CLI text processing with GNU sed (eBook)

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Hello! I am pleased to announce a new version of my "CLI text processing with GNU sed" ebook. This book heavily leans on examples to present features one by one. In addition to sed commands and options, regular expressions are also discussed in detail. Links: * PDF/EPUB versions: https://learnbyexample.gumroad.com/l/gnu_sed (free for a few days) * Web version: https://learnbyexample.github.io/learn_gnused/ (always free) * Markdown source, example files, etc: https://github.com/learnbyexample/learn_gnused * Interactive TUI app for exercises: https://github.com/learnbyexample/TUI-apps/blob/main/SedExercises I would highly appreciate it if you'd let me know how you felt about this book. It could be anything from a simple thank you, pointing out a typo, mistakes in code snippets, which aspects of the book worked for you (or didn't!) and so on. Reader feedback is essential and especially so for self-published authors. Happy learning :)

Visit product View on Hacker NewsJun 29, 2023

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

70%70% 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

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, new, code · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% 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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