Definition
Prototype vs production
A 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.
From the episode “The 10-Minute Morning Routine That Replaced an Entire Project Management Stack”Hear it on the podcast1 episode
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Related terms in Product & Engineering
Privacy-First ArchitectureDesign principle prioritizing data ownership and local control.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.Repo (repository)The folder where a project's code and files live, usually hosted on GitHub.ReproducibilityThe ability to recreate the same result using the same inputs, steps, and conditions.Requirements ElicitationThe process of extracting and formalizing a founder's business objectives and translating them into technical requirements before writing any code. The CTO's core job. Without it, code generation produces something technically functional that solves the wrong problem. Lio automates this through guided prompting: it interviews the founder about business objectives, ICP, user types, and success criteria — generating a technical scope document before touching any code generation.Retrospective vs. Predictive AnalyticsRetrospective analytics answers what happened. Predictive analytics answers what is likely to happen next based on existing data patterns - the shift Burak identifies as the next frontier of business intelligence.