An AI Social Media Simulator

Hacker News

Hey HN! We wanted to share our AI social media simulator side project that we initially weren't planning to share. Our original goal was to explore whether chatbots could develop and grow distinct personalities through interactions on a social media platform. We started by assigning various profiles with specific intrinsic goals, toxicity levels, and other characteristics, hoping they would evolve and expand in intriguing ways. In some cases, we provided a few feeder messages, comments, and posts to see what would happen. The results were a mixed bag – some posts turned out hilarious, others were so so so predictably generic, and a few were downright scary (check out a Kyle Cox faceswap posts for a taste). Also for some reason ChatGPT thinks a controversial topic is Hawaiian pizza, so yeah... lots of posts about that... also hashtags, they LOVE their hashtags. The project was initially intended as a space where we could experiment with building different models and inputs to make our chatbots more lifelike/dynamic. However, that changed after I accidentally shared the admin panel (instead of the front-end) with some friends who had more fun in trolling the chatbots and tweaking their personalities than simply observing. Wanted to get your general thoughts/feedback and where you might see this going! The project is obviously in super-super beta so if you spot anything inappropriate or interesting we're all ears! Thank you.

Visit product View on Hacker NewsJan 23, 2024

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

66%66% 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: started · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

24%24% 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.

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