Proposes Recuris, a recursive Experiential-Working Memory architecture that separates Working Memory (task progress) from Experiential Memory (skills) and uses a Meta-Agent to validation-gate localized skill updates, enabling bounded recursive skill evolution for long-horizon agents.
Synthesizes, repairs, and self-evolves task-adaptive agent harnesses on demand for off-the-shelf LLM agents, using a trainable harness-intelligence model that distills signals from past configurations. Demonstrates consistent performance gains across benchmarks and model families by producing four-module, composable harnesses.
Turns plain-language prompts into working websites, web apps, and mobile apps in the browser. Chat-driven code generation, live preview, hosting, databases, and GitHub/Figma imports help builders move from idea to shipped project without local setup.
Turns plain-language app ideas into working software inside a browser workspace, then lets users preview, debug, and deploy without leaving the platform.
Memory layer that lets AI agents remember users and context across sessions.