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I made a utility I named pmake to generate project files

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I made a utility I named pmake to generate project files

My code works but it could probably be improved. Something I'm worried about is this part of the code printf ' ;;; Directory Local Variables -*- no-byte-compile: t -*- ;;; For more information see (info "(emacs) Directory Variables") (setq flycheck-gcc-include-path "'$indir'") ' > .dir-locals.el It generates a .dir-locals.el file that forces flycheck (an emacs package) to look inside the include directory inside the project folder. I'm a little worried about whether setq is the right approach. Any help would be appreciated!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, code · Missing: agents, macos, agent
61%61% 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
52%52% 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: ide, 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.
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 · Missing: mobile apps, ios, personal
25%25% 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
13%13% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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