Installing OpenBSD on any VPS via ipxe (128mb RAM, 1GB Disk)

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I made this guide and posted it on the LowEndSpirit.com VPS/Hosting community forum, a guide to installing OpenBSD on any VPS with a console and ipxe support (pretty much any KVM based host should support this now) even if they dont have any BSD templates, no dhcp required, static config method included and ipxe shell commands to get started. I did this in a tint 128mb ram vps with 1 vcpu and 1GB disk, hopefully it gets people thinking about doing and achieving more with less, the LowEndSpirit philosophy!

Visit product View on Hacker NewsMar 27, 2026

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% 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

21%21% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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