#phm#ryland grace#rocky the eridian#project hail mary spoilers
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Texting, captions, video calls. Deaf communities used them before the rest of the world caught up. Sign language is next, and this time peop
Companies That Provide Data to AI Firms
follow up post to: thru Gnip the Tumblr @staff sold our posts to the highest bidders
Key players in social media/web data provision; many partner with or sell to AI developers for training datasets, sentiment analysis, or real-time intelligence
DataSift (now part of Meltwater or evolved): A major Gnip competitor that aggregated and filtered social data from multiple sources for enterprise use. firstmonday.org
Brandwatch (acquired by Cision): Social listening and analytics platform with strong data access; previously integrated directly with Gnip for full Twitter firehose access. prnewswire.com
Dataminr: Real-time social/media intelligence, often using public data streams for alerts and AI-powered insights. cbinsights.com
Bright Data (formerly Luminati): Provides large-scale web and social media data scraping/collection services, including structured datasets from platforms like Facebook, Instagram, etc., popular for AI training. brightdata.com
Other social listening/monitoring firms that supply or analyze data usable for AI:
Sprout Social
Hootsuite
Meltwater
Talkwalker
Crimson Hexagon
Broader AI Training Data Providers
Beyond pure social media firehoses, many companies supply curated, annotated, or scraped data (including social/web content) specifically for AI model training:
Scale AI: High-quality labeled data for training, including from various sources.
Appen, Labelbox, iMerit: Data annotation and collection services.
Defined.ai, Nexdata, others specializing in datasets. datarade.ai
Note: Many social platforms now restrict bulk data access due to AI scraping concerns (e.g., Twitter/X, Reddit licensing deals, Meta changes). AI companies often rely on licensed partnerships, public APIs (with limits), or compliant providers rather than open firehoses. Some platforms (like Reddit or X) directly license data to AI firms.
techpolicy.press If you're looking for providers for a specific platform (e.g., X/Twitter, Instagram), use case (training vs. real-time monitoring), or region, provide more details for tailored recommendations. Always check compliance with platform terms and privacy laws (GDPR, etc.).
see also:
Tumblr @Staff Finally Breaks Silence: “Yes, We Sold Your Unhinged Blogs… And We’d Do It Again”
hellsite tumblr is to blame for chatgpt dysfunctionality
Current digital toolbox upgrade: iphtml.com 🌐
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I will do data extraction, web scraper, yellow pages scraping, amazon scraping
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For only $40, Sojib572 will do data extraction, web scraper, yellow pages scraping, amazon scraping. | Hi,Are you looking for someone who ca
They are the real parasites. So, what's the medicine?
2026 güncel Boolean Search rehberi ile AND, OR, NOT operatörlerini ve LinkedIn X-Ray arama tekniklerini öğrenin. İK ve Finans profesyonelleri için veri madenciliği ipuçları.
I don’t like how people data-mined Tomodatchi life living the dream before release date. If you’ve been waiting for a game for so long and it FINALLY gets announced, if you really cared, you’d respect its release date. The creator’s decided that, that date is when people should be allowed to play, then people should respect that. Like, I fully understand being like “I can’t wait for the game to come out uggghhh” I’m like that too, but I love the game and respect their release day choice, yet others can’t. Feel free to talk about how you feel, I just really dislike how people data-mined it to play early. Idk if this sounds like normal like at all or is really badly worded but hopefully my opinion still gets across
It is predicted that crops like watermelon will be decreased in coming years and price of this product in the market will increase. This highlights the necessity of measures to choose high-quality watermelons by the final consumer. Also according to the concept of virtual water, sorted and more desirable watermelons can be exported at higher prices in order to create more benefit. In general, the aim of this study is to provide a measure that is based on Charleston Gray watermelon morphological characteristics and evaluation of the classification ratio in unripe, ripped and overripe classes, by data mining algorithms. The results of the sensory evaluation showed that experts (human) were able to classify 52% of the samples correctly. The correct classification algorithm K Nearest Neighbor was significantly higher than the classification of LVQ Neural Networks and Discriminant Analysis but classification results of different distance metrics of this algorithm showed no significant difference using them. The highest correct classification with the amount of 67.3 percent belonged to Support Vector Machine algorithm with Gaussian kernel function. Although at first glance it may seem that, this amount is far from ideal but it should be noted that this amount is 15% higher than the classification made by humans. Incidentally, this classification was done based on morphological characteristics of samples which measuring them does not require sophisticated tools and methods.
