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://mastodon.social/users/rexi/statuses/113870913956081667",
"type": "Note",
"summary": null,
"inReplyTo": "https://mastodon.social/users/rexi/statuses/111684058639485722",
"published": "2025-01-22T07:50:56Z",
"url": "https://mastodon.social/@rexi/113870913956081667",
"attributedTo": "https://mastodon.social/users/rexi",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
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],
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"atomUri": "https://mastodon.social/users/rexi/statuses/113870913956081667",
"inReplyToAtomUri": "https://mastodon.social/users/rexi/statuses/111684058639485722",
"conversation": "tag:mastodon.social,2024-01-02:objectId=611359690:objectType=Conversation",
"content": "<p><a href=\"https://phys.org/news/2025-01-peptides-microplastics.html\" target=\"_blank\" rel=\"nofollow noopener\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">phys.org/news/2025-01-peptides</span><span class=\"invisible\">-microplastics.html</span></a></p><p>biophysical modeling to predict peptide-plastic interactions at atomic resolution, then validated the results with molecular dynamics simulations. The process was optimized with the addition of quantum annealing and reinforcement learning—specifically a method known as proximal policy optimization.</p><p>Using these tools, the authors identified a set of plastic-binding peptides with high affinities for polyethylene and polypropylene.</p>",
"contentMap": {
"en": "<p><a href=\"https://phys.org/news/2025-01-peptides-microplastics.html\" target=\"_blank\" rel=\"nofollow noopener\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">phys.org/news/2025-01-peptides</span><span class=\"invisible\">-microplastics.html</span></a></p><p>biophysical modeling to predict peptide-plastic interactions at atomic resolution, then validated the results with molecular dynamics simulations. The process was optimized with the addition of quantum annealing and reinforcement learning—specifically a method known as proximal policy optimization.</p><p>Using these tools, the authors identified a set of plastic-binding peptides with high affinities for polyethylene and polypropylene.</p>"
},
"attachment": [],
"tag": [],
"replies": {
"id": "https://mastodon.social/users/rexi/statuses/113870913956081667/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://mastodon.social/users/rexi/statuses/113870913956081667/replies?only_other_accounts=true&page=true",
"partOf": "https://mastodon.social/users/rexi/statuses/113870913956081667/replies",
"items": []
}
},
"likes": {
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"shares": {
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}