LinkedIn 文章 API
按 URL 读取公开的 LinkedIn 文章或新闻通讯期刊，包括完整正文、作者、发布时间和互动数据。
US$5.65/千次请求，按请求付费，无需订阅。
Why most forecasting models fail in the second quarter
Daniela Ruiz
Published August 22, 2025
Suppose your forecast is built on last January's demand curve. By April, the assumptions underneath it have already moved twice.
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{"url": "https://www.linkedin.com/pulse/why-most-forecasting-models-fail-quarter-two-daniela-ruiz","title": "Why most forecasting models fail in the second quarter","body": "Suppose your forecast is built on last January's demand curve. By April, the assumptions underneath it have already moved twice.\n\nMost finance teams do not notice until the variance report lands, and by then the quarter is half over. The fix is not a better model. It is a shorter feedback loop between the actuals and the assumptions that produced the forecast.\n\nOver the last year we worked with a dozen mid-market finance teams rebuilding this loop. Three changes showed up in every team that closed its variance gap: weekly actuals instead of monthly, a single owner per assumption, and a standing fifteen minute review that only asks what changed.\n\nNone of this requires new tooling. It requires treating the forecast as a living document instead of a quarterly artifact.","description": "Three changes that closed the variance gap for a dozen finance teams, and none of them required new software.","author": "Daniela Ruiz","authorUrl": "https://www.linkedin.com/in/daniela-ruiz-fpa","authorFollowers": 18400,"createdUtc": 1755878400,... 还有 5 个字段}
价格
LinkedIn 文章 API 价格
试一试
一个请求获取 LinkedIn 文章
这个示例展示了 16 个字段中的 15 个。查看全部字段
{
"found": true,
"data": {
"url": "https://www.linkedin.com/pulse/why-most-forecasting-models-fail-quarter-two-daniela-ruiz",
"title": "Why most forecasting models fail in the second quarter",
"body": "Suppose your forecast is built on last January's demand curve. By April, the assumptions underneath it have already moved twice.\n\nMost finance teams do not notice until the variance report lands, and by then the quarter is half over. The fix is not a better model. It is a shorter feedback loop between the actuals and the assumptions that produced the forecast.\n\nOver the last year we worked with a dozen mid-market finance teams rebuilding this loop. Three changes showed up in every team that closed its variance gap: weekly actuals instead of monthly, a single owner per assumption, and a standing fifteen minute review that only asks what changed.\n\nNone of this requires new tooling. It requires treating the forecast as a living document instead of a quarterly artifact.",
"description": "Three changes that closed the variance gap for a dozen finance teams, and none of them required new software.",
"author": "Daniela Ruiz",
"authorUrl": "https://www.linkedin.com/in/daniela-ruiz-fpa",
"authorFollowers": 18400,
"createdUtc": 1755878400,
"updatedUtc": 1755964800,
"reactions": 612,
"comments": 47,
"image": "https://media.licdn.com/dms/image/D4E12AQF-forecast-cover/article-cover-image.jpg",
"type": "article"
}
}完整的参数与响应参考：这个接口的每个字段、类型和示例。
参考
一个请求，一种响应结构。
你发送的每个字段和收到的每个字段，都附有真实示例。
最近验证于 2026-11-14 · 可用率和延迟按 30d 统计
请求体
JSON，POST 到这个接口。
- urlstring
https://www.linkedin.com/pulse/why-most-forecasting-models-fail-quarter-two-daniela-ruiz
公开的 LinkedIn 文章或新闻通讯期刊 URL，例如 https://www.linkedin.com/pulse/your-article-slug。配合 linkedin.search_posts_full 返回的 attachmentUrl，可以读取帖子背后的文章。
响应
JSON，每个字段一个示例值。
- authorstring
Daniela Ruiz
Display name of the person or organization that published the article. Populated whenever the provider has data for the entity.
- authorFollowersinteger
18400
Follower count of the author at capture time.
- authorUrlstring
https://www.linkedin.com/in/daniela-ruiz-fpa
Canonical LinkedIn profile or company URL of the author.
- bodystring
Suppose your forecast is built on last January's demand curve. By April, the assumptions underneath it have already moved twice. Most finance teams do not notice until the variance report lands, and by then the quarter is half over. The fix is not a better model. It is a shorter feedback loop between the actuals and the assumptions that produced the forecast. Over the last year we worked with a dozen mid-market finance teams rebuilding this loop. Three changes showed up in every team that closed its variance gap: weekly actuals instead of monthly, a single owner per assumption, and a standing fifteen minute review that only asks what changed. None of this requires new tooling. It requires treating the forecast as a living document instead of a quarterly artifact.
Full body text of the article as plain text. Populated whenever the provider has data for the entity.
- commentsinteger
47
Total comments on the article.
- createdUtcnumber
1755878400
UTC epoch timestamp in seconds (Unix time). Multiply by 1000 for a JS Date in milliseconds. Populated whenever the provider has data for the entity.
- descriptionstring
Three changes that closed the variance gap for a dozen finance teams, and none of them required new software.
Short summary of the article, as LinkedIn presents it in previews.
- imagestring
https://media.licdn.com/dms/image/D4E12AQF-forecast-cover/article-cover-image.jpg
Cover image URL of the article.
- reactionsinteger
612
Total reactions on the article.
- titlestring
Why most forecasting models fail in the second quarter
Title of the article. Populated whenever the provider has data for the entity.
- typestring
article
Kind of LinkedIn publication, e.g. article or issue (a newsletter edition).
- updatedUtcnumber
1755964800
UTC epoch timestamp in seconds (Unix time). Multiply by 1000 for a JS Date in milliseconds.
- urlstring
https://www.linkedin.com/pulse/why-most-forecasting-models-fail-quarter-two-daniela-ruiz
Canonical URL of the article. Populated whenever the provider has data for the entity.
- foundboolean
true
False when the URL does not resolve to a readable LinkedIn article.
- reason"not_found"
Present only when `found` is false, and says why there is no result. `not_found`: the source states the target does not exist, or returned nothing for it. A `found: false` answer is a successful call, not an error, and `costUsd` is what it actually cost.
常见问题
关于 LinkedIn 文章 API
AnyAPI 的 LinkedIn 文章 API 只需一次 POST 请求 /v1/run/linkedin.article，就以统一格式的 JSON 返回 LinkedIn 数据。按 URL 读取公开的 LinkedIn 文章或新闻通讯期刊，包括完整正文、作者、发布时间和互动数据。无论哪个数据源提供服务，AnyAPI 都返回同一套统一的 schema。费用：每千次请求 US$5.65 起，以美元计价，无需订阅，也没有每月最低消费。过去 30 天，通过 AnyAPI 发起的 LinkedIn 文章 API 调用中有 93.0% 成功，响应时间中位数为 4.5 秒，共测量 215 次调用。