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Fatebook – the fastest way to make and track predictions

Hacker News

Fatebook – the fastest way to make and track predictions

I see forecasting as anti-bullshit technology: - It gives you truthseeking incentives - You communicate your uncertainty as a probability, which is way clearer (70% is better than “probably”) - You can aggregate forecasts to get wisdom of the crowd effects - You can build a track record, calibrate your uncertainty, and pay more attention to people who are consistently accurate I’m a fan of prediction markets [0] and forecasting platforms [1]. But predictions on existing platforms are public - and the questions that are most important to you are normally about your work, which are often best to keep private by default, so you can give your true credence. So this is a simple tool for very quickly recording your predictions, getting a reminder to resolve them as YES, NO or AMBIGUOUS, and visualising your track record (calibration chart, Brier score [2], relative Brier, frequency of predictions). ==== [0] https://news.ycombinator.com/item?id=30275860 [1] https://metaculus.com/ [2] https://en.wikipedia.org/wiki/Brier_score

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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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 · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, visual · Missing: mac, agents, macos
30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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