Neighbors & Enemies
Indie HackersApparel for the brave few who take Jesus at his word.
A hobby project to design and make the t-shirts and other apparel that I want to wear myself. Focused around the message of Jesus, who said that we are to love our neighbor and to love our enemy.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%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, revenue14%14% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Xpeer
Digitalized Word of Mouth
BetaListWo
Word2Bits – Quantized Word Vectors44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Word2Bits – Quantized Word Vectors
Hacker News221Pr
Probabilistic Spellchecker – You may have misspelled a word33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Probabilistic Spellchecker – You may have misspelled a word
Hacker News1Wo
Word.to33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Word.to
Hacker News3Et
EtymologyExplorer – word origin trees52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
EtymologyExplorer – word origin trees
Hacker News1Wh
Where I get the last word33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Where I get the last word
Hacker News8Wo
Word Slicer42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Word Slicer
Hacker News85Wo
Word Snake33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Word Snake
Hacker News8Spießig Or Not18%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Demistifying the german word spießig
Indie Hackerscommitment-side-projectDuos Word Pairs43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Word pairs app
Indie Hackerscommitment-side-project