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A tool to find books outside gender echo chambers

Hacker News

A tool to find books outside gender echo chambers

Hi HN. I made this mostly for my own curiosity. Many of us end up reading books by or about people of specific genders. So it's interesting to find books that are outside that echo chamber but still mostly _inside_ the topical or affinity area we are most engaged in. Doing this, it helps us to each expand our reading while elevating underrepresented authors, topics and character-types in the books we read. For myself, I found it most interesting to find more female writers in the topic area of economics and technology, where I've experienced a high concentration of male writers. From the explainer: . Imagine sitting in a library of limited size, tailored perfectly to your tastes. If we look at all the books in this library, the genders of their authors, and the genders of characters within the books, then we can find your "literary gender bias". That's what this tool attempts to do. Giving the tool 3-5 books can serve as the seeds of this hypothetical library. From these, we can then extrapolate, calculate and show you your gender bias, and a set of recommendations that seek to invert that bias.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: plain · Missing: mac, agents, macos
36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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