Cursor, Claude Code, and Copilot can ship a working app in a weekend. Keeping it durable — typed, tested, secured, and safe to change — is the part Post Code does. We work with solo founders professionalizing what they shipped fast, and with teams putting practice around the agents they have already adopted.
We work with founders shipping alone and with teams putting practice around the agents they already use. The shape of the engagement is different. The standards are the same.
For solo founders
You built something real with Cursor or Claude Code, and it is in front of users. The next set of changes is where vibe-coded apps usually start to crack.
For growing teams
Cursor, Copilot, and Claude Code are in everyone’s editor. The review habits, repo instructions, and CI gates that should sit around them are still catching up.
The first version proves demand. The next version needs boundaries, runtime checks, and the confidence to change important code without restarting the product
Plausible code, hidden risk
Generated code that looks consistent often skips the runtime checks at trust boundaries
Forms, API routes, webhooks, and queues validate at compile time but not at runtime. We surface where the database schema, the application types, and the actual inputs have quietly diverged.
Bugs the agent keeps reintroducing
Recurring regressions usually mean a missing type, test, or lint rule — not a longer prompt
We convert repeat bugs into discriminated unions, regression tests, and project-specific lint rules so the same mistake cannot land twice. Duplicated logic across screens and jobs becomes shared, typed utilities.
The demo-to-durable gap
Auth gaps, non-idempotent writes, and missing observability are what hurt real users
We harden authentication on every protected route, push authorization down to the data layer, make payment and background jobs idempotent end to end, and wire critical paths to monitoring so failures surface before customers notice.
Articles on the point where AI-assisted prototypes need clearer architecture, stronger types, and more durable workflows.
Why formatting, import rules, naming conventions, and static checks become practical safety systems in AI-heavy workflows.
Read articleA practical way to turn architectural decisions, naming conventions, data rules, and forbidden patterns into durable project constraints.
Read articleSend the repo context, your stack, and what is starting to feel hard. We come back with the shape of engagement that fits — and the price — usually within a day.
Start small with a diagnostic, then invest in the code paths that unblock growth. Pricing is scoped after reviewing the product and repository.
$649
A structured diagnostic that scores the repository, product, and agent workflow.
Custom
Flexible implementation, technical leadership, coaching, or maintenance for a team that needs support.
A few practical answers for teams deciding whether to stabilize, modernize, or rewrite an AI-built app.
The best fit is a working web app with real business value, a growing feature backlog, and increasing friction from generated code.
Not by default. I start by identifying what is already valuable, then replace brittle pieces only when that is cheaper or safer than stabilizing them.
No. The goal is to make agents safer by giving them clearer architecture, better repo instructions, typed contracts, and review gates.
Need more detail? Read the full FAQ.