Question 1

Why did my startup get a low score?

Accepted Answer

Low scores usually reflect weak market fit, crowded competition, unclear differentiation, or high execution risk. Our AI analyzes your idea across these dimensions and explains the main issues in the breakdown. Use the verdict and suggested pivot to refine your concept and re-roast to see improvement.

Question 2

What do the roast score tiers mean?

Accepted Answer

Ship It (85–100): Strong idea, worth validating further. Decent (70–84): Promising but has gaps to address. Needs Work (50–69): Significant issues; use feedback to iterate. Roasted (0–49): Major concerns; consider pivoting or solving the core problem before building.

Question 3

How does DontBuildThis score startup ideas?

Accepted Answer

Our AI evaluates your idea on market fit, competition, differentiation, execution risk, and clarity. It returns a score out of 100, a tier, a verdict, and a detailed breakdown so you know exactly what to improve before spending time and money building.

Question 4

Can I improve my startup idea score?

Accepted Answer

Yes. Use the breakdown and verdict to address weak points, then submit an updated idea or pivot. Many founders improve their score by narrowing the problem, clarifying the target customer, or strengthening their unique angle.

Question 5

What is a good score for a startup idea?

Accepted Answer

Scores of 70+ indicate promising ideas worth exploring. 85+ suggests strong potential. Scores below 50 mean the idea likely needs significant rework or a pivot before it’s worth building.

Question 6

What score did "Cursor for Product Managers

Over the last few years, we've seen an explosion of AI tools for writing code. Cursor and Claude Code are great at helping teams build software once it's clear what needs to be built.

But writing code is only part of building a product people want. The most important part is figuring out what to build in the first place!

Every successful product requires product management: talking to users, understanding markets, synthesizing feedback, and deciding what problems are worth solving and how the product should work. Whether this process is done by founders, engineers, or product managers, the activity is the same. Historically, the output has been product requirements docs, Figma mocks, and Jira tickets — artifacts designed to communicate intent to human engineers.

Today, teams use AI in isolated parts of this process, but there's no system that supports the full loop of product discovery.

Imagine a tool where you upload customer interviews and product usage data, ask "what should we build next?", and get the outline of a new feature complete with an explanation based on customer feedback as to why this is a change worth making. The tool would also propose specific changes to your product's UI, data model, and workflows, and would break down the development tasks so they could be handled by your favorite coding agent.

We think there's an opportunity to build a "Cursor for product management": an AI-native system focused on helping teams figure out what to build, not just how to build it.

As agents increasingly take the first pass at implementation, the way we define and communicate "what to build" needs to change.

If you're building in this space, we'd love to hear from you." get?

Accepted Answer

"Cursor for Product Managers