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.
{
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{
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"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://sigmoid.social/users/nbara/statuses/109364295784408988",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2022-11-18T10:18:40Z",
"url": "https://sigmoid.social/@nbara/109364295784408988",
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"content": "<p>Hi all, I’m a researcher working on brain-computer interfaces at Snap Inc.</p><p>My goal is to prove that noninvasive <a href=\"https://sigmoid.social/tags/BCI\" class=\"mention hashtag\" rel=\"tag\">#<span>BCI</span></a> and <a href=\"https://sigmoid.social/tags/neurofeedback\" class=\"mention hashtag\" rel=\"tag\">#<span>neurofeedback</span></a> can provide value outside of the lab.</p><p>Currently for me that includes (but does not limit to) applying <a href=\"https://sigmoid.social/tags/deeplearning\" class=\"mention hashtag\" rel=\"tag\">#<span>deeplearning</span></a> methods to <a href=\"https://sigmoid.social/tags/timeseries\" class=\"mention hashtag\" rel=\"tag\">#<span>timeseries</span></a> data like <a href=\"https://sigmoid.social/tags/EEG\" class=\"mention hashtag\" rel=\"tag\">#<span>EEG</span></a>, but also other sensors like <a href=\"https://sigmoid.social/tags/eyetracking\" class=\"mention hashtag\" rel=\"tag\">#<span>eyetracking</span></a> or inertial sensors (<a href=\"https://sigmoid.social/tags/IMUs\" class=\"mention hashtag\" rel=\"tag\">#<span>IMUs</span></a>)
</p><p>
Also a <a href=\"https://sigmoid.social/tags/python\" class=\"mention hashtag\" rel=\"tag\">#<span>python</span></a> geek. I maintain an M/EEG analysis toolbox<br />➡️ <a href=\"https://github.com/nbara/python-meegkit\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">github.com/nbara/python-meegki</span><span class=\"invisible\">t</span></a> </p><p>
<a href=\"https://sigmoid.social/tags/introduction\" class=\"mention hashtag\" rel=\"tag\">#<span>introduction</span></a> <a href=\"https://sigmoid.social/tags/neuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>neuroscience</span></a> <a href=\"https://sigmoid.social/tags/altac\" class=\"mention hashtag\" rel=\"tag\">#<span>altac</span></a> <a href=\"https://sigmoid.social/tags/neurotech\" class=\"mention hashtag\" rel=\"tag\">#<span>neurotech</span></a></p>",
"contentMap": {
"en": "<p>Hi all, I’m a researcher working on brain-computer interfaces at Snap Inc.</p><p>My goal is to prove that noninvasive <a href=\"https://sigmoid.social/tags/BCI\" class=\"mention hashtag\" rel=\"tag\">#<span>BCI</span></a> and <a href=\"https://sigmoid.social/tags/neurofeedback\" class=\"mention hashtag\" rel=\"tag\">#<span>neurofeedback</span></a> can provide value outside of the lab.</p><p>Currently for me that includes (but does not limit to) applying <a href=\"https://sigmoid.social/tags/deeplearning\" class=\"mention hashtag\" rel=\"tag\">#<span>deeplearning</span></a> methods to <a href=\"https://sigmoid.social/tags/timeseries\" class=\"mention hashtag\" rel=\"tag\">#<span>timeseries</span></a> data like <a href=\"https://sigmoid.social/tags/EEG\" class=\"mention hashtag\" rel=\"tag\">#<span>EEG</span></a>, but also other sensors like <a href=\"https://sigmoid.social/tags/eyetracking\" class=\"mention hashtag\" rel=\"tag\">#<span>eyetracking</span></a> or inertial sensors (<a href=\"https://sigmoid.social/tags/IMUs\" class=\"mention hashtag\" rel=\"tag\">#<span>IMUs</span></a>)
</p><p>
Also a <a href=\"https://sigmoid.social/tags/python\" class=\"mention hashtag\" rel=\"tag\">#<span>python</span></a> geek. I maintain an M/EEG analysis toolbox<br />➡️ <a href=\"https://github.com/nbara/python-meegkit\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">github.com/nbara/python-meegki</span><span class=\"invisible\">t</span></a> </p><p>
<a href=\"https://sigmoid.social/tags/introduction\" class=\"mention hashtag\" rel=\"tag\">#<span>introduction</span></a> <a href=\"https://sigmoid.social/tags/neuroscience\" class=\"mention hashtag\" rel=\"tag\">#<span>neuroscience</span></a> <a href=\"https://sigmoid.social/tags/altac\" class=\"mention hashtag\" rel=\"tag\">#<span>altac</span></a> <a href=\"https://sigmoid.social/tags/neurotech\" class=\"mention hashtag\" rel=\"tag\">#<span>neurotech</span></a></p>"
},
"updated": "2022-11-18T10:29:54Z",
"attachment": [],
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{
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},
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},
{
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"href": "https://sigmoid.social/tags/imus",
"name": "#imus"
},
{
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"name": "#Python"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/Introduction",
"name": "#Introduction"
},
{
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],
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