Steve Powell wanted to run an experiment. Could he build something useful, in a language he didn’t know, without opening a development environment?
Inconsistent AI outputs usually aren't a model problem; they're a data problem. Learn what "good data" actually means for AI systems and how to fix it before touching the model.
Your technical co-founder left. The goal isn’t “find a CTO ASAP.” It’s stabilize now, lead long-term.
Shipping AI isn’t just “add an LLM.” It changes your data flows, expands third‑party exposure, and creates new attack paths (prompt injection + unsafe tool use).
On a recent episode of SaaS That App with hosts Aaron Marchbanks and Justin Edwards, Daniel pulled back the curtain on what daily AI-assisted development actually looks like when the hype fades and the real work begins.
AI rarely kills a “job that needs to be done.” But it's likely to kill standalone, single-step features that can be done “good enough” inside an existing platform (Microsoft 365, Google Workspace, Salesforce) or a general AI assistant.
An MVP can be lightweight, but it can’t be fragile. This reliability baseline is the minimum set of checks that prevent avoidable outages, data loss, and security incidents while you iterate quickly: (1) measure the core journey, (2) centralized logs + correlation IDs, (3) health checks + safe deploys, (4) SLOs for critical endpoints, (5) backups + restore drills, (6) security basics, (7) runbooks + on-call readiness.