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://mastodon.social/users/ErikJonker/statuses/113486613595518370",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2024-11-15T10:58:18Z",
"url": "https://mastodon.social/@ErikJonker/113486613595518370",
"attributedTo": "https://mastodon.social/users/ErikJonker",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://mastodon.social/users/ErikJonker/followers"
],
"sensitive": false,
"atomUri": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370",
"inReplyToAtomUri": null,
"conversation": "tag:mastodon.social,2024-11-15:objectId=848692488:objectType=Conversation",
"content": "<p>I am using both GPT-4o and NotebookLM, i have the impression that NotebookLM is much better at summarizing and analyzing texts, i am wondering whether that's because of NotebookLM implementation aspects or because of that Gemini Pro 1.5 is better at this task then GPT-4o ? 🤔 <br /><a href=\"https://mastodon.social/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a> <a href=\"https://mastodon.social/tags/NotebookLM\" class=\"mention hashtag\" rel=\"tag\">#<span>NotebookLM</span></a> <a href=\"https://mastodon.social/tags/Geminipro\" class=\"mention hashtag\" rel=\"tag\">#<span>Geminipro</span></a> <a href=\"https://mastodon.social/tags/ChatGPT\" class=\"mention hashtag\" rel=\"tag\">#<span>ChatGPT</span></a> <a href=\"https://mastodon.social/tags/openai\" class=\"mention hashtag\" rel=\"tag\">#<span>openai</span></a> <a href=\"https://mastodon.social/tags/google\" class=\"mention hashtag\" rel=\"tag\">#<span>google</span></a> <a href=\"https://mastodon.social/tags/text\" class=\"mention hashtag\" rel=\"tag\">#<span>text</span></a> <a href=\"https://mastodon.social/tags/summarizing\" class=\"mention hashtag\" rel=\"tag\">#<span>summarizing</span></a> <a href=\"https://mastodon.social/tags/analysing\" class=\"mention hashtag\" rel=\"tag\">#<span>analysing</span></a></p>",
"contentMap": {
"en": "<p>I am using both GPT-4o and NotebookLM, i have the impression that NotebookLM is much better at summarizing and analyzing texts, i am wondering whether that's because of NotebookLM implementation aspects or because of that Gemini Pro 1.5 is better at this task then GPT-4o ? 🤔 <br /><a href=\"https://mastodon.social/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a> <a href=\"https://mastodon.social/tags/NotebookLM\" class=\"mention hashtag\" rel=\"tag\">#<span>NotebookLM</span></a> <a href=\"https://mastodon.social/tags/Geminipro\" class=\"mention hashtag\" rel=\"tag\">#<span>Geminipro</span></a> <a href=\"https://mastodon.social/tags/ChatGPT\" class=\"mention hashtag\" rel=\"tag\">#<span>ChatGPT</span></a> <a href=\"https://mastodon.social/tags/openai\" class=\"mention hashtag\" rel=\"tag\">#<span>openai</span></a> <a href=\"https://mastodon.social/tags/google\" class=\"mention hashtag\" rel=\"tag\">#<span>google</span></a> <a href=\"https://mastodon.social/tags/text\" class=\"mention hashtag\" rel=\"tag\">#<span>text</span></a> <a href=\"https://mastodon.social/tags/summarizing\" class=\"mention hashtag\" rel=\"tag\">#<span>summarizing</span></a> <a href=\"https://mastodon.social/tags/analysing\" class=\"mention hashtag\" rel=\"tag\">#<span>analysing</span></a></p>"
},
"attachment": [],
"tag": [
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/ai",
"name": "#ai"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/notebooklm",
"name": "#notebooklm"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/geminipro",
"name": "#geminipro"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/chatgpt",
"name": "#chatgpt"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/openai",
"name": "#openai"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/google",
"name": "#google"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/text",
"name": "#text"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/summarizing",
"name": "#summarizing"
},
{
"type": "Hashtag",
"href": "https://mastodon.social/tags/analysing",
"name": "#analysing"
}
],
"replies": {
"id": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370/replies?only_other_accounts=true&page=true",
"partOf": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370/replies",
"items": []
}
},
"likes": {
"id": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370/likes",
"type": "Collection",
"totalItems": 2
},
"shares": {
"id": "https://mastodon.social/users/ErikJonker/statuses/113486613595518370/shares",
"type": "Collection",
"totalItems": 2
}
}