Directory of NextJS Starters and Boilerplates

Hacker News

Hey hackers and makers. I wanted to run an experiment on building and launching a directory in one hour. I made it in 54 minutes. The following is the step-by-step breakdown. → Idea. NextJS starter kits and boilerplates are super hot these days. It was as hot as gpts or AI back in the day. So I saw the trend. → Name & Domain. I went to Google and searched for everything related to this to find a keyword that got nothing serious on the top. Then I looked at the Google searches for this keyword and picked the one with 10k searches but low competition and it was available on Godaddy for $10. → Directory and text. I went to unicornplatform, picked a directory template, and cloned it. I asked AI to edit to next based on the project name, it just made it all well from the first try. → Images, logo, favicon, video. I asked ChatGPT Vision to generate this. It did a good job. Then I went to runwayml to edit the generated images with prompts and the gif for the tweet. Took me about 10 minutes to make it beautiful. → Content. I went to DevHunt and PH and found winners. Added 4. Just to start with. → New content. I added a form so people can submit their NextJS starters, so that I can start to fill up the list. Submit your starter kits, in case this goes viral, those who submit first will be on the top of the list →→→ http://nextjsstarter.com

Visit product View on Hacker NewsDec 5, 2023

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

93%93% 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, new, chatgpt · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: video, google, way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, 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

35%35% 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

19%19% 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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