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Fragmenting Your Exposure to Big Tech

Hacker News

Fragmenting Your Exposure to Big Tech

It is with great excitement and trepidation that I’m finally posting my first Show HN! Though this short book is meant for a lay-consumer audience, it was written with the goal of being able to withstand the general scrutiny of the Hacker News community, which I cite in the references for any readers who are upset at the suggestion that they might not actually be all that technically inclined. Though a creative type with uncommercial tendencies, I still have a drive to succeed, and so I come to HN to hear about all the independent hustling, side projects, and tinkering going on, because I appreciate the spirit of it, even when I only understand so much of what is being talked about. Anyway, it took me 25 years as a writer to come up with a business plan, and here at the 8th title from the resulting small press, I’m finally ready to share it with HN, where I’ve been lurking for 11 years. I hope you enjoy it, or at least don’t find yourself wincing too much, cheers!

Visit productView on Hacker NewsJul 27, 2022
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, tiny · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
19%19% 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.

Incorrect prediction on native model

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