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Database of Important Questions

Hacker News

Database of Important Questions

Hey everyone! I have a question for you: What’s the best piece of advice you’ve ever received? A few months ago, I was also thinking about this question, so I began reviewing all the notebooks I had kept over the past decade. These notebooks spanned from High School to college, from launching a startup to my Master’s degree, and now to my new career in ad-tech. In all this information gathering, I realized that some of the most important information I had ever recorded had not been pithy remarks or wise one-liners. Instead, they were well-timed questions. I built The Database of Important Questions as a central way for anyone to start asking those well-timed questions. Every question in it has meaningfully helped me stop, think, and readjust my course in life for a better end. Of course, just like our personal development, it's not static. More questions will be added, existing questions will be made more insightful, and old questions will take on new meanings. But, at whatever stage you find DBIQ, I hope it helps you in your endeavors! Already, it's been super cool to see visitors from all over the world — Cape Town, Rome, London, Saigon, Casablanca, Los Angeles, Toronto, and Jakarta to name a few! We hope to see you there! https://dbiq.app/?ref=hn

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
49%49% 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
36%36% 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
19%19% 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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