Siftr

Siftr

Indie Hackers

The Tinder for finding roommates.

We are trying to solve the problem: "I wish I had know 'that' about my roommate before moving in with them. I would have never moved in with them if I had known."

Share card

Actual performance

1followers
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
46%46% 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, intuitive
34%34% 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
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Tripdip
Tripdip46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Like tinder for finding a perfect holiday

Indie Hackers1ai
Fi
Finding puns computationally51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Finding puns computationally

Hacker News24
Fi
Finding Interesting Publications on PubMed44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Finding Interesting Publications on PubMed

Hacker News2
Cr
CrushVote – Tinder for the US presidential election39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CrushVote – Tinder for the US presidential election

Hacker News2
No
Nosy – Imgur for Tinder and texting69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Nosy – Imgur for Tinder and texting

Hacker News4
Ti
Tinder for Clothing62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tinder for Clothing

Hacker News15
Ta
Tastebuds: Tinder for Concerts62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tastebuds: Tinder for Concerts

Hacker News1
Hi
Hinder – Tinder for HN65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Hinder – Tinder for HN

Hacker News1
Ti
Tinder for Netflix with AngularJS/Ionic63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tinder for Netflix with AngularJS/Ionic

Hacker News7
Ti
Tinder for Uber72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tinder for Uber

Hacker News6