Visualize and optimize your PyTorch models on Cellulose

Hacker News

Hey HN! I've been working on Cellulose for a couple weeks now, and today we're happy to announce PyTorch model support (via ONNX export) on our platform. Cellulose allows machine learning engineers understand, fine tune, and improve the inference performance of their models. With Cellulose, they can resolve these issues in just hours, not weeks. Feel free to give it a try and leave any questions you have down on this thread or shoot us an email at support@cellulose.ai. Cheers!

Visit product View on Hacker NewsJun 12, 2023

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent

87%87% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: visualize · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

46%46% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Library to visualize and inspect PyTorch models47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Library to visualize and inspect PyTorch models

Hacker News4

Scipy.optimize.linear_sum_assignment with wings61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Scipy.optimize.linear_sum_assignment with wings

Hacker News2

Antimander – Optimize Congressional Districts with Genetic Algorithms63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Antimander – Optimize Congressional Districts with Genetic Algorithms

Hacker News68

Prisma Optimize56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Prisma Optimize

Hacker News1

The Wavefiz – Visualize the Schrödinger Equation in WebGL68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Wavefiz – Visualize the Schrödinger Equation in WebGL

Hacker News9

EasingCookbook – Webapp to visualize Easing functions53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

EasingCookbook – Webapp to visualize Easing functions

Hacker News1

Visualize Fuzzy Searches59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualize Fuzzy Searches

Hacker News3

Visualize positions of a string52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualize positions of a string

Hacker News17

Poliana – visualize political influence38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Poliana – visualize political influence

Hacker News1

Visualize LR and SLR parsers56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualize LR and SLR parsers

Hacker News2

Browse all products Analyze your launch