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"apidojo/twitter-scraper-lite": 50, "xquik/x-tweet-scraper": 50, "danek/twitter-scraper": 55, "scrape.badger/twitter-tweets-scraper": 32 }, "scrape.badger/twitter-tweets-scraper": { "apidojo/tweet-scraper": 32, "kaitoeasyapi/twitter-x-data-tweet-scraper-pay-per-result-cheapest": 49, "apidojo/twitter-scraper-lite": 32, "xquik/x-tweet-scraper": 32, "danek/twitter-scraper": 32, "scrape.badger/twitter-tweets-scraper": 50 } }, "metric_agreement": { "views": { "ids": 71, "identical": 60 }, "likes": { "ids": 71, "identical": 60 }, "reposts": { "ids": 71, "identical": 71 }, "replies": { "ids": 71, "identical": 64 } }, "longform_capture": { "apidojo/tweet-scraper": { "long_ids_seen": 5, "full_text": 0, "pct": 0.0 }, "kaitoeasyapi/twitter-x-data-tweet-scraper-pay-per-result-cheapest": { "long_ids_seen": 0, "full_text": 0, "pct": null }, "apidojo/twitter-scraper-lite": { "long_ids_seen": 5, "full_text": 0, "pct": 0.0 }, "xquik/x-tweet-scraper": { "long_ids_seen": 5, "full_text": 5, "pct": 100.0 }, "danek/twitter-scraper": { "long_ids_seen": 5, "full_text": 0, "pct": 0.0 }, "scrape.badger/twitter-tweets-scraper": { "long_ids_seen": 0, "full_text": 0, "pct": null } } } }, "lookups": { "lookup_media": { "apidojo/tweet-scraper": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53572, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": null, "text_len": 238, "n_items": 1, "filler": 0, "media_n": 1 }, "kaitoeasyapi/twitter-x-data-tweet-scraper-pay-per-result-cheapest": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53572, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": null, "text_len": 238, "n_items": 15, "filler": 14, "media_n": 1 }, "apidojo/twitter-scraper-lite": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53572, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": null, "text_len": 238, "n_items": 1, "filler": 0, "media_n": 1 }, "xquik/x-tweet-scraper": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53572, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": false, "text_len": 238, "n_items": 1, "filler": 0, "media_n": 1 }, "danek/twitter-scraper": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53572, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": null, "text_len": 238, "n_items": 1, "filler": 0, "media_n": 1 }, "scrape.badger/twitter-tweets-scraper": { "id": "2095479050911309827", "text": "Apify is now available in Hermes Agent by @NousResearch πŸŽ‰\n\nπš™πš’πš™ πš’πš—πšœπšπšŠπš•πš• πšŠπš™πš’πšπš’-πš‘πšŽπš›πš–πšŽπšœ-πšŠπšπšŽπš—πš-πš™πš•πšžπšπš’πš—\nπš‘πšŽπš›πš–πšŽπšœ πš™πš•πšžπšπš’πš—πšœ πšŽπš—πšŠπš‹πš•πšŽ πšŠπš™πš’πšπš’\n\nThe largest marketplace of tools for AI, now inside your AI assistant.\nLinks in thread ↓ https://t.co/EsT1pXO32E", "likes": 312, "reposts": 27, "replies": 8, "quotes": 2, "views": 53573, "bookmarks": 275, "created_at": "Thu Sep 03 11:48:32 +0000 2026", "lang": "en", "author_username": "apify", "is_long": null, "text_len": 238, "n_items": 1, "filler": 0, "media_n": 1 } }, "lookup_note": { "apidojo/tweet-scraper": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": null, "text_len": 277, "n_items": 1, "filler": 0, "media_n": 0 }, "kaitoeasyapi/twitter-x-data-tweet-scraper-pay-per-result-cheapest": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products and the LLM call costs half a cent per page, the extraction run costs fifty dollars. Run it daily and the annual cost is eighteen thousand dollars for one data source.\n\nSelf-healing CSS selectors change the economics. The first run uses an LLM to identify the right selectors for each field. Subsequent runs use the selectors directly and only trigger LLM inference again when a selector breaks because the page structure changed. For a stable site, the LLM runs once. For a site that redesigns quarterly, it runs four times a year per field.\n\nThe MCP server layer matters because it means the scraper is callable as a tool from inside an agent loop, not just as a standalone script. An agent that needs price data can call DeepScrape as a tool, get structured output back, and continue without leaving the agentic context.\n\nFirecrawl, Apify, and Browserless each solve a piece of this. None of them combine self-healing selectors with an MCP interface in a single open-source package you can run yourself.\n\nhttps://t.co/JZYTWY8roq\n\nhttps://t.co/tIrnvH5vb2", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": null, "text_len": 1334, "n_items": 15, "filler": 14, "media_n": null }, "apidojo/twitter-scraper-lite": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": null, "text_len": 277, "n_items": 1, "filler": 0, "media_n": 0 }, "xquik/x-tweet-scraper": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products and the LLM call costs half a cent per page, the extraction run costs fifty dollars. Run it daily and the annual cost is eighteen thousand dollars for one data source.\n\nSelf-healing CSS selectors change the economics. The first run uses an LLM to identify the right selectors for each field. Subsequent runs use the selectors directly and only trigger LLM inference again when a selector breaks because the page structure changed. For a stable site, the LLM runs once. For a site that redesigns quarterly, it runs four times a year per field.\n\nThe MCP server layer matters because it means the scraper is callable as a tool from inside an agent loop, not just as a standalone script. An agent that needs price data can call DeepScrape as a tool, get structured output back, and continue without leaving the agentic context.\n\nFirecrawl, Apify, and Browserless each solve a piece of this. None of them combine self-healing selectors with an MCP interface in a single open-source package you can run yourself.\n\nhttps://t.co/JZYTWY8roq\n\nhttps://t.co/tIrnvH5vb2", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": true, "text_len": 1334, "n_items": 1, "filler": 0, "media_n": 0 }, "danek/twitter-scraper": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products and the LLM call costs half a cent per page, the extraction run costs fifty dollars. Run it daily and the annual cost is eighteen thousand dollars for one data source.\n\nSelf-healing CSS selectors change the economics. The first run uses an LLM to identify the right selectors for each field. Subsequent runs use the selectors directly and only trigger LLM inference again when a selector breaks because the page structure changed. For a stable site, the LLM runs once. For a site that redesigns quarterly, it runs four times a year per field.\n\nThe MCP server layer matters because it means the scraper is callable as a tool from inside an agent loop, not just as a standalone script. An agent that needs price data can call DeepScrape as a tool, get structured output back, and continue without leaving the agentic context.\n\nFirecrawl, Apify, and Browserless each solve a piece of this. None of them combine self-healing selectors with an MCP interface in a single open-source package you can run yourself.\n\nhttps://t.co/JZYTWY8roq\n\nhttps://t.co/tIrnvH5vb2", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": null, "text_len": 1334, "n_items": 1, "filler": 0, "media_n": null }, "scrape.badger/twitter-tweets-scraper": { "id": "2096838373524812149", "text": "The cost problem in web scraping for AI agents is not network latency. It is LLM inference on every fetch.\n\nA naive agent that scrapes a product catalog runs an LLM over the raw HTML for every page it fetches to extract structured data. If the catalog has ten thousand products and the LLM call costs half a cent per page, the extraction run costs fifty dollars. Run it daily and the annual cost is eighteen thousand dollars for one data source.\n\nSelf-healing CSS selectors change the economics. The first run uses an LLM to identify the right selectors for each field. Subsequent runs use the selectors directly and only trigger LLM inference again when a selector breaks because the page structure changed. For a stable site, the LLM runs once. For a site that redesigns quarterly, it runs four times a year per field.\n\nThe MCP server layer matters because it means the scraper is callable as a tool from inside an agent loop, not just as a standalone script. An agent that needs price data can call DeepScrape as a tool, get structured output back, and continue without leaving the agentic context.\n\nFirecrawl, Apify, and Browserless each solve a piece of this. None of them combine self-healing selectors with an MCP interface in a single open-source package you can run yourself.\n\nhttps://t.co/JZYTWY8roq\n\nhttps://t.co/tIrnvH5vb2", "likes": 5, "reposts": 2, "replies": 6, "quotes": 0, "views": 433, "bookmarks": 1, "created_at": "Mon Sep 07 05:50:00 +0000 2026", "lang": "en", "author_username": "stretchcloud", "is_long": null, "text_len": 1334, "n_items": 1, "filler": 0, "media_n": 0 } } } }