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Hey Community, in my PhD and work afterwards I have spent a ton of time reproducing and building new ML models. To reduce the time it takes me to implement each new approach and have a better comparability I have developed a tool that has stored SOTA and my custom implementations in a standardized way. To launch a new training I can then simply fill out the forms with dropdowns etc in my locally run web application. I have started for computer vision, but would love to understand how that can be applied to your area. It is still not perfect, you can run the netlify preview (in the ReadMe) so you do not have to run it locally. Appreciate your feedback.

Visit product View on Hacker NewsMay 26, 2024

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

93%93% 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 · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

85%85% 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

78%78% 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 · Strong signals: way, para · Missing: mobile apps, ios, personal

47%47% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

21%21% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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