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Tenproblems.com – Literature Reviews for Inquisitive Minds

Hacker News

Tenproblems.com – Literature Reviews for Inquisitive Minds

This barely ramen-profitable content project at https://www.tenproblems.com was started in early 2020 as a pandemic lockdown effort, aka a way to keep the mind busy at a truly difficult time for we all. Now that some of us are luckily, slowly going back to business as usual, side time is becoming scarce again and it remains to be seen where to pivot this thing for 2022. Here enter you proud HNs, the lions, a tech-savvy world audience from many backgrounds and lifestyles, and that’s my question in the form of an informal survey. I basically have two simple options I can pursue in my spare time: A) just open a blog on the same home website and carry on with exploiting mostly unknown academic sources while adding comments, articles and pages for a bigger audience and SEO purposes. B) switch to software by aggregating and customising existing resources, related with any booklet in the tenproblems series, so that words can be followed by implementable actions. Of course, the level needs to stay low-but-not-too-low, say near the undergraduate entry bar of the spectrum, and implicitly aimed at dissemination and education. What would you pursue then? Thanks.

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Actual performance

2points
Did not reach leaderboard

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91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, profitable, education · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, profit, profitable · Missing: mrr, revenue, saas
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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