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I gamified AI logo generation for better designs

Hacker News

I gamified AI logo generation for better designs

I recently quit my job at a decent TTS company I was working for as a software developer advocate to pursue what I have always enjoyed doing, which is creating software that is helpful. Before and after quitting my job, I have always found it expensive and time-consuming to hire a designer that can come up with a decent logo for my software, and as a software engineer, I really enjoy coding, not designing. One day, I made a pretty bold move to design a logo, and it took me a whole day to come up with a logo that looks nice. This is the reason I built Owl AI, a logo generator that generates top-notch logos using GPT-4o and Dalle-3. With OwlAI, I was able to build reliable software that would make my life much easier by allowing me to focus on my specialty, which is coding and definitely not designing. OwlAI is available for everyone to try out. Give me your honest feedback, and I promise I will respond to every single comment and make improvements.

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Actual performance

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5comments
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · 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: single, using, coding · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
29%29% 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, 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 · Missing: web3, chat, crypto
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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