@omarsar0
Top 50 LLM Interview Questions. Looks like a great resource to learn LLM basics: https://t.co/nCik0PGOcb
🐦 Twitter Post Details
Viewing enriched Twitter post
📊 Media Metadata
{
"media": [
{
"type": "image",
"url": "https://crmoxkoizveukayfjuyo.supabase.co/storage/v1/object/public/media/posts/1930984834454712537/media_0.jpg?",
"filename": "media_0.jpg"
}
],
"nlp": {
"sentiment": "positive",
"topics": [
"LLM Interview Questions",
"learning resources",
"LLM basics"
],
"entities": [],
"summary": "The tweet highlights a valuable resource for learning about LLM basics through interview questions.",
"language": "en",
"processed_at": "2025-08-07T11:49:21.083822"
}
}🔧 Raw API Response
{
"data": {
"threaded_conversation_with_injections_v2": {
"instructions": [
{
"type": "TimelineClearCache"
},
{
"type": "TimelineAddEntries",
"entries": [
{
"entryId": "tweet-1930984834454712537",
"sortIndex": "1953513795087237120",
"content": {
"entryType": "TimelineTimelineItem",
"__typename": "TimelineTimelineItem",
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1930984834454712537",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjozNDQ4Mjg0MzEz",
"rest_id": "3448284313",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": true,
"created_at": "Fri Sep 04 12:59:26 +0000 2015",
"default_profile": false,
"default_profile_image": false,
"description": "Building with AI agents @dair_ai • Prev: Meta AI, Galactica LLM, Elastic, PaperswithCode, PhD • I share insights on how to build with AI Agents ⬇️",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "dair-ai.thinkific.com",
"expanded_url": "https://dair-ai.thinkific.com/",
"url": "https://t.co/JBU5beHQNs",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 31690,
"followers_count": 258917,
"friends_count": 643,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 4290,
"location": "",
"media_count": 3776,
"name": "elvis",
"normal_followers_count": 258917,
"pinned_tweet_ids_str": [
"1953502698801623159"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/3448284313/1565974901",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/939313677647282181/vZjFWtAn_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "omarsar0",
"statuses_count": 15313,
"translator_type": "regular",
"url": "https://t.co/JBU5beHQNs",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1460724937073770505",
"professional_type": "Creator",
"category": [
{
"id": 713,
"name": "Science & Technology",
"icon_name": ""
}
]
},
"tipjar_settings": {
"is_enabled": false,
"cash_app_handle": "£elvisomarsaravia"
}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1930984834454712537"
],
"editable_until_msecs": "1749221235000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "345804",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 6708,
"bookmarked": false,
"created_at": "Fri Jun 06 13:47:15 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
0,
81
],
"entities": {
"hashtags": [],
"media": [
{
"display_url": "pic.x.com/nCik0PGOcb",
"expanded_url": "https://x.com/omarsar0/status/1930984834454712537/photo/1",
"id_str": "1930983133110796288",
"indices": [
82,
105
],
"media_key": "3_1930983133110796288",
"media_url_https": "https://pbs.twimg.com/media/Gsw67cRXAAAoP1B.png",
"type": "photo",
"url": "https://t.co/nCik0PGOcb",
"ext_media_availability": {
"status": "Available"
},
"features": {
"large": {
"faces": [
{
"x": 60,
"y": 355,
"h": 513,
"w": 513
},
{
"x": 84,
"y": 1219,
"h": 460,
"w": 460
},
{
"x": 90,
"y": 772,
"h": 479,
"w": 479
}
]
},
"medium": {
"faces": [
{
"x": 35,
"y": 211,
"h": 305,
"w": 305
},
{
"x": 50,
"y": 726,
"h": 274,
"w": 274
},
{
"x": 53,
"y": 459,
"h": 285,
"w": 285
}
]
},
"small": {
"faces": [
{
"x": 20,
"y": 119,
"h": 173,
"w": 173
},
{
"x": 28,
"y": 411,
"h": 155,
"w": 155
},
{
"x": 30,
"y": 260,
"h": 161,
"w": 161
}
]
},
"orig": {
"faces": [
{
"x": 60,
"y": 355,
"h": 513,
"w": 513
},
{
"x": 84,
"y": 1219,
"h": 460,
"w": 460
},
{
"x": 90,
"y": 772,
"h": 479,
"w": 479
}
]
}
},
"sizes": {
"large": {
"h": 2014,
"w": 1566,
"resize": "fit"
},
"medium": {
"h": 1200,
"w": 933,
"resize": "fit"
},
"small": {
"h": 680,
"w": 529,
"resize": "fit"
},
"thumb": {
"h": 150,
"w": 150,
"resize": "crop"
}
},
"original_info": {
"height": 2014,
"width": 1566,
"focus_rects": [
{
"x": 0,
"y": 0,
"w": 1566,
"h": 877
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 1566
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 1785
},
{
"x": 280,
"y": 0,
"w": 1007,
"h": 2014
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 2014
}
]
},
"allow_download_status": {
"allow_download": true
},
"media_results": {
"result": {
"media_key": "3_1930983133110796288"
}
}
}
],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": []
},
"extended_entities": {
"media": [
{
"display_url": "pic.x.com/nCik0PGOcb",
"expanded_url": "https://x.com/omarsar0/status/1930984834454712537/photo/1",
"id_str": "1930983133110796288",
"indices": [
82,
105
],
"media_key": "3_1930983133110796288",
"media_url_https": "https://pbs.twimg.com/media/Gsw67cRXAAAoP1B.png",
"type": "photo",
"url": "https://t.co/nCik0PGOcb",
"ext_media_availability": {
"status": "Available"
},
"features": {
"large": {
"faces": [
{
"x": 60,
"y": 355,
"h": 513,
"w": 513
},
{
"x": 84,
"y": 1219,
"h": 460,
"w": 460
},
{
"x": 90,
"y": 772,
"h": 479,
"w": 479
}
]
},
"medium": {
"faces": [
{
"x": 35,
"y": 211,
"h": 305,
"w": 305
},
{
"x": 50,
"y": 726,
"h": 274,
"w": 274
},
{
"x": 53,
"y": 459,
"h": 285,
"w": 285
}
]
},
"small": {
"faces": [
{
"x": 20,
"y": 119,
"h": 173,
"w": 173
},
{
"x": 28,
"y": 411,
"h": 155,
"w": 155
},
{
"x": 30,
"y": 260,
"h": 161,
"w": 161
}
]
},
"orig": {
"faces": [
{
"x": 60,
"y": 355,
"h": 513,
"w": 513
},
{
"x": 84,
"y": 1219,
"h": 460,
"w": 460
},
{
"x": 90,
"y": 772,
"h": 479,
"w": 479
}
]
}
},
"sizes": {
"large": {
"h": 2014,
"w": 1566,
"resize": "fit"
},
"medium": {
"h": 1200,
"w": 933,
"resize": "fit"
},
"small": {
"h": 680,
"w": 529,
"resize": "fit"
},
"thumb": {
"h": 150,
"w": 150,
"resize": "crop"
}
},
"original_info": {
"height": 2014,
"width": 1566,
"focus_rects": [
{
"x": 0,
"y": 0,
"w": 1566,
"h": 877
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 1566
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 1785
},
{
"x": 280,
"y": 0,
"w": 1007,
"h": 2014
},
{
"x": 0,
"y": 0,
"w": 1566,
"h": 2014
}
]
},
"allow_download_status": {
"allow_download": true
},
"media_results": {
"result": {
"media_key": "3_1930983133110796288"
}
}
}
]
},
"favorite_count": 2755,
"favorited": false,
"full_text": "Top 50 LLM Interview Questions.\n\nLooks like a great resource to learn LLM basics: https://t.co/nCik0PGOcb",
"is_quote_status": false,
"lang": "en",
"possibly_sensitive": false,
"possibly_sensitive_editable": true,
"quote_count": 11,
"reply_count": 13,
"retweet_count": 392,
"retweeted": false,
"user_id_str": "3448284313",
"id_str": "1930984834454712537"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "SelfThread"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet"
}
}
},
{
"entryId": "conversationthread-1930984837264953814",
"sortIndex": "1953513795087237110",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1930984837264953814-tweet-1930984837264953814",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1930984837264953814",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjozNDQ4Mjg0MzEz",
"rest_id": "3448284313",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": true,
"created_at": "Fri Sep 04 12:59:26 +0000 2015",
"default_profile": false,
"default_profile_image": false,
"description": "Building with AI agents @dair_ai • Prev: Meta AI, Galactica LLM, Elastic, PaperswithCode, PhD • I share insights on how to build with AI Agents ⬇️",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "dair-ai.thinkific.com",
"expanded_url": "https://dair-ai.thinkific.com/",
"url": "https://t.co/JBU5beHQNs",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 31690,
"followers_count": 258917,
"friends_count": 643,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 4290,
"location": "",
"media_count": 3776,
"name": "elvis",
"normal_followers_count": 258917,
"pinned_tweet_ids_str": [
"1953502698801623159"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/3448284313/1565974901",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/939313677647282181/vZjFWtAn_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "omarsar0",
"statuses_count": 15313,
"translator_type": "regular",
"url": "https://t.co/JBU5beHQNs",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1460724937073770505",
"professional_type": "Creator",
"category": [
{
"id": 713,
"name": "Science & Technology",
"icon_name": ""
}
]
},
"tipjar_settings": {
"is_enabled": false,
"cash_app_handle": "£elvisomarsaravia"
}
}
}
},
"card": {
"rest_id": "https://t.co/fJYIyLHUbd",
"legacy": {
"binding_values": [
{
"key": "vanity_url",
"value": {
"scribe_key": "vanity_url",
"string_value": "drive.google.com",
"type": "STRING"
}
},
{
"key": "domain",
"value": {
"string_value": "drive.google.com",
"type": "STRING"
}
},
{
"key": "title",
"value": {
"string_value": "LLM Interview Questions.pdf",
"type": "STRING"
}
},
{
"key": "card_url",
"value": {
"scribe_key": "card_url",
"string_value": "https://t.co/fJYIyLHUbd",
"type": "STRING"
}
}
],
"card_platform": {
"platform": {
"audience": {
"name": "production"
},
"device": {
"name": "Android",
"version": "12"
}
}
},
"name": "summary",
"url": "https://t.co/fJYIyLHUbd",
"user_refs_results": []
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1930984837264953814"
],
"editable_until_msecs": "1749221235000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "15918",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 410,
"bookmarked": false,
"created_at": "Fri Jun 06 13:47:15 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
0,
23
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [
{
"display_url": "drive.google.com/file/d/1cUxKsp…",
"expanded_url": "https://drive.google.com/file/d/1cUxKspEXgQ64s4OFEw0kabf_qNauOPiH/view?usp=sharing",
"url": "https://t.co/fJYIyLHUbd",
"indices": [
0,
23
]
}
],
"user_mentions": []
},
"favorite_count": 172,
"favorited": false,
"full_text": "https://t.co/fJYIyLHUbd",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "zxx",
"possibly_sensitive": false,
"possibly_sensitive_editable": true,
"quote_count": 1,
"reply_count": 3,
"retweet_count": 24,
"retweeted": false,
"user_id_str": "3448284313",
"id_str": "1930984837264953814"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "SelfThread"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1930984837264953814"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931043945142001716",
"sortIndex": "1953513795087237100",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931043945142001716-tweet-1931043945142001716",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931043945142001716",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjo5NTAxNDQ4NjE1NjIzMjI5NDQ=",
"rest_id": "950144861562322944",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": true,
"created_at": "Sun Jan 07 23:19:22 +0000 2018",
"default_profile": true,
"default_profile_image": false,
"description": "WARHODL IS A POP ARTIST KNOWN FOR HIS CRYPTO INVOLVEMENT, COLLABORATING WITH PARIS HILTON & BEING SHOWN BY THE GETTY MUSEUM. IRL @taylorgood",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "WARHODL.com",
"expanded_url": "http://WARHODL.com",
"url": "https://t.co/ODIjisog4g",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 49562,
"followers_count": 13222,
"friends_count": 2808,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 155,
"location": "",
"media_count": 1742,
"name": "WARHODL",
"normal_followers_count": 13222,
"pinned_tweet_ids_str": [
"1603454701424386048"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/950144861562322944/1754176581",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1951783372176695296/IIVxWAk8_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "warhodl",
"statuses_count": 13299,
"translator_type": "none",
"url": "https://t.co/ODIjisog4g",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931043945142001716"
],
"editable_until_msecs": "1749235328000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "2918",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
"legacy": {
"bookmark_count": 1,
"bookmarked": false,
"created_at": "Fri Jun 06 17:42:08 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
23
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
},
{
"id_str": "123149469",
"name": "Taylor Good",
"screen_name": "taylorgood",
"indices": [
12,
23
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 👀 @taylorgood",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "qme",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "950144861562322944",
"id_str": "1931043945142001716"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931043945142001716"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931005165093458325",
"sortIndex": "1953513795087237090",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931005165093458325-tweet-1931005165093458325",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931005165093458325",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNDYyNTk3MzI4MTcxNDA5NDEw",
"rest_id": "1462597328171409410",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": false,
"created_at": "Mon Nov 22 01:42:40 +0000 2021",
"default_profile": true,
"default_profile_image": false,
"description": "Live your Creativity™\nPhotographer, artist & AI/tech explorer | Red Pilled | https://t.co/QogtLoSVUM",
"entities": {
"description": {
"urls": [
{
"display_url": "alexcovo.com",
"expanded_url": "http://alexcovo.com",
"url": "https://t.co/QogtLoSVUM",
"indices": [
79,
102
]
}
]
},
"url": {
"urls": [
{
"display_url": "agentx.agency",
"expanded_url": "http://agentx.agency",
"url": "https://t.co/dYK5pS2kgP",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 46695,
"followers_count": 4093,
"friends_count": 1767,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 56,
"location": "New York, USA",
"media_count": 1802,
"name": "alexcovo.eth",
"normal_followers_count": 4093,
"pinned_tweet_ids_str": [
"1861064977693487117"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/1462597328171409410/1742310137",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1902014032703377409/hTXxa370_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "alexcovo_eth",
"statuses_count": 13392,
"translator_type": "none",
"url": "https://t.co/dYK5pS2kgP",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1469023669057986569",
"professional_type": "Creator",
"category": [
{
"id": 552,
"name": "Photographer",
"icon_name": ""
}
]
},
"tipjar_settings": {}
}
}
},
"card": {
"rest_id": "https://t.co/ODVsVACOLt",
"legacy": {
"binding_values": [
{
"key": "unified_card",
"value": {
"string_value": "{\"component_objects\":{\"details_1\":{\"type\":\"grok_share\",\"data\":{\"profile_user\":{\"id_str\":\"1462597328171409410\",\"profile_image_url_https\":\"https:\\/\\/pbs.twimg.com\\/profile_images\\/1902014032703377409\\/hTXxa370.jpg\",\"name\":\"alexcovo.eth\",\"screen_name\":\"alexcovo_eth\",\"verified\":false,\"is_blue_verified\":true},\"grok_user\":{\"id_str\":\"1720665183188922368\",\"profile_image_url_https\":\"https:\\/\\/pbs.twimg.com\\/profile_images\\/1893219113717342208\\/Vgg2hEPa.jpg\",\"name\":\"Grok\",\"screen_name\":\"grok\",\"highlighted_label\":{\"description\":\"xAI\",\"badge\":{\"url\":\"https:\\/\\/pbs.twimg.com\\/profile_images\\/1769430779845611520\\/lIgjSJGU_bigger.jpg\"},\"url\":{\"urlType\":\"DeepLink\",\"url\":\"https:\\/\\/twitter.com\\/xai\"},\"userLabelType\":\"BusinessLabel\",\"userLabelDisplayType\":\"Badge\"},\"verified\":false,\"is_blue_verified\":true,\"verified_type\":\"Business\"},\"id\":\"lwnJbVSAByDP9rY0sTE3ANCen\",\"conversation_preview\":[{\"message\":\"Can you write the top 50 questions and the answers?\",\"sender\":\"USER\",\"grokMode\":\"NORMAL\",\"mediaUrls\":[]},{\"message\":\"Below is a comprehensive list of Top 50 Large Language Model (LLM) Interview Questions along with detailed answers. These questions are inspired by the structure and topics mentioned in the X post by @omarsar0, web search results, and general knowledge about LLMs up to my last update in late 2023, extended with logical projections to 2025 trends. The questions are divided into categories such as foundational concepts, tokenization, transformers, fine-tuning, evaluation, ethics, and practical applications, reflecting the growing demand for LLM expertise in AI roles.\\n--\\nFoundational Concepts (Questions 1-10)\\nWhat is a Large Language Model (LLM)? \\nAnswer: A Large Language Model (LLM) is a type of AI model designed to understand and generate human-like text by leveraging vast amounts of training data. LLMs are typically based on neural networks, especially transformers, and are pre-trained on diverse datasets like books, websites, and articles. Examples include GPT-4 by OpenAI and Google\\u2019s PaLM 2. They excel in tasks like text generation, translation, and question-answering due to their ability to capture linguistic patterns.\\nWhat is the difference between a traditional language model and an LLM? \\nAnswer: Traditional language models, like n-gram models or early RNNs, predict the next word based on a limited context window and struggle with long-range dependencies. LLMs, built on transformer architectures, handle larger contexts, scale to billions of parameters (e.g., PaLM 2 with 340B parameters), and are pre-trained on massive datasets, making them more versatile and capable of tasks like zero-shot learning.\\nWhat is tokenization, and why is it critical for LLMs? \\nAnswer: Tokenization is the process of breaking down text into smaller units (tokens), such as words, subwords, or characters, that an LLM can process. For example, \\\"playing\\\" might be tokenized into \\\"play\\\" and \\\"##ing\\\" using subword tokenization like Byte-Pair Encoding (BPE). It\\u2019s critical because LLMs operate on numerical representations of tokens, and effective tokenization ensures the model can handle diverse languages, rare words, and varying contexts efficiently.\\nWhat are the main challenges in training LLMs? \\nAnswer: Challenges include: (1) Computational cost\\u2014training models like GPT-3 (175B parameters) requires massive GPU clusters; (2) Data quality\\u2014bias in datasets (e.g., Wikipedia\\u2019s underrepresentation of certain demographics) can lead to biased outputs; (3) Overfitting\\u2014models may memorize training data instead of generalizing; (4) Environmental impact\\u2014training emits significant CO2 (e.g., GPT-3 training emitted ~552 tons of CO2, per 2021 estimates); and (5) Ethical concerns\\u2014misinformation or harmful outputs.\\nWhat is the role of pre-training in LLMs? \\nAnswer: Pre-training involves training an LLM on a large, general-purpose dataset to learn broad language patterns, such as grammar, semantics, and world knowledge. For example, BERT is pre-trained on masked language modeling (predicting masked words) and next-sentence prediction. This step equips the model with a foundational understanding, which can then be fine-tuned for specific tasks like sentiment analysis.\\nWhat is fine-tuning, and how does it differ from pre-training? \\nAnswer: Fine-tuning adjusts a pre-trained LLM on a smaller, task-specific dataset to improve performance on that task. For example, fine-tuning GPT-3 on medical texts improves its accuracy in medical Q&A. Pre-training is unsupervised and broad, while fine-tuning is supervised and targeted, often involving fewer epochs to avoid catastrophic forgetting of general knowledge.\\nWhat is the difference between supervised and unsupervised learning in the context of LLMs? \\nAnswer: Unsupervised learning, used in pre-training, involves learning patterns from unlabelled data (e.g., predicting the next word in a sentence). Supervised learning, often used in fine-tuning, requires labelled data (e.g., question-answer pairs) to train the model for specific tasks. Most LLMs combine both: unsupervised pre-training for general knowledge and supervised fine-tuning for specialization.\\nWhat is zero-shot learning, and how do LLMs achieve it? \\nAnswer: Zero-shot learning is the ability of an LLM to perform a task without specific training examples, relying on its pre-trained knowledge. For example, GPT-3 can translate French to English without being explicitly trained for translation, by understanding the prompt and leveraging its language understanding. This is achieved through broad pre-training on diverse datasets, enabling the model to generalize across tasks.\\nWhat is few-shot learning, and how does it compare to zero-shot learning? \\nAnswer: Few-shot learning involves providing an LLM with a small number of examples (e.g., 2-5) in the prompt to guide its performance on a task. For example, giving GPT-4 a few translation examples improves its accuracy compared to zero-shot. Few-shot learning outperforms zero-shot by providing context, but it requires more input than zero-shot, which relies entirely on pre-trained knowledge.\\nWhat is the role of the attention mechanism in LLMs? \\nAnswer: The attention mechanism, introduced in the 2017 paper \\\"Attention is All You Need,\\\" allows LLMs to weigh the importance of different words in a sentence when processing text. For example, in \\\"The cat, which is black, sits,\\\" attention helps the model focus on \\\"cat\\\" when predicting the verb \\\"sits.\\\" It enables transformers to handle long-range dependencies better than RNNs, making it foundational for modern LLMs.\\n--\\nTokenization and Encoding (Questions 11-15)\\nWhat are the different types of tokenization methods used in LLMs? \\nAnswer: Common methods include: (1) Word-level tokenization (splitting by spaces, e.g., \\\"I am\\\" \\u2192 [\\\"I\\\", \\\"am\\\"]); (2) Subword tokenization (e.g., BPE in GPT models, where \\\"playing\\\" \\u2192 [\\\"play\\\", \\\"##ing\\\"]); (3) Character-level tokenization (e.g., \\\"cat\\\" \\u2192 [\\\"c\\\", \\\"a\\\", \\\"t\\\"]); and (4) Byte-level tokenization (used in models like ByT5, handling raw bytes). Subword tokenization balances vocabulary size and meaning, making it popular in LLMs.\\nHow does Byte-Pair Encoding (BPE) work in tokenization? \\nAnswer: BPE starts with a character-level vocabulary and iteratively merges the most frequent pair of tokens into a single token. For example, in a corpus with \\\"low\\\" and \\\"lowest,\\\" BPE might merge \\\"l\\\" and \\\"o\\\" into \\\"lo,\\\" then \\\"lo\\\" and \\\"w\\\" into \\\"low.\\\" Used in GPT models, BPE reduces vocabulary size while handling rare words by breaking them into known subwords.\\nWhat are the advantages of subword tokenization over word-level tokenization? \\nAnswer: Subword tokenization: (1) Handles out-of-vocabulary words (e.g., \\\"unhappiness\\\" \\u2192 \\\"un\\\", \\\"happi\\\", \\\"##ness\\\"); (2) Reduces vocabulary size compared to word-level (e.g., 50k subword tokens vs. millions of words); (3) Captures morphological patterns (e.g., \\\"play\\\", \\\"##ing\\\"). Word-level tokenization struggles with rare words and large vocabularies, leading to inefficiency.\\nHow does tokenization impact the performance of LLMs? \\nAnswer: Tokenization affects performance by determining how well the model represents text. Poor tokenization (e.g., splitting \\\"COVID-19\\\" into meaningless parts) can lead to loss of semantic meaning, while efficient tokenization (e.g., BPE) improves handling of rare words and reduces memory usage. It also impacts speed\\u2014smaller vocabularies mean faster processing but may lose nuance.\\nWhat are the challenges of tokenization in multilingual LLMs? \\nAnswer: Challenges include: (1) Language-specific rules\\u2014e.g., Chinese lacks spaces, requiring character-based tokenization; (2) Vocabulary size\\u2014supporting multiple languages increases token count; (3) Cross-lingual transfer\\u2014tokens may not align across languages (e.g., \\\"dog\\\" in English vs. \\\"chien\\\" in French); and (4) Script diversity\\u2014handling scripts like Arabic (right-to-left) or Devanagari requires specialized tokenizers.\\n--\\nTransformers and Attention Mechanisms (Questions 16-20)\\nWhat are transformers, and how do they function in LLMs? \\nAnswer: Transformers, introduced in the 2017 paper \\\"Attention is All You Need,\\\" are neural network architectures that rely on self-attention to process input sequences in parallel. They consist of encoder and decoder layers, where self-attention computes the relevance of each token to others (e.g., \\\"The cat\\u2026sits\\\" links \\\"cat\\\" to \\\"sits\\\"). Transformers enable LLMs like GPT-4 to handle long-range dependencies and scale efficiently.\\nHow does self-attention differ from multi-head attention? \\nAnswer: Self-attention computes a weighted sum of all tokens in a sequence, where weights are determined by the similarity of token embeddings (via query, key, and value vectors). Multi-head attention, used in transformers, runs self-attention multiple times (heads) in parallel, each focusing on different aspects of the input (e.g., syntax vs. semantics), then concatenates the results for richer representations.\\nWhat is the role of positional encoding in transformers? \\nAnswer: Transformers process tokens in parallel, unlike RNNs, so they lack inherent order awareness. Positional encoding adds a vector to each token\\u2019s embedding to indicate its position in the sequence, using sinusoidal functions or learned embeddings. For example, in \\\"I am happy,\\\" positional encoding ensures \\\"I\\\" is treated as the first token, preserving sequence order.\\nWhat are the advantages of transformers over RNNs in LLMs? \\nAnswer: Transformers: (1) Process sequences in parallel, making them faster than sequential RNNs; (2) Capture long-range dependencies better via attention (RNNs suffer from vanishing gradients); (3) Scale to larger models and datasets; and (4) Enable transfer learning (e.g., BERT\\u2019s bidirectional context). RNNs, like LSTMs, are slower and less effective for long sequences.\\nWhat are some limitations of the attention mechanism in transformers? \\nAnswer: Limitations include: (1) Quadratic complexity\\u2014self-attention scales as O(n\\u00b2) with sequence length n, making it memory-intensive for long inputs; (2) Lack of explicit recurrence\\u2014transformers may struggle with tasks requiring sequential reasoning (e.g., counting); (3) Over-reliance on training data\\u2014attention may focus on spurious correlations; and (4) Interpretability\\u2014attention weights are hard to interpret causally.\\n--\\nFine-Tuning and Optimization (Questions 21-25)\\nWhat is the process of fine-tuning an LLM? \\nAnswer: Fine-tuning involves: (1) Starting with a pre-trained LLM; (2) Preparing a task-specific dataset (e.g., labeled customer reviews for sentiment analysis); (3) Adjusting model weights using supervised learning with a smaller learning rate; (4) Validating on a held-out set to avoid overfitting; and (5) Deploying the fine-tuned model. For example, fine-tuning BERT on SQuAD improves its question-answering accuracy.\\nWhat is catastrophic forgetting, and how can it be mitigated during fine-tuning? \\nAnswer: Catastrophic forgetting occurs when fine-tuning causes an LLM to lose general knowledge learned during pre-training. Mitigation strategies include: (1) Using a smaller learning rate to preserve pre-trained weights; (2) Freezing lower layers of the model (e.g., early transformer layers); (3) Regularization techniques like Elastic Weight Consolidation (EWC); and (4) Multi-task learning to retain general capabilities.\\nWhat is parameter-efficient fine-tuning (PEFT), and why is it useful? \\nAnswer: PEFT fine-tunes only a small subset of parameters, reducing computational cost. Methods like LoRA (Low-Rank Adaptation) add low-rank updates to weight matrices, while adapters insert small trainable layers. It\\u2019s useful for: (1) Reducing memory usage (e.g., fine-tuning a 175B parameter model like GPT-3 on a single GPU); (2) Faster training; and (3) Deploying on resource-constrained devices.\\nHow does prompt engineering differ from fine-tuning? \\nAnswer: Prompt engineering involves designing input prompts to guide an LLM\\u2019s behavior without changing its weights (e.g., \\\"Translate this: Bonjour\\\" for GPT-3). Fine-tuning modifies the model\\u2019s weights using task-specific data. Prompt engineering is faster and requires no training, but fine-tuning typically yields better performance for specialized tasks due to direct optimization.\\nWhat is the role of learning rate in fine-tuning LLMs? \\nAnswer: The learning rate controls how much the model\\u2019s weights are updated during fine-tuning. A smaller learning rate (e.g., 1e-5) ensures gradual updates, preserving pre-trained knowledge and preventing overfitting. A higher learning rate (e.g., 1e-3) may lead to faster convergence but risks catastrophic forgetting or instability, especially for large models like LLMs.\\n--\\nEvaluation and Metrics (Questions 26-30)\\nHow do you evaluate the performance of an LLM? \\nAnswer: Evaluation depends on the task: (1) For text generation, use metrics like BLEU (comparing generated text to reference text) or ROUGE (for summarization); (2) For classification tasks (e.g., sentiment analysis), use accuracy, precision, recall, and F1-score; (3) For human-like quality, use human evaluation (e.g., fluency, coherence); and (4) For efficiency, measure latency and throughput.\\nWhat is the BLEU score, and what are its limitations? \\nAnswer: BLEU (Bilingual Evaluation Understudy) measures the similarity between generated text and reference text by comparing n-gram overlaps. A higher BLEU score (e.g., 0.9) indicates better quality. Limitations: (1) It doesn\\u2019t capture semantic meaning (e.g., \\\"big\\\" vs. \\\"large\\\" scores low despite similarity); (2) It favors short outputs; and (3) It\\u2019s less effective for creative tasks like storytelling.\\nWhat is perplexity, and how is it used to evaluate LLMs? \\nAnswer: Perplexity measures how well an LLM predicts a sequence of tokens, calculated as the exponentiated average negative log-likelihood of the test data. Lower perplexity (e.g., 20 vs. 100) indicates better predictive ability. It\\u2019s useful for language modeling but doesn\\u2019t directly measure task-specific performance or human-like quality.\\nWhat are some qualitative methods to evaluate LLMs? \\nAnswer: Qualitative methods include: (1) Human evaluation\\u2014experts assess fluency, coherence, and relevance (e.g., rating chatbot responses); (2) Case studies\\u2014analyzing specific outputs for errors (e.g., factual inaccuracies in GPT-4); (3) User feedback\\u2014collecting end-user satisfaction scores; and (4) Adversarial testing\\u2014probing for biases or harmful outputs (e.g., asking biased questions to detect stereotypes).\\nWhat is the difference between intrinsic and extrinsic evaluation of LLMs? \\nAnswer: Intrinsic evaluation measures the model\\u2019s language understanding directly (e.g., perplexity on a test set or accuracy on a cloze task). Extrinsic evaluation assesses performance on downstream tasks (e.g., F1-score on question-answering with SQuAD). Intrinsic evaluation focuses on the model itself, while extrinsic evaluation measures real-world utility.\\n--\\nEthics and Challenges (Questions 31-35)\\nWhat are the ethical concerns associated with LLMs? \\nAnswer: Concerns include: (1) Bias\\u2014models may perpetuate stereotypes (e.g., gender bias in job-related outputs); (2) Misinformation\\u2014generating false facts (e.g., GPT-3 fabricating historical events); (3) Privacy\\u2014training data may include personal information; (4) Misuse\\u2014enabling deepfakes or phishing; and (5) Environmental impact\\u2014high energy consumption (e.g., GPT-3 training emitted ~552 tons of CO2).\\nHow can bias in LLMs be mitigated? \\nAnswer: Mitigation strategies: (1) Curate diverse training data (e.g., including underrepresented groups); (2) Use debiasing techniques like re-weighting training examples; (3) Fine-tune on fairness-focused datasets; (4) Apply post-processing to outputs (e.g., neutralizing gendered pronouns); and (5) Conduct regular audits for bias using tools like Fairness Indicators.\\nWhat are the environmental impacts of training LLMs, and how can they be reduced? \\nAnswer: Training LLMs consumes significant energy\\u2014e.g., GPT-3 training emitted ~552 tons of CO2, equivalent to 120 cars annually (per 2021 estimates). Reduction strategies: (1) Use energy-efficient hardware (e.g., TPUs); (2) Optimize algorithms (e.g., sparse transformers); (3) Train in regions with renewable energy; (4) Use smaller models or PEFT; and (5) Share pre-trained models to avoid redundant training.\\nHow can LLMs be used responsibly in high-stakes domains like healthcare? \\nAnswer: Responsible use includes: (1) Validating outputs with domain experts (e.g., doctors verifying diagnoses); (2) Ensuring transparency\\u2014disclosing model limitations; (3) Fine-tuning on high-quality medical data (e.g., PubMed); (4) Implementing guardrails to prevent harmful advice; and (5) Complying with regulations like HIPAA for patient data privacy.\\nWhat is the risk of overfitting in LLMs, and how can it be addressed? \\nAnswer: Overfitting occurs when an LLM memorizes training data, leading to poor generalization (e.g., high accuracy on training data but low on test data). Address it by: (1) Using larger, diverse datasets; (2) Applying regularization (e.g., dropout in transformers); (3) Early stopping during training; (4) Data augmentation (e.g., paraphrasing inputs); and (5) Cross-validation.\\n--\\nPractical Applications and Trends (Questions 36-40)\\nWhat are some common applications of LLMs in industry? \\nAnswer: Applications include: (1) Chatbots\\u2014e.g., customer support (like xAI\\u2019s Grok); (2) Content generation\\u2014writing articles or marketing copy; (3) Translation\\u2014e.g., Google Translate using LLMs; (4) Code generation\\u2014e.g., GitHub Copilot; (5) Sentiment analysis\\u2014e.g., analyzing social media; and (6) Question-answering\\u2014e.g., virtual assistants like Alexa.\\nHow are LLMs used in natural language understanding (NLU)? \\nAnswer: LLMs power NLU by: (1) Classifying intents (e.g., BERT identifying \\\"book a flight\\\" as a command); (2) Extracting entities (e.g., recognizing \\\"New York\\\" as a location); (3) Sentiment analysis (e.g., detecting positive\\/negative tone); and (4) Semantic similarity (e.g., comparing sentence meanings). They excel due to their ability to capture context and nuance.\\nWhat is the role of LLMs in code generation? \\nAnswer: LLMs like Codex (powering GitHub Copilot) generate code by learning from vast repositories (e.g., GitHub). Given a prompt like \\\"write a Python function to sort a list,\\\" the model predicts syntactically correct code. They use pre-training on codebases and fine-tuning on specific programming tasks, often leveraging few-shot prompts to improve accuracy.\\nHow can LLMs be used for text summarization? \\nAnswer: LLMs summarize text by: (1) Extractive methods\\u2014selecting key sentences (e.g., BERT-based models); or (2) Abstractive methods\\u2014generating concise summaries in new words (e.g., T5 or BART). For example, summarizing a news article involves encoding the full text, then decoding a shorter version while preserving key information, often evaluated using ROUGE scores.\\nWhat are some emerging trends in LLM development as of 2025? \\nAnswer: Based on trends up to 2023, 2025 likely sees: (1) Multimodal LLMs\\u2014integrating text, images, and video (e.g., CLIP + GPT); (2) Energy-efficient training\\u2014sparse models and quantization; (3) Domain-specific LLMs\\u2014e.g., medical or legal models; (4) Improved ethics\\u2014stronger bias mitigation; and (5) Decentralized training\\u2014collaborative efforts to reduce costs.\\n--\\nAdvanced Topics (Questions 41-45)\\nWhat is the difference between encoder-only, decoder-only, and encoder-decoder LLMs? \\nAnswer: (1) Encoder-only (e.g., BERT): Bidirectional, excels in understanding tasks like classification. (2) Decoder-only (e.g., GPT): Autoregressive, ideal for generation tasks like text completion. (3) Encoder-decoder (e.g., T5, BART): Combines both, suited for tasks like translation or summarization, where input understanding and output generation are distinct.\\nWhat is the role of knowledge distillation in LLMs? \\nAnswer: Knowledge distillation trains a smaller \\\"student\\\" model to mimic a larger \\\"teacher\\\" LLM, reducing size while retaining performance. For example, distilling GPT-3 into a smaller model involves training the student on the teacher\\u2019s soft predictions (logits). It\\u2019s useful for deployment on edge devices with limited resources.\\nHow does retrieval-augmented generation (RAG) enhance LLMs? \\nAnswer: RAG combines LLMs with a retrieval system to fetch relevant external documents before generating responses. For example, when answering \\\"Who won the 2024 Nobel Peace Prize?\\\" RAG retrieves recent articles, then the LLM generates a factual answer. It improves accuracy and reduces hallucination by grounding outputs in real-time data.\\nWhat is the impact of model scaling on LLM performance? \\nAnswer: Scaling (increasing parameters, data, or compute) often improves performance\\u2014e.g., GPT-3 (175B parameters) outperforms GPT-2 (1.5B). The 2020 paper \\\"Scaling Laws for Neural Language Models\\\" shows that performance scales predictably with size, but diminishing returns occur, and larger models increase costs, bias risks, and environmental impact.\\nWhat are sparse transformers, and how do they improve efficiency? \\nAnswer: Sparse transformers reduce the O(n\\u00b2) complexity of self-attention by attending to a subset of tokens (e.g., using locality-sensitive hashing or fixed patterns). For example, BigBird reduces memory usage while maintaining performance on long sequences. They improve efficiency by lowering compute and memory requirements, enabling training on longer inputs.\\n--\\nScenario-Based and Role-Specific (Questions 46-50)\\nHow would you design an LLM for a low-resource language? \\nAnswer: Steps: (1) Collect available data (e.g., web texts, books); (2) Use cross-lingual transfer\\u2014pre-train on a related high-resource language (e.g., Hindi for Marathi); (3) Apply data augmentation (e.g., back-translation); (4) Use a smaller model or PEFT to reduce compute needs; (5) Fine-tune on community-provided data; and (6) Collaborate with native speakers for evaluation.\\nHow would you handle an LLM generating biased outputs in a chatbot? \\nAnswer: (1) Identify the bias (e.g., gender stereotypes in responses); (2) Audit training data for imbalances; (3) Fine-tune on a debiased dataset (e.g., balanced gender representation); (4) Use post-processing to filter biased outputs; (5) Monitor user feedback for recurring issues; and (6) Document and disclose limitations to users.\\nHow would you optimize an LLM for real-time applications like customer support? \\nAnswer: (1) Use a smaller model or distill the LLM (e.g., DistilBERT); (2) Quantize the model to reduce latency (e.g., 8-bit weights); (3) Deploy on efficient hardware (e.g., GPUs or TPUs); (4) Cache frequent responses (e.g., FAQs); (5) Use asynchronous processing for non-urgent queries; and (6) Monitor latency and scale resources dynamically.\\nHow would you explain an LLM\\u2019s decision-making process to a non-technical stakeholder? \\nAnswer: I\\u2019d say: \\\"The LLM is like a super-smart librarian who\\u2019s read millions of books. When you ask a question, it searches its memory for patterns it\\u2019s seen before\\u2014like how words are used together\\u2014and picks the most likely answer. It doesn\\u2019t think like a human but uses math to predict what fits best based on its training. We can guide it with better examples to improve its answers.\\\"\\nHow would you approach building a domain-specific LLM for legal applications? \\nAnswer: (1) Start with a pre-trained LLM (e.g., LLaMA); (2) Collect legal texts (e.g., contracts, case law, statutes); (3) Clean data for quality (e.g., remove sensitive info); (4) Fine-tune using PEFT to save compute; (5) Evaluate on legal tasks (e.g., contract analysis) with metrics like F1-score; (6) Ensure compliance with privacy laws (e.g., GDPR); and (7) Work with legal experts to validate outputs.\\n--\\nSummary and Relevance to 2025\\nThese questions and answers cover the spectrum of LLM knowledge needed for 2025 interviews, reflecting the rapid evolution of AI. The focus on tokenization, transformers, fine-tuning, ethics, and practical applications aligns with industry trends, such as the rise of multimodal models, energy-efficient training, and domain-specific LLMs. The answers incorporate insights from foundational papers (e.g., \\\"Attention is All You Need\\\"), recent developments (e.g., PaLM 2\\u2019s scale), and ethical considerations (e.g., Google\\u2019s responsible AI guidelines), making this a valuable resource for AI job seekers.\",\"sender\":\"AGENT\",\"grokMode\":\"NORMAL\",\"mediaUrls\":[],\"deepsearchHeaders\":[]}],\"destination\":\"destination_1\",\"allowed_actions\":[],\"metadata\":{\"text\":\"Answer by Grok, expand to see full conversation\"}}}},\"destination_objects\":{\"destination_1\":{\"type\":\"browser\",\"data\":{\"url_data\":{\"url\":\"https:\\/\\/x.com\\/i\\/grok\\/share\\/lwnJbVSAByDP9rY0sTE3ANCen\",\"vanity\":\"Grok\"}}}},\"components\":[\"details_1\"]}",
"type": "STRING"
}
},
{
"key": "card_url",
"value": {
"scribe_key": "card_url",
"string_value": "https://t.co/ODVsVACOLt",
"type": "STRING"
}
}
],
"card_platform": {
"platform": {
"audience": {
"name": "production"
},
"device": {
"name": "Android",
"version": "12"
}
}
},
"name": "unified_card",
"url": "https://t.co/ODVsVACOLt",
"user_refs_results": []
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931005165093458325"
],
"editable_until_msecs": "1749226082000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "6563",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 32,
"bookmarked": false,
"created_at": "Fri Jun 06 15:08:02 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
48
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [
{
"display_url": "x.com/i/grok/share/l…",
"expanded_url": "https://x.com/i/grok/share/lwnJbVSAByDP9rY0sTE3ANCen",
"url": "https://t.co/ODVsVACOLt",
"indices": [
25,
48
]
}
],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 22,
"favorited": false,
"full_text": "@omarsar0 Grok version:\n\nhttps://t.co/ODVsVACOLt",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"possibly_sensitive": false,
"possibly_sensitive_editable": true,
"quote_count": 0,
"reply_count": 0,
"retweet_count": 7,
"retweeted": false,
"user_id_str": "1462597328171409410",
"id_str": "1931005165093458325"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931005165093458325"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931023648686546999",
"sortIndex": "1953513795087237080",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931023648686546999-tweet-1931023648686546999",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931023648686546999",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNzkxOTczMDk=",
"rest_id": "179197309",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": false,
"created_at": "Mon Aug 16 19:00:58 +0000 2010",
"default_profile": false,
"default_profile_image": false,
"description": "Valedictorian turned Outlaw ·Thinks Deeply about AI ·Signal Extractor ·No Bandwidth Wasted ·Copy & Speech Expert ·Ex-$KARATE",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "danecurley.com",
"expanded_url": "http://danecurley.com",
"url": "https://t.co/yw3Uifke8G",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 22802,
"followers_count": 1346,
"friends_count": 252,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 28,
"location": "🦆The Meadowlands",
"media_count": 338,
"name": "Dane Curley",
"normal_followers_count": 1346,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/179197309/1694631155",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1931093213781069824/mFCRKe4N_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "DaneCurley",
"statuses_count": 2701,
"translator_type": "none",
"url": "https://t.co/yw3Uifke8G",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1685487971472936960",
"professional_type": "Creator",
"category": [
{
"id": 941,
"name": "Music Producer",
"icon_name": ""
}
]
},
"tipjar_settings": {
"is_enabled": true,
"bandcamp_handle": "https://danecurley.bandcamp.com/",
"bitcoin_handle": "bc1q23rf7msrkmnyuj5v600ym4fy967njdcxrs0dll",
"cash_app_handle": "$DaneCurley",
"ethereum_handle": "0xD52787B985646fd23ba8942e461afCC84C0D21Fb",
"venmo_handle": "Dane-Curley"
}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931023648686546999"
],
"editable_until_msecs": "1749230489000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "4032",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jun 06 16:21:29 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
112
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 9,
"favorited": false,
"full_text": "@omarsar0 And what sources were cited in determining that these are the Top 50 being asked by actual recruiters?",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "179197309",
"id_str": "1931023648686546999"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931023648686546999"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931012586327122196",
"sortIndex": "1953513795087237070",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931012586327122196-tweet-1931012586327122196",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931012586327122196",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjo2ODc5MTky",
"rest_id": "6879192",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": false,
"created_at": "Mon Jun 18 01:10:56 +0000 2007",
"default_profile": false,
"default_profile_image": false,
"description": "Father, husband, Software engineer, ultramarathoner, race car driver, sailor, shooter, Based since 2013",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "linktr.ee/bnewport",
"expanded_url": "https://linktr.ee/bnewport",
"url": "https://t.co/nMvh9mNW5r",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 2067,
"followers_count": 849,
"friends_count": 416,
"has_custom_timelines": false,
"is_translator": false,
"listed_count": 57,
"location": "Ocala, FL",
"media_count": 53,
"name": "Billy",
"normal_followers_count": 849,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/6879192/1668300907",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1908093026335088640/X02btCMe_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "billynewport",
"statuses_count": 3859,
"translator_type": "none",
"url": "https://t.co/nMvh9mNW5r",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1539360828171984897",
"professional_type": "Creator",
"category": [
{
"id": 713,
"name": "Science & Technology",
"icon_name": ""
}
]
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931012586327122196"
],
"editable_until_msecs": "1749227851000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "5327",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/#!/download/ipad\" rel=\"nofollow\">Twitter for iPad</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jun 06 15:37:31 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
56
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 7,
"favorited": false,
"full_text": "@omarsar0 They look an exam for an undergraduate course.",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 1,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "6879192",
"id_str": "1931012586327122196"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931012586327122196"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931092154648281569",
"sortIndex": "1953513795087237060",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931092154648281569-tweet-1931092154648281569",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931092154648281569",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjo4ODQ4MTcyMDY=",
"rest_id": "884817206",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Tue Oct 16 16:55:12 +0000 2012",
"default_profile": false,
"default_profile_image": false,
"description": "Programmer | Effective accelerationist (e/acc) | History and language aficionado | Proponent of transhumanism 🐷",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "medium.com/@hathibel",
"expanded_url": "https://medium.com/@hathibel",
"url": "https://t.co/MVsYzG8FNs",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 2407,
"followers_count": 1787,
"friends_count": 16,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 51,
"location": "",
"media_count": 1683,
"name": "Ashraff Hathibelagal",
"normal_followers_count": 1787,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/884817206/1686005069",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1665855497012928515/GeKIIO23_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "Hathibel",
"statuses_count": 3881,
"translator_type": "none",
"url": "https://t.co/MVsYzG8FNs",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1605225264350371845",
"professional_type": "Creator",
"category": [
{
"id": 1042,
"name": "Content Creator",
"icon_name": ""
}
]
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931092154648281569"
],
"editable_until_msecs": "1749246822000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "2523",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jun 06 20:53:42 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
112
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 Why would anyone want to join a company that asks such questions? They might as well just hire chatgpt",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "884817206",
"id_str": "1931092154648281569"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931092154648281569"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931249185065459855",
"sortIndex": "1953513795087237050",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931249185065459855-tweet-1931249185065459855",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931249185065459855",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxMzQ3MjUyMzk=",
"rest_id": "134725239",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": false,
"created_at": "Mon Apr 19 07:58:27 +0000 2010",
"default_profile": false,
"default_profile_image": false,
"description": "",
"entities": {
"description": {
"urls": []
}
},
"fast_followers_count": 0,
"favourites_count": 5597,
"followers_count": 153,
"friends_count": 2228,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 0,
"location": "",
"media_count": 8,
"name": "mlds9",
"normal_followers_count": 153,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/134725239/1744546927",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1603149855861374976/Qz-pCVP-_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "mldsqc",
"statuses_count": 390,
"translator_type": "none",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931249185065459855"
],
"editable_until_msecs": "1749284261000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "1172",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Sat Jun 07 07:17:41 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
11
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 😂",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "qme",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "134725239",
"id_str": "1931249185065459855"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931249185065459855"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931305579286556747",
"sortIndex": "1953513795087237040",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931305579286556747-tweet-1931305579286556747",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931305579286556747",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNDM1MDIzOTU=",
"rest_id": "143502395",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Thu May 13 17:00:17 +0000 2010",
"default_profile": true,
"default_profile_image": false,
"description": "Physics & CS @ Stanford | Prev @ TikTok | Currently @buddybot_fun\n\nContact: yumich@stanford.edu\n\nhttps://t.co/tipBsPIfH4",
"entities": {
"description": {
"urls": [
{
"display_url": "linkedin.com/in/michaelyu7",
"expanded_url": "https://www.linkedin.com/in/michaelyu7",
"url": "https://t.co/tipBsPIfH4",
"indices": [
97,
120
]
}
]
},
"url": {
"urls": [
{
"display_url": "github.com/amichaelyu",
"expanded_url": "http://github.com/amichaelyu",
"url": "https://t.co/mo0d6Fkq4K",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 241,
"followers_count": 753,
"friends_count": 447,
"has_custom_timelines": false,
"is_translator": false,
"listed_count": 1,
"location": "CA",
"media_count": 9,
"name": "Michael 'Coetzee' Yu",
"normal_followers_count": 753,
"pinned_tweet_ids_str": [
"1931298899031195658"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/143502395/1749290585",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1931292063628476416/4YEz06b1_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "miquecoetzee",
"statuses_count": 142,
"translator_type": "none",
"url": "https://t.co/mo0d6Fkq4K",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1931290111930736769",
"professional_type": "Creator",
"category": [
{
"id": 713,
"name": "Science & Technology",
"icon_name": ""
}
]
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931305579286556747"
],
"editable_until_msecs": "1749297706000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "137",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Sat Jun 07 11:01:46 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
56
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 2,
"favorited": false,
"full_text": "@omarsar0 They look an exam for an undergraduate course.",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "143502395",
"id_str": "1931305579286556747"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931305579286556747"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931157052446884247",
"sortIndex": "1953513795087237030",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931157052446884247-tweet-1931157052446884247",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931157052446884247",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjo4ODY4NDky",
"rest_id": "8868492",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Fri Sep 14 04:15:34 +0000 2007",
"default_profile": false,
"default_profile_image": true,
"description": "一个纯粹的人",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "about.cn",
"expanded_url": "http://about.cn",
"url": "http://t.co/H8kBIu5vVG",
"indices": [
0,
22
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 1710,
"followers_count": 31,
"friends_count": 233,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 1,
"location": "北京",
"media_count": 0,
"name": "木材",
"normal_followers_count": 31,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_image_url_https": "https://abs.twimg.com/sticky/default_profile_images/default_profile_normal.png",
"profile_interstitial_type": "",
"screen_name": "fnxtdm",
"statuses_count": 219,
"translator_type": "none",
"url": "http://t.co/H8kBIu5vVG",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931157052446884247"
],
"editable_until_msecs": "1749262295000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "1534",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Sat Jun 07 01:11:35 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
32
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
},
{
"id_str": "920321515077414912",
"name": "Readwise",
"screen_name": "readwise",
"indices": [
10,
19
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 @readwise save thread",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 1,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "8868492",
"id_str": "1931157052446884247"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931157052446884247"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1930988017734357247",
"sortIndex": "1953513795087237020",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1930988017734357247-tweet-1930988017734357247",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1930988017734357247",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNzMzODY3MzU4MjA3NDk2MTky",
"rest_id": "1733867358207496192",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": true,
"created_at": "Sun Dec 10 15:13:07 +0000 2023",
"default_profile": true,
"default_profile_image": false,
"description": "Certified Monk 🛕 → Merging Cosmic Wisdom 🌌 with AI/ML 🤖 & Full-Stack Solutions 👨🏼💻 | Using Neovim BTW ❤️🔥 | My Blessings 🙏🏼 | View more 🫠",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "3-d-portfolio-nine-mu.vercel.app",
"expanded_url": "https://3-d-portfolio-nine-mu.vercel.app/",
"url": "https://t.co/tqBtbjgqD2",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 287,
"followers_count": 245,
"friends_count": 271,
"has_custom_timelines": false,
"is_translator": false,
"listed_count": 0,
"location": "🪷 Remote ",
"media_count": 251,
"name": "Engineer Monk",
"normal_followers_count": 245,
"pinned_tweet_ids_str": [
"1917395008774148482"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/1733867358207496192/1743054640",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1907773035391889409/0Ecwq4m0_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "AshuMishraG1",
"statuses_count": 1904,
"translator_type": "none",
"url": "https://t.co/tqBtbjgqD2",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1852101456972910639",
"professional_type": "Creator",
"category": [
{
"id": 1055,
"name": "Software developer/Programmer/Software engineer",
"icon_name": ""
}
]
},
"tipjar_settings": {
"is_enabled": true
}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1930988017734357247"
],
"editable_until_msecs": "1749221994000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "2549",
"state": "EnabledWithCount"
},
"source": "<a href=\"https://mobile.twitter.com\" rel=\"nofollow\">Twitter Web App</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jun 06 13:59:54 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
14
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 Nice",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "1733867358207496192",
"id_str": "1930988017734357247"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1930988017734357247"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931298256182550824",
"sortIndex": "1953513795087237010",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931298256182550824-tweet-1931298256182550824",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931298256182550824",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNzYyMDE2NjUwMjE4MzA3NTg0",
"rest_id": "1762016650218307584",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Mon Feb 26 07:28:22 +0000 2024",
"default_profile": true,
"default_profile_image": false,
"description": "All about AI and Co.\n#Alles_rund_um_KI_und_Co",
"entities": {
"description": {
"urls": []
}
},
"fast_followers_count": 0,
"favourites_count": 149,
"followers_count": 16,
"friends_count": 373,
"has_custom_timelines": false,
"is_translator": false,
"listed_count": 0,
"location": "Germany",
"media_count": 5,
"name": "Quantum Quill",
"normal_followers_count": 16,
"pinned_tweet_ids_str": [],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/1762016650218307584/1710489803",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1905395609982337024/j0Ev54ss_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "QQ200224",
"statuses_count": 47,
"translator_type": "none",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"tipjar_settings": {}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931298256182550824"
],
"editable_until_msecs": "1749295960000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "620",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Sat Jun 07 10:32:40 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
19
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 Thank you",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "1762016650218307584",
"id_str": "1931298256182550824"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931298256182550824"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931066772234359175",
"sortIndex": "1953513795087237000",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931066772234359175-tweet-1931066772234359175",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1931066772234359175",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjoxNzU4Nzc3ODA1NTc4Mzc1MTY4",
"rest_id": "1758777805578375168",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": true,
"can_media_tag": false,
"created_at": "Sat Feb 17 08:58:15 +0000 2024",
"default_profile": true,
"default_profile_image": false,
"description": "@dev3pack fellow | DataScience |",
"entities": {
"description": {
"urls": []
}
},
"fast_followers_count": 0,
"favourites_count": 4234,
"followers_count": 429,
"friends_count": 1755,
"has_custom_timelines": false,
"is_translator": false,
"listed_count": 0,
"location": "",
"media_count": 82,
"name": "Essie",
"normal_followers_count": 429,
"pinned_tweet_ids_str": [
"1951176029608755432"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/1758777805578375168/1751225444",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1952367356472139776/AxXVjAMw_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "Essie__bx",
"statuses_count": 1289,
"translator_type": "none",
"verified": false,
"want_retweets": false,
"withheld_in_countries": []
},
"professional": {
"rest_id": "1761790672128843990",
"professional_type": "Creator",
"category": [
{
"id": 713,
"name": "Science & Technology",
"icon_name": ""
}
]
},
"tipjar_settings": {
"is_enabled": true
}
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1931066772234359175"
],
"editable_until_msecs": "1749240770000",
"is_edit_eligible": false,
"edits_remaining": "5"
},
"is_translatable": false,
"views": {
"count": "1587",
"state": "EnabledWithCount"
},
"source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
"legacy": {
"bookmark_count": 0,
"bookmarked": false,
"created_at": "Fri Jun 06 19:12:50 +0000 2025",
"conversation_id_str": "1930984834454712537",
"display_text_range": [
10,
34
],
"entities": {
"hashtags": [],
"symbols": [],
"timestamps": [],
"urls": [],
"user_mentions": [
{
"id_str": "3448284313",
"name": "elvis",
"screen_name": "omarsar0",
"indices": [
0,
9
]
}
]
},
"favorite_count": 0,
"favorited": false,
"full_text": "@omarsar0 More if this please 🙌🙌🙌🙌",
"in_reply_to_screen_name": "omarsar0",
"in_reply_to_status_id_str": "1930984834454712537",
"in_reply_to_user_id_str": "3448284313",
"is_quote_status": false,
"lang": "en",
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"retweeted": false,
"user_id_str": "1758777805578375168",
"id_str": "1931066772234359175"
},
"quick_promote_eligibility": {
"eligibility": "IneligibleNotProfessional"
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931066772234359175"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "conversationthread-1931302805677175231",
"sortIndex": "1953513795087236990",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "conversationthread-1931302805677175231-tweet-1931302805677175231",
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "TweetTombstone",
"tombstone": {
"__typename": "TextTombstone",
"text": {
"rtl": false,
"text": "This Post was deleted by the Post author. Learn more",
"entities": [
{
"fromIndex": 42,
"toIndex": 52,
"ref": {
"type": "TimelineUrl",
"url": "https://help.twitter.com/rules-and-policies/notices-on-twitter",
"urlType": "ExternalUrl"
}
}
]
}
}
}
},
"tweetDisplayType": "Tweet"
},
"clientEventInfo": {
"component": "tweet",
"element": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
}
],
"metadata": {
"conversationMetadata": {
"allTweetIds": [
"1931302805677175231"
],
"enableDeduplication": true
}
},
"displayType": "VerticalConversation",
"clientEventInfo": {
"component": "tweet",
"details": {
"conversationDetails": {
"conversationSection": "HighQuality"
},
"timelinesDetails": {
"controllerData": "DAACDAAEDAABCgABAAAAAAAAAAEKAAIAAAAAAAAAAAAAAAA="
}
}
}
}
},
{
"entryId": "cursor-bottom-1953513795087236989",
"sortIndex": "1953513795087236989",
"content": {
"entryType": "TimelineTimelineCursor",
"__typename": "TimelineTimelineCursor",
"value": "DAAFCgABGxxGckv__3sLAAIAAACwRW1QQzZ3QUFBZlEvZ0dKTjB2R3AvQUFBQUE0YXpTem0wSmJnanhyTllERWVHdkJMR3N3OGVlUlc4Tmthek5rYmhKWkJseHJNWDhjSTF5QTNHczFkcTFZYWNiOGF6RlczWU5ZaEZCck1oditFbHRHSEdzeE85M3pYRVpVYXpEOWZEcGVRL3hyTW5oVlNXc0hoR3MxWmlCTVhvU2dhekhJOHE5dWdOQnJNUEhxTDE5SFcIAAMAAAACAAA",
"cursorType": "Bottom"
}
}
]
},
{
"type": "TimelineTerminateTimeline",
"direction": "Top"
},
{
"type": "TimelineTerminateTimeline",
"direction": "Bottom"
}
],
"metadata": {
"reader_mode_config": {
"is_reader_mode_available": true
},
"scribeConfig": {
"page": "ranked_replies"
}
}
}
}
}