My trial co-founder and I built Unpitched as a 5-week run to test if we'd work well together. The twist: we're experienced software engineers yet AI wrote ~90% of the 24k lines of code in Unpitched.
The product analyzes customer interview transcripts to catch when founders slip into "pitch mode" instead of learning. It's based on principles from The Mom Test book - essentially a digital coach that flags your mistakes and gives you personalized advice how to do better.
Why this project for our trial:
- Real problem we'd witnessed (founders talking too much in user interviews)
- Tight scope but production-grade requirement
- Chance to push AI-accelerated development to its limits
Tech: Next.js 15, Supabase, Trigger.dev, GPT-4.1 via Vercel AI SDK. We used Cursor, Claude Code, V0, and (briefly) Grok for development.
Key learning: AI development requires adopting new working patterns. You can think of AI as a chaotic software engineering intern. You need to be highly intentional in guiding the AI to do the right thing. Just like with human teams, bad managers get bad output from their people and the same applies to managing AI.
If you're an experienced software engineer, you have a lot of implicit assumptions about how to build software, how to rate importance of tasks, etc. You need to transfer these to the AI, and we think we found early patterns how to do this well.
For example, we used "walking skeleton" and "tracer bullet" concepts to structure project planning we did with AI. We found the basic pattern of think-research-brainstorm, and plan before writing any code to dramatically improve the quality of AI coding, as the project gets more complex. E.g. we'd plan error handling with AI first, save it as a doc, then use that as context for implementation - this kept the AI consistent across the codebase.
We shared details of this approach at Warsaw AI Tinkerers (over 200 people attending) a couple of weeks ago.
The co-founder trial worked - we built a working mini-product in 5 weeks, found out how we approach this alien technology in the form of modern AI, and uncovered many interesting personal quirks of each other (everyone has them).
You can check out Unpitched at https://unpitched.app. Sadly, we require sign up as underlying LLM calls are a little expensive.
We wrote more about how we approached the cofounder trial process at https://unpitched.app/about. Let us know if you have any questions about our trial, maybe share your own stories of looking for cofounders, or have any feedback on the app!
PS. Shootout to Circleback team (YC W24) as the only note-taking app we found that has working webhooks that we could integrate with Unpitched.
gkk•3h ago
The product analyzes customer interview transcripts to catch when founders slip into "pitch mode" instead of learning. It's based on principles from The Mom Test book - essentially a digital coach that flags your mistakes and gives you personalized advice how to do better.
Why this project for our trial:
- Real problem we'd witnessed (founders talking too much in user interviews) - Tight scope but production-grade requirement - Chance to push AI-accelerated development to its limits
Tech: Next.js 15, Supabase, Trigger.dev, GPT-4.1 via Vercel AI SDK. We used Cursor, Claude Code, V0, and (briefly) Grok for development.
Key learning: AI development requires adopting new working patterns. You can think of AI as a chaotic software engineering intern. You need to be highly intentional in guiding the AI to do the right thing. Just like with human teams, bad managers get bad output from their people and the same applies to managing AI.
If you're an experienced software engineer, you have a lot of implicit assumptions about how to build software, how to rate importance of tasks, etc. You need to transfer these to the AI, and we think we found early patterns how to do this well.
For example, we used "walking skeleton" and "tracer bullet" concepts to structure project planning we did with AI. We found the basic pattern of think-research-brainstorm, and plan before writing any code to dramatically improve the quality of AI coding, as the project gets more complex. E.g. we'd plan error handling with AI first, save it as a doc, then use that as context for implementation - this kept the AI consistent across the codebase.
We shared details of this approach at Warsaw AI Tinkerers (over 200 people attending) a couple of weeks ago.
The co-founder trial worked - we built a working mini-product in 5 weeks, found out how we approach this alien technology in the form of modern AI, and uncovered many interesting personal quirks of each other (everyone has them).
You can check out Unpitched at https://unpitched.app. Sadly, we require sign up as underlying LLM calls are a little expensive.
We wrote more about how we approached the cofounder trial process at https://unpitched.app/about. Let us know if you have any questions about our trial, maybe share your own stories of looking for cofounders, or have any feedback on the app!
PS. Shootout to Circleback team (YC W24) as the only note-taking app we found that has working webhooks that we could integrate with Unpitched.
-- gkk & ykka