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Burnt out on dev, so I wrote a novella (hacking/karma/purpose in life)

Hacker News

Burnt out on dev, so I wrote a novella (hacking/karma/purpose in life)

Imgur post: http://imgur.com/a/8nFsW PDF view/download: https://www.docdroid.net/5gE3dZw/secret-society-of-the-stealth-karma-squad.pdf.html Synopsis: Stu is a burned out white-hat hacker living in a halfway house when he has an epiphany while helping an old lady at the grocery store. Is there a secret cabal of individuals who are all about spreading good karma, one small human interaction at a time? I know this isn't normal HN fare but thought it might not hurt for a change of pace. (also, the story involves hacking, social media, dank memes, etc)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
17%17% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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