DomainsGPT – GPT-Powered Domain Name Generator

Hacker News

Hello Hacker News! Steven here – very excited to be launching DomanisGPT! Some of you may remember One Word Domains, which I launched back in 2020. It gained significant traction after being featured on the front page of HN for half a day ( https://news.ycombinator.com/item?id=23291337 ). So, launching DomanisGPT here, almost 3 years later, feels incredibly special. Interestingly, this isn't my first attempt at building an AI Name Generator for One Word Domains. I first tried in early 2021, long before GPT-4/ChatGPT existed. As part of my undergrad NLP thesis, I trained a Markov Chain on 100,000+ company names, stored it in S3, and hosted a Flask API endpoint on EC2. It was decent, but nowhere near the level of quality I wanted it to be. Now that OpenAI's APIs are available to use, I decided to give this another shot! Hope you guys like this, and please let me know if you have any feedback about it!

Visit product View on Hacker NewsMar 30, 2023

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2points

Did not reach leaderboard

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

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

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, hacker news · Missing: https docs, just released, lua

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, openai · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% 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

17%17% 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.

Incorrect prediction on native model

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