Model-agnostic output control for LLM agents: concise, action-first answers with proof and risk preserved. Native install paths for Claude Code and Codex.
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Updated
Oct 25, 2026 - Python
Model-agnostic output control for LLM agents: concise, action-first answers with proof and risk preserved. Native install paths for Claude Code and Codex.
Neural-network surrogates of WaterTAP water-treatment flowsheets (seawater RO with pressure exchanger, BSM2 activated sludge), trained and optimized with OpenNN, with a browser control-room demo that runs the networks next to a live WaterTAP simulator
Self-hosted multi-agent AI platform: generates, evaluates and refines LLM answers until they hit a quality bar (~22% more accurate, ~45% fewer hallucinations)
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