Pe

Persistence of sklearn models, tens of times faster, smaller

Hacker News

Persistence of sklearn models, tens of times faster, smaller

Share card

Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos
76%76% 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.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
63%63% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
51%51% 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
47%47% 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, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Th
The Probability Times39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Probability Times

Hacker News20
Ho
How many more times will you see your mother?40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

How many more times will you see your mother?

Hacker News1
TimeCheck
TimeCheck22%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find times when everyone is free

Indie Hackers1b2b
BroadBoard Times Square 🗽🌟
BroadBoard Times Square 🗽🌟46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get your product launch featured in Times Square 🗽🌟

Indie Hackers1advertising
Fi
Find the best times to commute42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find the best times to commute

Hacker News3
No
Nope – a Yup alternative that is 15 times smaller and 200 times faster69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Nope – a Yup alternative that is 15 times smaller and 200 times faster

Hacker News5
Pr
Proof of concept: Running UglifyJS 2.5 times faster43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Proof of concept: Running UglifyJS 2.5 times faster

Hacker News6
Ge
Get featured on a famous billboard in Times Square with 1 click55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get featured on a famous billboard in Times Square with 1 click

Hacker News3
The Hamburg Times
The Hamburg Times39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Manage Your Own Newspaper

Product Hunt+20
It
It's better to call Java drawImage() many times than once55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

It's better to call Java drawImage() many times than once

Hacker News2