Definition
Privacy-First Architecture
Design principle prioritizing data ownership and local control.
From the episode “AI Is Quietly Stealing Your Life's Work”Hear it on the podcast1 episode
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Related terms in Product & Engineering
POC (Proof of Concept)A pilot project demonstrating feasibility without full commitment.Point SolutionA narrow software tool designed for a single task within a broader workflow. Devansh's diagnosis: law firms drowning in point solutions is the core problem Irys was built to solve.PRDProduct Requirements Document, the spec that AI coding agents execute against.Predictive analyticsUsing data, statistics, and machine learning to forecast future outcomes, like churn, demand, or risk.Privacy-preserving computationMethods that let analysis or model training happen on sensitive data without exposing or copying it. TripleBlind's specialty.Production-Ready AppAn app that meets security requirements (no exposed PII, no auth vulnerabilities), passes App Store or Play Store review (account deletion flows, privacy disclosures, content guidelines), handles real customer data reliably, and performs correctly at scale. Visually and functionally indistinguishable from a prototype at demo time — the difference is entirely in what happens when real customers use it. The gap between 'demo-ready' and 'production-ready' is the core problem Woz was built to solve for non-technical founders who don't know what they don't know.Prototype and PruneA design workflow replacing the traditional comp → handoff → build waterfall: use AI to generate multiple real, enterprise-appropriate prototypes grounded in design system context, then let a human with taste prune to the right one. Coined by Jules at Meta, reinforced by Scott Belsky. The key insight: AI is a proxy for skill (production execution) but not taste (judgment about which correct option fits this context). Human curation is the irreducible step — and now it is the entire job.Prototype vs productionA prototype proves the idea works. Production means it works reliably for many users under real conditions with real data, and the distance between them is where most AI builds stall.