The AI Writing Contest, powered by Bright Data and HackerNoon, taps into the global AI revolution. It invites developers, data scientists, researchers, writers, and everyone interested in AI and Large Language Models (LLMs) to share their insights for a chance to win up to $2,500 in prizes. Share your story with the #ai tag to enter. 

For this contest, we're seeking innovative approaches to AI and LLM training. Explore how Bright Data's tools can be leveraged to unlock diverse and high-quality datasets for AI training, and demonstrate the real-world applications of this data in AI and LLM development.

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Get Started with Bright Data: To help you prepare for the contest, Bright Data is offering $15 in credit for you to test out its tools. Use this credit to explore:

Web Data for AI
Dataset Marketplace

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Participants will have access to free datasets, highlighted on the platform. For specific dataset requests, contact hackernoon@brightdata.com, and the data will be provided to your account within 24 hours.

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Prompts for the AI Writing Contest 

Harnessing Bright Data for AI Training

• Demonstrate how you've used Bright Data to collect web data for AI, and provide a detailed analysis of the tasks performed and their results.

• Explore how Bright Data's tools can be leveraged for improved AI and LLM training.

• Discuss how Bright Data’s Marketplace and Scraper APIs help collect high-quality data for AI training.  

• Share examples of how Bright Data’s tools make data collection easier and more effective.

• Discuss how Bright Data's APIs and Marketplace provide sources for text, video, images, audio, and other dataset types needed for AI model training, and how they can be used for various AI tasks.

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The Future of Data Collection for AI and LLMs

• Discuss new trends in AI and LLM data collection and their potential impact on current practices. 

• Explore how diverse datasets impact the performance of AI and LLMs.

• Examine techniques for optimizing data pipelines to enhance the efficiency and effectiveness of training large language models.

• Highlight innovative approaches to data preprocessing, integration, and management that can accelerate model training and improve performance.

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Data Labeling/Annotation for AI Model Training Using Public Web Dat

• Demonstrate how public web data can be labeled for AI training, with examples of how labeled data improved AI performance in specific applications.

• Explore how high-quality, labeled web data affects AI model accuracy, particularly in NLP, image recognition, or recommendation systems.

• Explain how a pre-labeled dataset can be reused for different AI tasks by applying various labeling schemes, without creating new datasets for each use case

Web Scraping for AI and LLM Data Collection

• Highlight web scraping tools that could transform AI training data. 

• Provide examples of real-world applications where data collected at scale through web scraping has been used to train successful AI models.

• Discuss cutting-edge scraping technologies or "hacks" that most people don't know about, which can save time and resources.

• Discover and discuss hidden or unconventional data sources on the web that can be scraped for training AI models.

• Share your experiences and strategies for overcoming common challenges in collecting data at scale through web scraping for AI and LLMs.

• Share success stories or "David vs. Goliath" scenarios where small teams outperformed industry giants using innovative web scraping techniques. Add a personal touch by sharing your own journey and surprising lessons learned​