GPT-3 and WhatsApp

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GPT-3 and WhatsApp

I built a WhatsApp chat bot(https://aibuddy.chat/) that uses GPT-3 to hold a pretty decent conversation (Users have to bring their own OpenAI keys). It holds a memory of last 5 conversations currently. Few planned improvements in near future. - Running this for WhatsApp is a bit expensive for both myself and the users), So the next plan is built this for telegram. - Let users choose different personas for the AI. - Let users choose how much conversation memory they want the bot to hold for them(more memory means more tokens means bigger bill from OpenAI) - Let users bring in their own data to fine-tune GPT-3 for a custom bot. For example, a bot trained on a writer's work or even your own. I can hook people up with some codes to try it out: just drop an email at aibuddy.chat@gmail.com

Visit product View on Hacker NewsDec 25, 2021

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

83%83% 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 · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

45%45% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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