St

Startups, this is how design works.

Hacker News

Startups, this is how design works.

Share card

Actual performance

10points
4comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
77%77% 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.
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.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
49%49% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

De
Design for Startups51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Design for Startups

Hacker News32
Ki
Kissui.sticky – 2kb sticky position that works everywhere54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kissui.sticky – 2kb sticky position that works everywhere

Hacker News2
Ki
Kissui.sticky – 2KB sticky position that works everywhere54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kissui.sticky – 2KB sticky position that works everywhere

Hacker News1
A
A Viewport That Works36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A Viewport That Works

Hacker News1
Ho
How reMarkable LiveView Works36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

How reMarkable LiveView Works

Hacker News3
Da
DaLMatian – Text2sql that works55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

DaLMatian – Text2sql that works

Hacker News44
Th
This is basically how OAuth2 works26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

This is basically how OAuth2 works

Hacker News2
Br
Brazilian hobbyist artist works37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Brazilian hobbyist artist works

Hacker News3
Vi
Visualizing How a Perceptron Works50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualizing How a Perceptron Works

Hacker News5
Th
The Most Influential Works on TvTropes According to PageRank52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Most Influential Works on TvTropes According to PageRank

Hacker News38