Se
Sense2vec model trained on all 2015 Reddit comments
Hacker NewsSense2vec model trained on all 2015 Reddit comments
Share cardActual performance
225points
79comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos81%81% 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.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto66%66% predicted probability of success on BetaList, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal50%50% 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, podcasting41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive28%28% 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, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Ev
Every haiku that happened on Reddit comments since December 200570%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Every haiku that happened on Reddit comments since December 2005
Hacker News14I
I made a map of Reddit from 176M comments68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I made a map of Reddit from 176M comments
Hacker News8Re
Reddit Unread Comments – track new comments on Reddit easier65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Reddit Unread Comments – track new comments on Reddit easier
Hacker News3Re
Reddit Comments / Threads Dataset65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Reddit Comments / Threads Dataset
Hacker News3I
I analyzed 50TB of Reddit comments to get top recommended fitness links51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I analyzed 50TB of Reddit comments to get top recommended fitness links
Hacker News9FIFI29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Surfaces buried high-engagement Reddit comments
Indie Hackerscommitment-side-projectEm
Embeddable hackernews comments69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Embeddable hackernews comments
Hacker News3Le
Lectio, contextualized comments for lectures75%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lectio, contextualized comments for lectures
Hacker News21Co
Collapse HN Comments54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Collapse HN Comments
Hacker News141Fl
Flocking comments63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Flocking comments
Hacker News3