BuddyBreeds, a dog directory built with AI-created images and content

Hacker News

Greetings, Jay here. This was a side experiment. Primary goals: 1. Test using MidJourney for website image creation 2. Test using ChatGPT for data creation 3. Determine the best breed for my family's first dog AI pain points: - Getting MidJourney to create similarly-styled images - Hitting usage limits with GPT 4 (created it when they were much less generous than now) Created a year ago (April 2023). Took maybe a week of after-hours work. Was wondering how much organic traffic it might get on its own without any marketing efforts. Result: little to none (!). In the past month, however, it's been organically getting a little Google traffic. Love building things, hate marketing them (sounds familiar, right?). Figured I'd share it here to see what feedback folks might have. Grateful for feedback!

Visit product View on Hacker NewsApr 15, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

92%92% 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: google, chatgpt, using · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google · 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

40%40% 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

39%39% 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

18%18% 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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