A different way to practice machine learning

Hacker News

We built a site that posts one machine learning question every day. Each question presents a problem. Usually, a real problem that you and I have or will face at some point in our careers. Questions are multi-choice, and we show you a full explanation after you answer them. No bullshit: you load the page, answer the question, and leave. The next day, rinse and repeat. We are using AWS in the backend. The questions are stored in a simple DynamoDB table. The React app fetches the questions from an API and tracks some basic stats, like the accuracy of the answers. Very basic functionality, but after the huge reception, we are already working on a few features like sessions and email subscriptions. We have many ideas around a database of high-quality machine learning questions. We are already planning to extend the site in different ways.

Visit product View on Hacker NewsApr 14, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, 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: mac, email, using · Missing: agents, macos, agent

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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

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

Correct prediction on native model

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