Fo

Forevermore – commit your marriage vows to 16,000 nodes

Hacker News

Forevermore – commit your marriage vows to 16,000 nodes

Share card

Actual performance

5points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
81%81% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: 000 · Missing: https docs, excited, just released
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 · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% 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
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Go
GoodReadsLunatics – I turned 16,000 book reviews into a game62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GoodReadsLunatics – I turned 16,000 book reviews into a game

Hacker News2
Oo
Oops, commit typo55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Oops, commit typo

Hacker News1
cc
ccc (Conventional Commit Cheatsheet)55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ccc (Conventional Commit Cheatsheet)

Hacker News2
A
A Quine Commit55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Quine Commit

Hacker News2
Dualora
Dualora59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Record in both 16:9 and 9:16 at the same time

Product Hunt+111Android
LB
LBreakoutHD is a scaleable 16:9 remake of LBreakout2 ported for the web61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

LBreakoutHD is a scaleable 16:9 remake of LBreakout2 ported for the web

Hacker News4
Se
Search more than 16,000 lute pieces in French tablature65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Search more than 16,000 lute pieces in French tablature

Hacker News1
80
808 16-beat sequencer63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

808 16-beat sequencer

Hacker News2
My
My hardware implementation of the DCPU-16 in Verilog RTL70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My hardware implementation of the DCPU-16 in Verilog RTL

Hacker News4
I
I got 16,000 requests on my app in less than 24 hours65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I got 16,000 requests on my app in less than 24 hours

Hacker News1