A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount"
}
],
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872/replies",
"type": "Collection",
"first": {
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872/replies?page=true",
"type": "CollectionPage",
"next": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872/replies?only_other_accounts=true&page=true",
"partOf": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872/replies",
"items": [
{
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711",
"type": "Note",
"summary": null,
"inReplyTo": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872",
"published": "2023-01-05T18:45:38Z",
"url": "https://sigmoid.social/@mervenoyan/109638080161363711",
"attributedTo": "https://sigmoid.social/users/mervenoyan",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://sigmoid.social/users/mervenoyan/followers",
"https://sigmoid.social/users/knutjaegersberg",
"https://sigmoid.social/users/minimaxir"
],
"sensitive": false,
"atomUri": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711",
"inReplyToAtomUri": "https://sigmoid.social/users/mervenoyan/statuses/109638068927155872",
"conversation": "tag:sigmoid.social,2023-01-04:objectId=4050663:objectType=Conversation",
"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I just checked for both:<br /><a href=\"https://paperswithcode.com/task/extractive-document-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/extrac</span><span class=\"invisible\">tive-document-summarization</span></a><br /><a href=\"https://paperswithcode.com/task/abstractive-text-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/abstra</span><span class=\"invisible\">ctive-text-summarization</span></a><br />it depends on dataset apparently but what I said is still applicable. if I did applied ML I'd use longformer as seq2seq models are hard to get right for generation and tend to speak gibberish</p>",
"contentMap": {
"tr": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I just checked for both:<br /><a href=\"https://paperswithcode.com/task/extractive-document-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/extrac</span><span class=\"invisible\">tive-document-summarization</span></a><br /><a href=\"https://paperswithcode.com/task/abstractive-text-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/abstra</span><span class=\"invisible\">ctive-text-summarization</span></a><br />it depends on dataset apparently but what I said is still applicable. if I did applied ML I'd use longformer as seq2seq models are hard to get right for generation and tend to speak gibberish</p>"
},
"attachment": [],
"tag": [
{
"type": "Mention",
"href": "https://sigmoid.social/users/knutjaegersberg",
"name": "@knutjaegersberg"
},
{
"type": "Mention",
"href": "https://sigmoid.social/users/minimaxir",
"name": "@minimaxir"
}
],
"replies": {
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711/replies?only_other_accounts=true&page=true",
"partOf": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711/replies",
"items": []
}
},
"likes": {
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711/likes",
"type": "Collection",
"totalItems": 1
},
"shares": {
"id": "https://sigmoid.social/users/mervenoyan/statuses/109638080161363711/shares",
"type": "Collection",
"totalItems": 0
}
}
]
}
}