NY
NYT Spelling Bee Year in Review
Hacker NewsNYT Spelling Bee Year in Review
Hey! As a super-fan of the NYT Spelling Bee, I'm excited to share this year's "Year In Review," an in-depth analysis of the words and trends we saw throughout the year. If you're also a fan of the Spelling Bee, or just want to check out Hex, the tool used for the analysis and data viz, it's worth a look. Hope y'all enjoy.
Share cardActual performance
1points
Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: excited · Missing: https docs, just released, exist42%42% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive30%30% 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, revenue14%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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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