Data Is Plural, one year later

Hacker News

Last year, I posted a "Show HN" about Data Is Plural, a newsletter that shares five interesting/useful datasets each week: https://news.ycombinator.com/item?id=10513012 Your feedback was encouraging, and your suggestions useful. Thanks in large part to you, Data Is Plural is thriving. Last week marked the newsletter's one-year anniversary, and yesterday I sent the 50th edition. To celebrate, I've open-sourced the newsletter's edition-by-edition stats: https://github.com/data-is-plural/newsletter-stats (You can see the HN bump in the first chart.) And, as always, you can find a structured archive of all previously featured datasets here: https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit Finally, if you'd like to subscribe, you can do so here: https://tinyletter.com/data-is-plural Many thanks, Jeremy

Visit product View on Hacker NewsOct 27, 2016

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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: google, way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, new, tiny · Missing: mac, agents, macos

47%47% 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

37%37% 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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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