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Feather – 90 percent of Bloomberg terminal, for 5 percent of the price

Hacker News

Feather – 90 percent of Bloomberg terminal, for 5 percent of the price

Hey, Wanted to share what my friend and I built — Feather. It provides investors with all imaginable financial data, without breaking the bank. Effectively 90 percent of the Bloomberg Terminal, at 5 percent of the price. We just opened sign ups for early access — all you need to sign up is your email address. We’ll open access to the software in order of sign ups, and we’d love to have you onboard. Check it out! https://try-feather.com

Visit productView on Hacker NewsJun 25, 2022
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Actual performance

69points
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
62%62% 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
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, open · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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