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Group directories first with modified macOS `ls`
Hacker NewsGroup directories first with modified macOS `ls`
I finally got tired of not having directories first on macOS, and something like exa < https://the.exa.website/ > seemed overkill. So I dug into the source code, hunted down the missing BSD-licensed headers, figured out how to work around the APSL headers (reverse engineering is documented in the comments in print.c), and smoothed out some rough edges. Very much a personal itch-scratching exercise, but I think this is something that bugs a lot of people, so hopefully it's useful!
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2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, code · Missing: agents, agent, cursor86%86% 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 · Missing: https docs, excited, just released61%61% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive33%33% 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, revenue15%15% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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