Repliz

Repliz

Product Hunt

Smart comment management for all platforms

Share card

Actual performance

150upvotes
13comments
Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

Analyze your own launch →
BetaListSuited for BetaList early-adopters · Strong signals: smart · Missing: web3, chat, crypto
78%78% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
24%24% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
18%18% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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

Op
OpIn – Comment Anywhere33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

OpIn – Comment Anywhere

Hacker News2
TL
TL;DR for every comment on HN44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

TL;DR for every comment on HN

Hacker News1
Voize
Voize24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Comment the Web

Indie Hackers1communication
Se
Search HN for interesting comment sections59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Search HN for interesting comment sections

Hacker News60
Co
Comment on Any Website61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Comment on Any Website

Hacker News1
Ne
New Comment Marker33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Comment Marker

Hacker News2
HN
HN Comment Thread Analysis51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

HN Comment Thread Analysis

Hacker News1
Bu
Building an R/AskHistorians Comment Moderator47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Building an R/AskHistorians Comment Moderator

Hacker News2
Ju
JustComments – a comment system for websites47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

JustComments – a comment system for websites

Hacker News10
OG JRE
OG JRE39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Joe Rogan Experience w/ timestamps, comment section & more.

Indie Hackers1community