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- 10-minute AI playbook that replaces $50k app builds or 12 months timelines
10-minute AI playbook that replaces $50k app builds or 12 months timelines
~$1k to build. Weeks to ship. No dev team required.
Hey Vibe Marketer,
You don’t need to become a developer to build software. You just need AI.
A few weeks ago, Greg Kowalczyk walked us through a 90-min session on: how to turn AI into a build partner, break features into modules, reset context before things spiral, document as you go, and get through App Store review without losing your mind.
He shared exactly how he used AI to build his apps and publish it in App store.
If you are a founder or a solopreneur, this is the exact system that is going to help you build a system for you to build your app.
Also, this is just 10 mins of the entire session. If you want to access the full 90-minute masterclass, join The Vibe Marketer’s community.
10-minute playbook to build an app that scales:
1) Start with a pain point → write a PRD with AI (but don’t code yet)
Use ChatGPT / Claude / Gemini to produce a PRD: screens, core flows, data needed, edge cases.
He explicitly said he gathered outputs from multiple models, then consolidated.
Key takeaway: You’re not asking AI to “build an app.” You’re asking it to produce a plan that’s testable.
2) Prototype fast in an “easy builder” → but in tiny modules
Get started with a working skeleton / prototype
big lesson: don’t paste a 15-page PRD and ask for the whole city.
Build one small feature at a time: one screen, one flow, one integration.
Rule: One prompt = one small outcome.
3) Debug like a product engineer: test → observe → fix → repeat
Test constantly
When something breaks, return to the AI with:
what you expect
what happened
exact error / screenshot
where in the build it occurred
Rule: Treat AI like a junior dev: it needs tight bug reports.
4) Prevent hallucinations: “clear context” aggressively
When an agent starts drifting / breaking unrelated parts:
Clear history / reset context
Re-issue the instruction cleanly, using current state + goal
Rule: If the AI starts “being creative,” you reset, not argue.
5) Move from prototype → real dev environment for control (Cursor/Claude Code)
When things got complex (GPS/maps, store rejections, deeper edits):
Switch to Cursor so you can:
access files directly
make precise edits
debug properly
stop relying on “agent magic”
Rule: Prototype in the simple tool, ship in the real IDE.
6) Documentation becomes a superpower (and makes shipping easier)
After each module, ask AI to produce:
READMEexplaining architecture/logicCHANGELOGtracking changes by version
Those docs then help for:
app store submissions (policies, terms, guides)
maintenance
future features
Rule: “If it isn’t documented, AI will break it later.”
7) Submit early to the App Store (don’t wait for perfection)
This was very actionable:
Submit once you have basic structure + identity (name/icon).
Then iterate through rejections while building.
(quick story - Greg got rejected 39 times for his first app, but the second app got approved in days because he learned the pattern)
Rule: App Store review is part of development, not the final step.
Wrapping up
This is just a 8-minute segment from the full 90-minute session, where Aaron showed how to:
Ship production-ready mobile apps using AI tools with zero coding background
The exact decision-making process behind each app (what to build, what to skip, and why)
Live walkthroughs of real app features (onboarding, data capture, notifications, recommendations, GPS, integrations)
How the apps were positioned as brand ecosystems, not standalone products
Monetization thinking: free vs paid tiers, perceived value, and long-term pricing strategy
How to document apps as you build to simplify updates, approvals, and scaling
How this approach changes what’s possible for founders, marketers, and small teams in 2026
Live Q&A covering maps, authentication, go-to-market, and user acquisition
If you’re a founder or business owner who’s been sitting on an app idea because “I’m not technical,” this session shows how to use AI to turn ideas into shipped products without a dev team, massive budget, or long timelines.
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