Sylve – A management plane for FreeBSD (ZFS, VMs, jails, clustering)

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I’ve been working on Sylve, a management plane focused on making FreeBSD infrastructure easier to manage. It integrates closely with FreeBSD (Bhyve, ZFS, networking) and provides a UI + API for managing VMs, jails, and clusters across nodes. It features: * VM (Bhyve) and jail management * ZFS-based storage management (with Zelta for backups) * Multi-node clustering (based on Raft) * Runs in ~256MB RAM (even with clustering enabled) * Built-in web terminal (Ghostty/WASM) * Integrated networking (bridges, DHCP via dnsmasq) * Samba share management The backend is written in Go, and the frontend uses SvelteKit + TypeScript. Development of this project was supported by the FreeBSD Foundation. Quick install: fetch -o - https://sh.sylve.io | sh

Visit product View on Hacker NewsMar 27, 2026

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

72%72% 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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

61%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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

28%28% 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, revenue

11%11% 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

1%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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