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  • AI-driven platform for video creation and human feedback.
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    What is VidINsight?
    VidInsight offers a streamlined video creation process by combining AI-generated storyboards with real human feedback. This dual approach ensures that videos are not only creatively produced but are also optimized for emotional and attentional impact on audiences. By leveraging advanced AI technology, VidInsight makes it possible to quickly generate video previews and test them on a human-based panel, ensuring effective and engaging content.
  • An open-source autonomous AI agent framework executing tasks, integrating tools like browser and terminal, and memory through human feedback.
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    What is SuperPilot?
    SuperPilot is an autonomous AI agent framework that leverages large language models to perform multi-step tasks without manual intervention. By integrating GPT and Anthropic models, it can generate plans, call external tools such as a headless browser for web scraping, a terminal for executing shell commands, and memory modules for context retention. Users define goals, and SuperPilot dynamically orchestrates sub-tasks, maintains a task queue, and adapts to new information. The modular architecture allows adding custom tools, adjusting model settings, and logging interactions. With built-in feedback loops, human input can refine decision-making and improve results. This makes SuperPilot suitable for automating research, coding tasks, testing, and routine data processing workflows.
  • Text-to-Reward learns general reward models from natural language instructions to effectively guide RL agents.
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    What is Text-to-Reward?
    Text-to-Reward provides a pipeline to train reward models that map text-based task descriptions or feedback into scalar reward values for RL agents. Leveraging transformer-based architectures and fine-tuning on collected human preference data, the framework automatically learns to interpret natural language instructions as reward signals. Users can define arbitrary tasks via text prompts, train the model, and then incorporate the learned reward function into any RL algorithm. This approach eliminates manual reward shaping, boosts sample efficiency, and enables agents to follow complex multi-step instructions in simulated or real-world environments.
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