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://writeout.ink/users/ljwrites/statuses/113374248888611191",
"type": "Note",
"summary": null,
"inReplyTo": "https://tldr.nettime.org/users/festal/statuses/113373333803787443",
"published": "2024-10-26T14:42:28Z",
"url": "https://writeout.ink/@ljwrites/113374248888611191",
"attributedTo": "https://writeout.ink/users/ljwrites",
"to": [
"https://writeout.ink/users/ljwrites/followers"
],
"cc": [
"https://www.w3.org/ns/activitystreams#Public",
"https://tldr.nettime.org/users/festal"
],
"sensitive": false,
"atomUri": "https://writeout.ink/users/ljwrites/statuses/113374248888611191",
"inReplyToAtomUri": "https://tldr.nettime.org/users/festal/statuses/113373333803787443",
"conversation": "tag:tldr.nettime.org,2024-10-26:objectId=20677253:objectType=Conversation",
"localOnly": false,
"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://tldr.nettime.org/@festal\" class=\"u-url mention\">@<span>festal</span></a></span> This, and without data of people training and reading AI won't have enough data to continue anyway. Output-wise, in my experience the problem is not that machine-learning models are wrong all the time, it's that they can be right a lot and then be VERY VERY WRONG and there's no telling when that happens, because they don't know what they don't know and can't throw errors like code. They require constant error checking, and the checkers themselves will be lulled by the appearance of competence and polish. This is bad enough in my field of translation, but in the medical field the stakes are so much higher and more immediate.</p>",
"contentMap": {
"en": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://tldr.nettime.org/@festal\" class=\"u-url mention\">@<span>festal</span></a></span> This, and without data of people training and reading AI won't have enough data to continue anyway. Output-wise, in my experience the problem is not that machine-learning models are wrong all the time, it's that they can be right a lot and then be VERY VERY WRONG and there's no telling when that happens, because they don't know what they don't know and can't throw errors like code. They require constant error checking, and the checkers themselves will be lulled by the appearance of competence and polish. This is bad enough in my field of translation, but in the medical field the stakes are so much higher and more immediate.</p>"
},
"attachment": [],
"tag": [
{
"type": "Mention",
"href": "https://tldr.nettime.org/users/festal",
"name": "@festal@tldr.nettime.org"
}
],
"replies": {
"id": "https://writeout.ink/users/ljwrites/statuses/113374248888611191/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://writeout.ink/users/ljwrites/statuses/113374248888611191/replies?only_other_accounts=true&page=true",
"partOf": "https://writeout.ink/users/ljwrites/statuses/113374248888611191/replies",
"items": []
}
}
}