What is drip.new?
drip.new revolutionizes data labeling for Large Language Models (LLMs) by integrating human feedback directly into free-to-play games and apps. Instead of traditional rewarded ads, users complete short labeling tasks—such as ranking responses, evaluating text quality, or providing preferences—to earn in-game rewards. This approach generates massive, diverse, and high-quality training data at scale, addressing the bottleneck of RLHF (Reinforcement Learning from Human Feedback) data collection.
Key features include seamless integration with existing mobile games and apps, real-time data collection, and a gamified interface that ensures high user engagement. The system supports various labeling tasks, from binary preferences to multi-label classification, and automatically filters low-quality responses. Benefits include cost-effective data acquisition compared to manual labeling, access to a global user base, and continuous data streams that adapt to evolving model needs.
Use cases span from training reward models for RLHF to fine-tuning LLMs for specific domains like customer support or content moderation. Developers can leverage drip.new to quickly iterate on model alignment without relying on expensive human annotators. Technical details include an API for task submission and data retrieval, support for custom task templates, and built-in quality control mechanisms like inter-annotator agreement checks. The platform handles millions of daily interactions, ensuring low latency and high throughput.
By turning idle screen time into productive data generation, drip.new offers a scalable, ethical, and engaging solution for LLM training. It reduces the reliance on synthetic data or costly expert annotators, providing real human insights at a fraction of the cost. Whether you are a startup or a large AI lab, drip.new accelerates your RLHF pipeline while keeping users entertained.
Use Cases
Key Features
gamified data labeling human feedback integration real time data collection multi task labeling support low quality response filtering cost effective data acquisition global user base access continuous data streamsWho is it for?
LLM developers, AI researchers, data scientists, product teams, mobile game developers, customer support teams, content moderation teams
