I made a demo for my project management tool

Hacker News

Hey Hackers, Last time I posted alot of the comments were asking about the posibility for a demo and maybe more reasoning on why someone should use the service. I have tried to implement some more things into the basic tier so that out of the box, the service is more unique with its use of AI. Anyway, there is now some new AI features as well and I've improved the existing features to ensure they have the right level of accuracy and creativity. Enjoy, Dan

Visit product View on Hacker NewsJun 21, 2024

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% 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 · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

11%11% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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