Hacker Fables – A satirical cyberpunk novel you can read as a man page

Hacker News

The project is entirely open source. It takes a single Pandoc Markdown source file and produces HTML, EPUB, PDF, man pages, GNU Info, an audiobook, an HTML-integrated audio player that highlights the content as it plays, and an MP4 output for the "video audiobook." It's generic enough to be reusable. I plan to use the pipeline for my tech blog later on. The novella contains a lot of programming lore. It's an over-the-top love letter to the old-school Linux programmer and to the craft itself. Source code: https://github.com/sebastiancarlos/hacker-fables

Visit product View on Hacker NewsJul 30, 2026

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

77%77% 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: open source, ide, pipe · Missing: https docs, excited, just released

58%58% 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: single, code, open · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

35%35% 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

29%29% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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