Rolling my own website for a web novel about AI and climate

Hacker News

Since I'm trying my hand at a web novel about tech issues like AI and broader contemporary topics like climate change, I thought it would be appropriate to roll my own publishing system using Github pages and Python. The website also features some interactive components to engage readers in the world. (Also, making my own frontend gives me the chance to add a custom dark mode toggle) I hope you guys find it interesting! Repo is here: https://github.com/aulddaegs/aulddaegs.github.io

Visit product View on Hacker NewsJun 15, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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 · Missing: mobile apps, ios, personal

45%45% 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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

11%11% 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.

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