ThisOrThis.ai is an innovative platform designed to simplify the comparison of AI-generated responses. Users can input prompts and generate outputs from multiple AI models, enabling side-by-side evaluations. This unique feature fosters community-driven feedback and collaborative decision-making by allowing users to share their findings and vote on the preferred responses. It’s perfectly suited for AI enthusiasts, developers, and researchers looking to understand the nuances and capabilities of different AI models.

AgenticIR (Agentic Information Retrieval) provides a modular framework where LLM-powered agents autonomously plan and execute IR workflows. It enables the definition of agent roles — such as query generator, document retriever, and summarizer — running in customizable sequences. Agents can fetch raw text, refine queries based on intermediate results, and merge extracted passages into concise summaries. The framework supports multi-step pipelines including iterative web search, API-based data ingestion, and local document parsing. Developers can adjust agent parameters, plug in different LLMs, and fine-tune behavior policies. AgenticIR also offers logging, error handling, and parallel agent execution to accelerate large-scale information gathering. With a minimal code setup, researchers and engineers can prototype and deploy autonomous retrieval systems.