API para LinkedIn: Artigo
Leia um artigo público do LinkedIn ou uma edição de newsletter pela URL, com o texto completo, autor, horário de publicação e contagens de engajamento.
US$ 5,65/mil req., pague por requisição, sem assinatura.
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,... 5 campos a mais}
Preços
Preços do endpoint Artigo da API para LinkedIn
Teste
Obtenha Artigo do LinkedIn em uma só requisição
Este exemplo mostra 15 de 16 campos. Ver todos os campos
{
"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"
}
}Referência completa de parâmetros e resposta - todos os campos, tipos e exemplos deste endpoint.
Referência
Uma requisição, um formato de resposta.
Cada campo que você envia e cada campo que você recebe, cada um com um exemplo real.
Última verificação em 2026-11-14 · disponibilidade e latência medidas em 30d
Corpo da requisição
JSON, enviado via POST para este endpoint.
- urlstring
https://www.linkedin.com/pulse/why-most-forecasting-models-fail-quarter-two-daniela-ruiz
URL pública de artigo ou edição de newsletter do LinkedIn, por exemplo https://www.linkedin.com/pulse/your-article-slug. Combine com o attachmentUrl retornado por linkedin.search_posts_full para ler o artigo por trás de um post.
Resposta
JSON, um valor de exemplo por campo.
- 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.
Dúvidas frequentes
Sobre o endpoint Artigo da API para LinkedIn
O endpoint Artigo da AnyAPI para LinkedIn retorna dados de LinkedIn em JSON normalizado com uma chamada POST para /v1/run/linkedin.article. Leia um artigo público do LinkedIn ou uma edição de newsletter pela URL, com o texto completo, autor, horário de publicação e contagens de engajamento. A AnyAPI retorna um schema normalizado, seja qual for a fonte que atende. Custa a partir de US$ 5,65 por mil requisições, em dólares, sem assinatura e sem mínimo mensal. Nos últimos 30 dias, 93,0% das chamadas ao endpoint Artigo da API para LinkedIn feitas pela AnyAPI tiveram sucesso, com tempo de resposta mediano de 4,5 segundos, em 215 chamadas medidas.