Launch Intel
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A NextJS and MERN stack boilerplate to automate all the boring stuff

Hacker News

A NextJS and MERN stack boilerplate to automate all the boring stuff

Hey HN, I'm a solopreneur and I ship apps like a madman. I realized I was doing the same thing over and over: set up DNS records, connect DB, listen to Stripe webhooks... So I built ShipSeo 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 those boilerplates will be as helpful to you as it is for me. Would love your feedback pls Arthur

Visit productView on Hacker NewsNov 24, 2024
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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, stripe · Missing: mac, agents, macos
83%83% 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
75%75% 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
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
52%52% 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
30%30% 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
20%20% 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.

Incorrect prediction on native model

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