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Making GNU Make a better Task Runner

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Making GNU Make a better Task Runner

I know this could be considered blasphemous, but I constantly find myself using Make as a task runner. I have written my own task runner in the past, but somehow I always end up using make. I went, and I put together 3 quality of life snippets I use all the time and put it in a single makext.mk file that can be included in other Makefiles and wrote a basic readme for it. This is not meant to be a replacement for other task runners, but I do think it can be useful to some of you. https://github.com/mitjafelicijan/makext Check it out and see if it makes sense to you. Thanks for any feedback or comments. Cheers

Visit productView on Hacker NewsMay 12, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using · Missing: mac, agents, macos
77%77% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
34%34% 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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