Aiproduct.tools – Generative AI Tools for product, business and more

Hacker News

Hi all, I have been working on a fun little project - https://www.aiproduct.tools/ This is an exploration of Generative AI and UX paradigms for building applications on top of LLMs, with use cases focused on Product, Business etc. Currently, there are four tools to play with: - Lean Canvas Generator: Generate and download a Lean Canvas for a business idea - Opportunity Solution Tree Generator: Generate and download an Opportunity Solution Tree for a desired outcome - Metrics Explorer: Explore all the dependencies of a North Star Metric - MINDSPACE Framework: Ideate effective behavioral interventions The inputs on mobile are a bit cranky and I would suggest using this on a desktop for the best experience. I am planning to keep expanding this library of tools and exploring other use-cases on similar lines. I'd love to hear your feedback and suggestions, so please feel free to try out the tools and let me know what you think. Thanks for checking it out!

Visit product View on Hacker NewsApr 8, 2023

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

52%52% 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.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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