Feedback Widget for Production ML

Hacker News

Hey HN! ML Feedback Widget is an simple and open-source React component that you can add to your ML products/projects to get direct feedback from customers on the results of your production models. A few months ago, we started exploring the ML tooling space. Through our conversations with various startups, we noticed that production ML evaluation tends to be based on how the model affects product metrics and KPIs rather than model metrics. At the same time, it wasn’t always easy for them to actually tie those systems together or quickly get a clear pulse on how users were liking the model-powered feature, because product insights and model inference usually live in different places. For now, the widget is just a simple React component. To view the feedback sent by customers, you’ll have to write your own endpoint. If this use case resonates with you, we’re also working on a more fleshed out version with a few initial integrations (S3, Posthog, etc) and support for custom styling to support prod workflows.

Visit product View on Hacker NewsMar 19, 2023

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

92%92% 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 · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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: month, users, way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

18%18% 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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