I

I wrote mangl – An Enhanced Manpage Viewer

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I wrote mangl – An Enhanced Manpage Viewer

Hi! I wrote mangl - an enhanced manpage viewer with a GUI and clickable links, manpage search, search on manpage, history etc. The motivation was to replace opening a manpage in the browser since I frequently look up functions during programming. The standard text based manpage viewer 'man' did not provide all the necessary features I wanted. Currently the program is pretty feature complete and has been used on various linux distributions. For manpage interpretation it uses the mandoc library which comes from the OpenBSD project. For windowing an graphics I used GLFW and OpenGL. Building is easy - described on github. Please try it if you're interested in such software. My goal is to get it accepted into a few linux distributions as a package. Thanks for your attention.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
38%38% 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
33%33% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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