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",
"blurhash": "toot:blurhash",
"focalPoint": {
"@container": "@list",
"@id": "toot:focalPoint"
},
"Hashtag": "as:Hashtag"
}
],
"id": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2025-06-05T09:24:52Z",
"url": "https://sigmoid.social/@pixeltracker/114630032900529595",
"attributedTo": "https://sigmoid.social/users/pixeltracker",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://sigmoid.social/users/pixeltracker/followers"
],
"sensitive": false,
"atomUri": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595",
"inReplyToAtomUri": null,
"conversation": "tag:sigmoid.social,2025-06-05:objectId=59275471:objectType=Conversation",
"content": "<p>🧠 New <a href=\"https://sigmoid.social/tags/preprint\" class=\"mention hashtag\" rel=\"tag\">#<span>preprint</span></a>! Confavreux et al. use meta-learning to uncover thousands of diverse, local <a href=\"https://sigmoid.social/tags/plasticity\" class=\"mention hashtag\" rel=\"tag\">#<span>plasticity</span></a> rule quadruplets that stabilize <a href=\"https://sigmoid.social/tags/RecurrentSpikingNetworks\" class=\"mention hashtag\" rel=\"tag\">#<span>RecurrentSpikingNetworks</span></a> — and incidentally support <a href=\"https://sigmoid.social/tags/memory\" class=\"mention hashtag\" rel=\"tag\">#<span>memory</span></a> functions like novelty detection, replay, & contextual prediction. A striking case of function emerging from stability.</p><p>📄 <a href=\"https://doi.org/10.1101/2025.05.28.656584\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.1101/2025.05.28.656</span><span class=\"invisible\">584</span></a></p><p><a href=\"https://sigmoid.social/tags/Neuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>Neuroscience</span></a> <a href=\"https://sigmoid.social/tags/Plasticity\" class=\"mention hashtag\" rel=\"tag\">#<span>Plasticity</span></a> <a href=\"https://sigmoid.social/tags/ComputationalNeuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalNeuroscience</span></a> <a href=\"https://sigmoid.social/tags/CompNeuro\" class=\"mention hashtag\" rel=\"tag\">#<span>CompNeuro</span></a> <a href=\"https://sigmoid.social/tags/SNN\" class=\"mention hashtag\" rel=\"tag\">#<span>SNN</span></a> <a href=\"https://sigmoid.social/tags/SpikingNeurons\" class=\"mention hashtag\" rel=\"tag\">#<span>SpikingNeurons</span></a></p>",
"contentMap": {
"en": "<p>🧠 New <a href=\"https://sigmoid.social/tags/preprint\" class=\"mention hashtag\" rel=\"tag\">#<span>preprint</span></a>! Confavreux et al. use meta-learning to uncover thousands of diverse, local <a href=\"https://sigmoid.social/tags/plasticity\" class=\"mention hashtag\" rel=\"tag\">#<span>plasticity</span></a> rule quadruplets that stabilize <a href=\"https://sigmoid.social/tags/RecurrentSpikingNetworks\" class=\"mention hashtag\" rel=\"tag\">#<span>RecurrentSpikingNetworks</span></a> — and incidentally support <a href=\"https://sigmoid.social/tags/memory\" class=\"mention hashtag\" rel=\"tag\">#<span>memory</span></a> functions like novelty detection, replay, & contextual prediction. A striking case of function emerging from stability.</p><p>📄 <a href=\"https://doi.org/10.1101/2025.05.28.656584\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">doi.org/10.1101/2025.05.28.656</span><span class=\"invisible\">584</span></a></p><p><a href=\"https://sigmoid.social/tags/Neuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>Neuroscience</span></a> <a href=\"https://sigmoid.social/tags/Plasticity\" class=\"mention hashtag\" rel=\"tag\">#<span>Plasticity</span></a> <a href=\"https://sigmoid.social/tags/ComputationalNeuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalNeuroscience</span></a> <a href=\"https://sigmoid.social/tags/CompNeuro\" class=\"mention hashtag\" rel=\"tag\">#<span>CompNeuro</span></a> <a href=\"https://sigmoid.social/tags/SNN\" class=\"mention hashtag\" rel=\"tag\">#<span>SNN</span></a> <a href=\"https://sigmoid.social/tags/SpikingNeurons\" class=\"mention hashtag\" rel=\"tag\">#<span>SpikingNeurons</span></a></p>"
},
"updated": "2025-06-05T09:26:15Z",
"attachment": [
{
"type": "Document",
"mediaType": "image/jpeg",
"url": "https://cdn.masto.host/sigmoidsocial/media_attachments/files/114/630/038/297/633/405/original/658bc114245a8bfc.jpeg",
"name": "Figure 1 from the preprint.",
"blurhash": "U9QT4LVYMw=v~qWBt8TK-oxtM_s:%Lx[WVM|",
"width": 1244,
"height": 436
}
],
"tag": [
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/preprint",
"name": "#preprint"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/plasticity",
"name": "#plasticity"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/recurrentspikingnetworks",
"name": "#recurrentspikingnetworks"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/memory",
"name": "#memory"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/neuroscience",
"name": "#neuroscience"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/computationalneuroscience",
"name": "#computationalneuroscience"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/compneuro",
"name": "#compneuro"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/snn",
"name": "#snn"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/spikingneurons",
"name": "#spikingneurons"
}
],
"replies": {
"id": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595/replies?only_other_accounts=true&page=true",
"partOf": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595/replies",
"items": []
}
},
"likes": {
"id": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595/likes",
"type": "Collection",
"totalItems": 1
},
"shares": {
"id": "https://sigmoid.social/users/pixeltracker/statuses/114630032900529595/shares",
"type": "Collection",
"totalItems": 1
}
}