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FreeBSD Manager for Desktops (Via Ansible)

Hacker News

FreeBSD Manager for Desktops (Via Ansible)

I started working on the original playbook some time ago when needed to change my daily OS. Recently, I decided to convert it into a collection so I could replace source pulls and awkward templating with a simple one-liner solution. The result is `freebsd-collection`. Altho not exactly a one-liner since one needs to adjust user settings for sudo/doas before the execution, it feels like a massive upgrade compared to a classic playbook workflow. To achieve this, I implemented a simple profile system [1] that imports predefined settings according to the user’s needs. Right now it only pulls profiles (variables) from its source, but I feel that I need to add some offline capabilities as well (testing, follow-up runs, etc). The current version is still slightly opinionated as I continue to shave off the original layout (e.g. it would install `ohmyzsh` if `zsh` is detected) but it is ready for general GNOME/Sway/etc configurations. The example command will deploy my active configuration for Hyprland (start with `hyprstart`). [1] https://charlesrocket.github.io/essential-collection/remote_...

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3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
70%70% 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: user · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, 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
43%43% 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
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% 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.

Correct prediction on native model

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