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Playing Telephone with GPT-4 and Dall-E-3

Hacker News

Playing Telephone with GPT-4 and Dall-E-3

Hello Hacker News! I’ve been having some fun recently and I wanted to share with you. My idea was to feed ChatGPT an image and ask for a caption, feed that back to DallE, then call this in a loop, and observe how the image that was generated changed over time. Similar to playing the game “telephone”. The results were really intriguing, so I built something that you can play with. Note this is best on a desktop (streamlit is optimized for a large screen), but if you’re on mobile you’ll want to expand the sidebar to start — it’s the carrot on top. My initial intention was for you to be able to submit seed images on the site and watch them progress, but that was too expensive and slow. So instead I built an explorer. While this also illustrates a framework I’m developing that helps structure code, it should be interesting regardless of whether or not you’re looking for new tools. I’m happy to add more images — feel free to suggest starting ones you’d like to see!

Visit productView on Hacker NewsDec 20, 2023
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, code · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
51%51% 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 · Missing: mobile apps, ios, personal
49%49% 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
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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.

Correct prediction on native model

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