Launch Intel
Pa

Panini AI – Serve ML/DL Models in Few Minutes

Hacker News

Panini AI – Serve ML/DL Models in Few Minutes

Visit productView on Hacker NewsApr 10, 2019
Share card

Actual performance

11points
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
91%91% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
47%47% 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
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% 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, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
22%22% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Ef
Efemarai – Visualizing and debugging ML models71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Efemarai – Visualizing and debugging ML models

Hacker News8
Bu
Build predictive models with no prior ML experience85%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Build predictive models with no prior ML experience

Hacker News70
De
Deploy ML Models on a Budget55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Deploy ML Models on a Budget

Hacker News117
I
I indexed 37h of my videos using an RTX 4090 and local ML models in 24h80%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I indexed 37h of my videos using an RTX 4090 and local ML models in 24h

Hacker News4
De
Deploy any ML models with 1 line62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Deploy any ML models with 1 line

Hacker News1
An
An annotation tool for ML and NLP63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An annotation tool for ML and NLP

Hacker News76
An
An annotation tool for ML and NLP63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An annotation tool for ML and NLP

Hacker News5
Se
ServeIt – simple API serving for Python ML models68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ServeIt – simple API serving for Python ML models

Hacker News5
De
Deploy ML Models with ML.NET, Docker and Azure Container Instances48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Deploy ML Models with ML.NET, Docker and Azure Container Instances

Hacker News2
M2
M2cgen – generate native code from ML models(Sk-learn/XGBoost/LightGBM)64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

M2cgen – generate native code from ML models(Sk-learn/XGBoost/LightGBM)

Hacker News4
Browse all productsAnalyze your launch