Vibe Coding で SaaS MVP を1週間で動かした実装記録（Day1-7 と詰まったポイント）A hands-on 7-day build log showing how to ship a SaaS MVP by combining v0,…

A recently published Zenn post documents a seven-day sprint to build and ship a working SaaS minimum viable product using what the author describes as "vibe coding" — the practice of leaning heavily on AI tools to generate large portions of an application from natural-language prompts. The write-up matters because it offers a concrete, day-by-day account of how several popular AI-assisted development tools fit together in a single workflow, along with the practical friction points that emerged along the way. For solo builders and small teams weighing whether such a stack can realistically compress weeks of setup into days, the log serves as a grounded reference rather than a marketing claim.

The starting question the author poses is a familiar one: you want to get a SaaS running in a week, but you are unsure where to begin. The answer offered is a division of labor across specialized tools. For user interface generation, the project uses v0, which produces components based on shadcn/ui, alongside Lovable, which ships with built-in Supabase integration. The database and authentication layer runs on Supabase, providing Postgres plus Auth. Business logic is implemented with the help of Cursor and Claude Code, and the finished application is deployed to Cloudflare Pages. The core idea is that each tool handles the part of the stack it is strongest at, rather than expecting one assistant to do everything.

Some background helps explain why this particular combination appears well suited to rapid prototyping. The term "vibe coding" was popularized by Andrej Karpathy to describe building software by conversing with a large language model and accepting much of its generated output. v0 is Vercel's prompt-to-UI generator, and its reliance on shadcn/ui — a widely adopted collection of accessible React components — makes its output relatively easy to customize by hand afterward. Lovable belongs to a newer class of full-stack AI app builders that scaffold both front end and back end, and its Supabase integration reduces the manual wiring usually required to connect a UI to a database. Supabase itself is often described as an open-source alternative to Firebase, bundling a managed Postgres database with authentication, storage, and auto-generated APIs, which lowers the amount of backend code a small team must write.

On the coding side, Cursor is an AI-native editor built as a fork of VS Code, offering inline generation and codebase-aware chat, while Claude Code is Anthropic's agentic command-line tool that can read, edit, and run code across a project. Using them together suggests a pattern where one tool handles interactive editing and the other takes on more autonomous, multi-file tasks. Cloudflare Pages, the chosen deployment target, is a platform for hosting front-end applications and static sites with integrated serverless functions, and it competes with options such as Vercel and Netlify.

The post is structured around a Day 1 through Day 7 progression, pairing an overview of each day's goal with the points where the author got stuck. That framing is arguably the most useful part, because tool-combining workflows tend to break down at the seams: exporting AI-generated UI into a real repository, reconciling authentication flows, aligning generated database schemas with application logic, and handling deployment configuration are all common sources of delay. Readers looking for specifics on which steps proved troublesome will need to consult the original article, as the excerpt signals that blockers and their solutions are recorded per day rather than summarized in one place.

It is worth treating the "one week" timeline as an account of a single project rather than a guaranteed outcome. The result described is an MVP — a deliberately minimal product intended to validate an idea — so the scope is narrower than a production-grade release, and factors like the author's prior experience with the stack likely influence how smoothly the week progressed. Costs are another consideration the excerpt does not detail; several of these tools operate on freemium or usage-based pricing, so a comparable build could incur subscription or metered fees depending on volume.

Taken together, the log reflects a broader industry shift toward assembling applications from AI-assisted building blocks rather than writing every layer from scratch. The value for a reader is less about the promise of speed and more about the concrete role each tool plays and where the handoffs between them require manual attention. For anyone evaluating a similar approach, the write-up appears to function best as a checklist of what to expect and where to budget extra time.