V0-Devin-Cursor Build Flow
Prototype in V0, delegate to Devin, then finish and verify in Cursor.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 98%
The V0-Devin-Cursor Build Flow divides AI-assisted product development into three stages. Start in V0, where a product leader or designer can turn a customer problem into a working visual prototype and refine interactions through several prompts. That iteration clarifies the specification before production work begins. Once the intended experience is concrete, hand the final prompt, prototype, or shared URL to Devin for asynchronous implementation in the real repository. If Devin cannot finish cleanly, move into Cursor or a pairing mode to resolve the remaining code. A human then checks architecture, behavior, and tests before merging. The mechanism removes serial handoffs without removing judgment: AI performs most construction, while the human owns intent and acceptance.
Origin
Sahil Lavingia demonstrated this as his usual workflow while replacing Flexile's native contractor start-date input with a natural-language date picker during the episode.
Core principles
- 01Use visual prototyping to clarify the specification before generating production code
- 02Match each AI tool to the stage where it is strongest
- 03Let asynchronous agents handle implementation while the human keeps moving
- 04Treat Cursor and human review as the recovery path when the autonomous agent stops short
How to run it
- 1
Select a bounded problem
Start with a concrete customer-facing issue whose desired outcome is easy to inspect. Sahil chose a weak native date picker that was useful but too small to justify a traditional two-week handoff chain.
Pro tip Favor changes that can be described against a specific page, component, or interaction.
Watch out Do not begin with a vague request that forces the agent to invent the product goal.
- 2
Prototype in V0
Ask V0 to build the interaction in the context of the product and user. Use the live prototype to see both the interface and its behavior.
Pro tip Include domain context, such as an HR onboarding form, so the prototype suggests relevant defaults and interactions.
- 3
Refine the specification
Spend several prompts correcting labels, layout, edge cases, and interaction details. Treat each adjustment as a discovery that makes the eventual implementation brief more precise.
Pro tip Invest 10 to 40 minutes here when the implementation agent is likely to execute the result quickly.
Watch out A first-pass prototype leaves avoidable ambiguity for the implementation stage.
- 4
Delegate to Devin
Give Devin the refined prompt, component, or prototype URL and point it at the correct repository. Let it locate files, make changes, run the application, and prepare a pull request asynchronously.
Pro tip Keep independent small tasks running in parallel while you continue product work.
Watch out Repository setup or stale project context can prevent the agent from running the application correctly.
- 5
Finish and verify
Use Cursor or pairing mode when autonomous execution is incomplete. Review the architecture and user behavior, run relevant tests, and merge only after the change works in context.
Pro tip Ask a teammate familiar with the affected system to confirm the change and whether tests need updating.
Watch out Do not treat a plausible diff as proof that the feature works.
In the wild
Sahil noticed that Flexile's contractor invitation page used a poor native date input. He prototyped a richer HR-specific date picker in V0, refined details such as suggested dates and explanatory text, then asked Devin to replace the production component. The resulting pull request was reviewed and merged during the conversation.
→ A change Sahil estimated could once have taken at least a day, or passed through a two-week process, reached a merged state during the episode.
Common mistakes
Stopping after the first prototype
The first visual pass may look convincing while still hiding unclear interactions and weak product decisions. The specification improves through deliberate back-and-forth in V0.
Sending ambiguous language downstream
A label such as 'magical' does not explain the underlying behavior. Name the actual capability, such as natural-language date parsing, so the implementation reflects the intent.
Merging without contextual review
An agent can produce clean-looking code while missing tests, repository state, or the requested behavior. Verify the running product and involve the right human reviewer.
Is it for you?
Best for
Product-minded builders working on bounded software changes in an AI-friendly web stack.
Not ideal for
Large architectural migrations, safety-critical changes, or codebases that autonomous agents cannot reliably run and test.
From the episode
Announcing a brand-new podcast: “How I AI” with Claire Vo 🔥