🏛️ AI team debates in your terminal. Multiple expert personas discuss, challenge each other, and reach a decision.
Multi-agent debate framework beats single-AI answers, but scaling multi-turn prompts isn't novel.
Engineers, architects, and product teams making technical decisions in a terminal environment
Claude API multi-turn conversations · DSPy (Stanford multi-agent frameworks) · LangChain agent orchestration
Turns product testing into a Reddit-like sandbox: spawn opinionated AI personas, run threaded chats and collect 'insights' before you go public. The UI hints at practical workflows (Input / Personas / Chat / Insights and 'real data input now supported'), but the product's usefulness will hinge on persona fidelity, dataset provenance, and how it handles bias and edge cases.
Fun Gilfoyle persona, but it's prompt templates with install scripts.
Debate mode where models change minds is novel, but model comparison tools already exist.
Everything runs locally — upload CSV/XLSX/JSON, map columns, set multi-condition rules and run simulations across millions of rows using Web Workers/streaming, then inspect timeline, distribution and sensitivity curves. It’s a focused, privacy-first toolbox for quieting alert noise; the UX around column mapping, paste/import from Sheets, and exportable reports feels thoughtfully done, though saving profiles behind a $12/mo wall is a pragmatic but annoying limitation.
The five-role council (Analyst, Muse, Logician, Ethicist, Pragmatist) is a neat way to force diversity of perspective and makes for entertaining, shareable threads; live chat, voting and a verdict mechanic add community glue. It feels like a well-polished demo rather than a research advance — interesting and fun, but derivative of existing multi-agent/LLM playgrounds and likely limited by shallow or repetitive model outputs unless they invest in moderation, grounding, or stronger agent orchestration.
Real survey data grounding beats generic LLM personas for market research validation.
