QuickAI Version 2 Released

Hacker News

I am very excited to announce version 2 of QuickAI Version 2 has exciting changes as follows: * Image classification models can be saved in the Apple CoreML format, allowing you to use these models on Apple devices. * YOLOV4 Inference can be performed in 2 lines of code. * Custom YOLOV4 models can now be trained, also in just 2 lines of code. * QuickAI can be run in a Docker container, making the setup/installation even easier than before. Please let me know if you have any questions/feedback.

Visit product View on Hacker NewsAug 17, 2021

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

80%80% 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 · Strong signals: excited, io · Missing: https docs, just released, exist

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% 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, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% 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.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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