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A NextJS boilerplate to automate all the boring stuff

Hacker News

A NextJS boilerplate to automate all the boring stuff

Hey HN, I'm a solopreneur and I ship apps like a madman. 16 startups in the last 2 years. I realized I was doing the same thing over and over: set up DNS records, connect DB, listen to Stripe webhooks... So I built ShipFast for 2 reasons: 1. Save time and focus on what matters: building a business 2. Avoid headaches like emails ending in spam or waiting 3 days for Google to approve I hope this boilerplate will be as helpful to you as it is for me. Would love your feedback pls Marc

Visit productView on Hacker NewsAug 31, 2023
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Actual performance

23points
37comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, stripe · Missing: mac, agents, macos
82%82% 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, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
63%63% 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 · Strong signals: apps, google · Missing: mobile apps, ios, personal
50%50% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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
1%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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