Carton –- Scoping products from 0.0.1 to success

Hacker News

Hey! Let me introduce Carton … A product can be defined by the answers to your customers' questions. A feature brings together questions and answers. While the questions mostly remain the same, the answers can change and may correspond to different versions, forming a roadmap for future product success. A phenotype is the visualization of an answer, evolving as the answers change over time. Carton provides a framework for product scoping (not project management or product design) that makes use of this definition. Let's think even further. With the advent of artificial intelligence, the entire process of software development is undergoing a fundamental transformation. Soon, software will be created more through natural conversation than traditional programming languages. Carton helps ensure you always have a clear record of what you've asked from the AI and, even better, helps to request what comes next. Its a very early proof of concept phase. Super excited to hearing what you think!

Visit product View on Hacker NewsDec 16, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: visual, project management · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, way · Missing: mobile apps, ios, personal

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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