Founder knowledge engine
Arif KhanCategory
7 posts
If you're new, this recaps Parts 1 and 2. If you're continuing, this is the payoff: how scoring + discovery connect with memory and research so corrections become permanent upgrades.
Without a memory architecture, every AI agent correction evaporates at session end. Same mistake, next week. Borges closes the loop — a Monday correction becomes a permanent skill improvement by Wednesday.
A single-pass web search produces a confident-sounding summary with six gaps and three outdated facts. Hawking runs 3-6 progressive rounds until coverage hits 80%, and it's not allowed to stop early.
We didn't invent self-improving AI. We got curious, studied five frameworks, and adapted them for a problem nobody talks about; agent skills rot.
Darwin doesn't just improve existing skills. Every week it scans every agent conversation to find capability gaps — and proposes new skills before anyone asks.
Speed is the easy part. The harder design problem is review architecture: how correction, escalation, and quality control should work once agents enter the system.
Not tools. Governance, handoffs, and clarity. Agent systems fail at the seams where nobody owns decisions.
