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://econtwitter.net/users/brhkim/statuses/109626073684693662",
"type": "Note",
"summary": null,
"inReplyTo": "https://econtwitter.net/users/paulgp/statuses/109625995765955958",
"published": "2023-01-03T15:52:14Z",
"url": "https://econtwitter.net/@brhkim/109626073684693662",
"attributedTo": "https://econtwitter.net/users/brhkim",
"to": [
"https://econtwitter.net/users/brhkim/followers"
],
"cc": [
"https://www.w3.org/ns/activitystreams#Public",
"https://econtwitter.net/users/paulgp"
],
"sensitive": false,
"atomUri": "https://econtwitter.net/users/brhkim/statuses/109626073684693662",
"inReplyToAtomUri": "https://econtwitter.net/users/paulgp/statuses/109625995765955958",
"conversation": "tag:econtwitter.net,2023-01-03:objectId=4563932:objectType=Conversation",
"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://econtwitter.net/@paulgp\" class=\"u-url mention\">@<span>paulgp</span></a></span> Mmm that's a good call, though exactly how much human editing is required to avoid which methods and thresholds of detection is the key Q (juice, squeeze, etc.). </p><p>We're also talking an endlessly increasing variety of LLMs, which can be arbitrarily mixed together, and a growing market for LLMs without fingerprinting... So even if a major player (e.g., OpenAI) uses robust fingerprints, it may not be sufficient against many automated avoidance techs one could throw together quickly</p>",
"contentMap": {
"en": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://econtwitter.net/@paulgp\" class=\"u-url mention\">@<span>paulgp</span></a></span> Mmm that's a good call, though exactly how much human editing is required to avoid which methods and thresholds of detection is the key Q (juice, squeeze, etc.). </p><p>We're also talking an endlessly increasing variety of LLMs, which can be arbitrarily mixed together, and a growing market for LLMs without fingerprinting... So even if a major player (e.g., OpenAI) uses robust fingerprints, it may not be sufficient against many automated avoidance techs one could throw together quickly</p>"
},
"attachment": [],
"tag": [
{
"type": "Mention",
"href": "https://econtwitter.net/users/paulgp",
"name": "@paulgp"
}
],
"replies": {
"id": "https://econtwitter.net/users/brhkim/statuses/109626073684693662/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://econtwitter.net/users/brhkim/statuses/109626073684693662/replies?only_other_accounts=true&page=true",
"partOf": "https://econtwitter.net/users/brhkim/statuses/109626073684693662/replies",
"items": []
}
},
"likes": {
"id": "https://econtwitter.net/users/brhkim/statuses/109626073684693662/likes",
"type": "Collection",
"totalItems": 0
},
"shares": {
"id": "https://econtwitter.net/users/brhkim/statuses/109626073684693662/shares",
"type": "Collection",
"totalItems": 0
}
}