In Development
Progressive Learning OS
A research cycle that turns complex goals into compounding knowledge and shipped outputs.
A tri-track operating system that advances objective execution while compounding learning and producing publishable outputs. Decomposes complex goals into risk-prioritized research, evidence-backed learning cards, versioned decision packets, and day-0 to day-7 execution plans.
Story
Most AI workflows are stateless — each conversation starts from zero. The Progressive Learning OS treats every interaction as a step in a compounding cycle: research feeds learning cards, learning cards feed decision packets, decision packets feed execution plans, and execution outcomes feed back into the next research cycle. Nothing learned is ever lost.
Focus
Self-improving research and execution cycles with structured knowledge persistence across an Obsidian vault.
Product Type: Agent System
Technical Highlights
- Tri-track system: objective execution, compounding learning, and publishable outputs running in parallel.
- Risk-prioritized research decomposition with evidence-backed learning cards.
- Versioned decision packets (v0.1 → v0.3) that mature through progressive research cycles.
- Multi-format publishing outputs: LinkedIn, X/Twitter, TikTok, and arXiv pathway.
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