Every greenfield project starts the same way: a senior dev spends two weeks re-deciding auth, data fetching, and CI before a feature ships. That's the greenfield tax — and it's the most accepted waste in software.
The question FACTA Templates asks is blunt: how many weeks do you lose re-deciding auth, data fetching, and CI on every greenfield? The answer, for most teams, is two or more — every time, on every new project.
The Greenfield Tax, Itemized
Issue-tree the waste and it's the same plumbing, re-decided:
- Auth: which provider, which session model, which token flow.
- Data fetching: which client, which cache, which error path.
- CI: which pipeline, which test runner, which deploy target.
None of that is the product. All of it is the tax. A senior dev wires it from scratch every time, and the cost is two-plus weeks of their time per greenfield.
The Cashflow Math
A senior dev costs thousands per week. Two weeks of rewiring plumbing on every greenfield is real money — and it's money spent producing nothing the customer sees. A template is $50 once. The math doesn't need a spreadsheet.
What the Templates Ship
FACTA Templates are 7 production-grade code scaffolds — cli, frontend-react, mobile-rn, backend-python, backend-rust, chrome-extension, html-ppt-template — with auth, typed data, CI, and tests baked in. Copy one, rename `<app>`/`<scope>`, ship on day one. Every template carries the same invariants; only the stack changes. Each ships its own README, architecture docs (C4 + invariants), and skeleton.
Who It's For
Not for everyone — for the operator who wants invariants decided before the agent writes a line. Three one-time tiers: one template, three, or all seven. No recurring, no SaaS, no lock-in. Free updates for life. One-time payment; the template is yours.
Conclusion
The greenfield tax is two-plus weeks of senior-dev time on plumbing that isn't the product. FACTA Templates decide the invariants before the agent writes a line, so the greenfield starts at the product, not the plumbing.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Pick a stack and stop paying the greenfield tax.
Browse the seven templates and copy the one that fits. See how Package Builder instantiates them for the full build toolkit.
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