Presents Metis, a prototype memory foundation model that embeds a persistent native memory state into the backbone so historical experience is compressed and accessed via memory attention. Key features: forward-only, gradient-free online memory updates; memory-specific mid-training objectives; and a dual text/code memory design.
Indexes chemistry literature as provenance-bearing atomic claims and provides a faceted taxonomy, evidence graph, and REST/SDK/MCP APIs so researchers and AI agents can retrieve verifiable, claim-level findings across papers; live index contains 2.4M claims from 147K papers.
Autonomously proposes, modifies, executes, and evaluates ML experiments to study recursive self-improvement in machine learning engineering. Implements an open stack (OpenMLE-Gym, -RL, -Evo) and post-trains Frontis-MA1 (35B) around four evolution operators (Draft, Improve, Debug, Crossover); releases model weights and the full codebase.
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.