsCFfsMXCVw5VGJBU)

2\) Select offers options and choose **Subset of items: From newsfeed**

3\) Choose the preferred date and time range. And PosherVa sends offers for items that were liked at this time range.

![](/files/-M_jtFhHGmwFgA2o0_QY)

4\) Click **Start.**

To review items like time range, open News Feed -> Liles  <https://poshmark.com/news/like>

![](/files/-M_juJ1mPa_1dzuBH11c)

{% hint style="info" %}
❓If you have any other questions, [contact us](mailto:posherva@gmail.com?subject=Question) by email <help@posherva.com>
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.posherva.com/features/send-bulk-offers/send-offers-for-recent-like-from-the-news-feed.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
