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",
"Hashtag": "as:Hashtag"
}
],
"id": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/activity",
"type": "Create",
"actor": "https://mathstodon.xyz/users/dlzv",
"published": "2022-11-05T11:56:23Z",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://mathstodon.xyz/users/dlzv/followers"
],
"object": {
"id": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2022-11-05T11:56:23Z",
"url": "https://mathstodon.xyz/@dlzv/109291070024045910",
"attributedTo": "https://mathstodon.xyz/users/dlzv",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://mathstodon.xyz/users/dlzv/followers"
],
"sensitive": false,
"atomUri": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910",
"inReplyToAtomUri": null,
"conversation": "tag:mathstodon.xyz,2022-11-05:objectId=28369321:objectType=Conversation",
"content": "<p>A short thread on <a href=\"https://mathstodon.xyz/tags/graph\" class=\"mention hashtag\" rel=\"tag\">#<span>graph</span></a> algorithms in the language of <a href=\"https://mathstodon.xyz/tags/LinearAlgebra\" class=\"mention hashtag\" rel=\"tag\">#<span>LinearAlgebra</span></a> 🧵<br />It is well-known that the adjacency matrix is a useful tool to compute various properties of the underlying graph: the number of connected components, isomorphism to other graphs, etc.<br />But can we go further? Most graph algorithms are still expressed in terms of iterations over all nodes or edges. Can we leverage linear algebra to express these operations more efficiently?<br />(1/n)</p>",
"contentMap": {
"en": "<p>A short thread on <a href=\"https://mathstodon.xyz/tags/graph\" class=\"mention hashtag\" rel=\"tag\">#<span>graph</span></a> algorithms in the language of <a href=\"https://mathstodon.xyz/tags/LinearAlgebra\" class=\"mention hashtag\" rel=\"tag\">#<span>LinearAlgebra</span></a> 🧵<br />It is well-known that the adjacency matrix is a useful tool to compute various properties of the underlying graph: the number of connected components, isomorphism to other graphs, etc.<br />But can we go further? Most graph algorithms are still expressed in terms of iterations over all nodes or edges. Can we leverage linear algebra to express these operations more efficiently?<br />(1/n)</p>"
},
"attachment": [],
"tag": [
{
"type": "Hashtag",
"href": "https://mathstodon.xyz/tags/linearalgebra",
"name": "#linearalgebra"
},
{
"type": "Hashtag",
"href": "https://mathstodon.xyz/tags/graph",
"name": "#graph"
}
],
"replies": {
"id": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/replies?min_id=109291071352945761&page=true",
"partOf": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/replies",
"items": [
"https://mathstodon.xyz/users/dlzv/statuses/109291071352945761"
]
}
},
"likes": {
"id": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/likes",
"type": "Collection",
"totalItems": 31
},
"shares": {
"id": "https://mathstodon.xyz/users/dlzv/statuses/109291070024045910/shares",
"type": "Collection",
"totalItems": 26
}
}
}