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I built a remove BG product using ChatGPT, Midjourney and HuggingFace
Hacker NewsI built a remove BG product using ChatGPT, Midjourney and HuggingFace
I wanted to select certain parts of a photo, extract them, crop them, and remove the BG. All without using a brush or any manual process. ChatGPT helped me to come up with the product domain, write code snippets, and do copywriting. I designed the logo and photos with Midjourney. I found many free APIs on Huggingface to do the image segmentation and removal of the background.
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4points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: chatgpt, using, code · Missing: mac, agents, macos74%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 · Missing: supports, reddit linkedin, podcasting71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive48%48% 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 released46%46% 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, personal36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue21%21% 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, cryptocurrency1%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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