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A Dalle-3 and GPT4-Vision feedback loop

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A Dalle-3 and GPT4-Vision feedback loop

I used to enjoy Translation Party, and over the weekend I realized that we can build the same feedback loop with DALLE-3 and GPT4-Vision. Start with a text prompt, let DALLE-3 generate an image, then GPT-4 Vision turns that image back into a text prompt, DALLE-3 creates another image, and so on. You need to bring your own OpenAI API key (costs about $0.10/run) Some prompts are very stable, others go wild. If you bias GPT4's prompting by telling it to "make it weird" you can get crazy results. Here's a few of my favorites: - Gnomes: https://dalle.party/?party=k4eeMQ6I - Start with a sailboat but bias GPT4V to "replace everything with cats": https://dalle.party/?party=0uKfJjQn - A more stable one (but everyone is always an actor): https://dalle.party/?party=oxpeZKh5

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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.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
59%59% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
37%37% 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
22%22% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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