The place to learn any topic, quickly

Hacker News

Hi HN, we’re building deriveit.org, the website where you can learn any topic, quickly. On our website, you pay to ask any physics, computer science, or math question, and our community competes to give you the best explanation (for a cash reward). Right now, resources with high quality content are a time drain (papers, textbooks, and courses). We think that material is typically written in an unnecessarily hard-to-read way, but that there are tons of people who are passionate about explaining it well to others. We want to give the world access to those people, and remove barriers for them to answer questions and write content. I'm writing to encourage you to try us out, if you’re interested - ask a question on a topic you always wanted to learn (we added a free mode so you don't have to pay or sign in), and we’ll give you a condensed, easy-to-read explanation. Or write about the intuitions that you have, which you know you won’t find anywhere else online. We’re obviously in the early stages, and are very open to feedback.

Visit product View on Hacker NewsJul 26, 2023

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

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

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

61%61% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

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

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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