Use AI to practice tough conversation

Hacker News

The best way to get better at tough conversation (that I run away from) is to practice. I created this to help myself by simulating tough scenarios using AI. With Tough Tongue AI, you can stumble, stammer, and say the weirdest stuff—then try again until you nail it. No judgment, no sweaty palms, and definitely no awkward pauses...okay, maybe a few, the AI is still far from perfect, but it’s all part of the fun! It has been helpful and given me confidence to have those conversations I've been avoiding! Hope it's of help to you as well :)

Visit product View on Hacker NewsDec 18, 2024

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4points

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Launch Intel predictions

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

74%74% 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.

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

63%63% predicted probability of success on Indie Hackers, 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

42%42% 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: ios, way · Missing: mobile apps, personal, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

14%14% 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.

Correct prediction on native model

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