Fail2zig. A single-binary fail2ban replacement written in Zig

Hacker News

Built fail2zig because I like fail2ban's log-to-ban model but wanted a smaller runtime with explicit limits on the work attacker controlled input can cause. fail2zig is a single static binary with bounded parsers instead of a regex engine, SQLite-backed durable state, and nftables enforcement directly through netlink. Also has the ability to inspect and migrate existing fail2ban configurations. I just release 0.4.0 today which is the point where I'm more comfortable putting it in front of more people. Source: https://github.com/ul0gic/fail2zig Wouldn't mind feedback from people running fail2ban today, including reasons you would not switch.

Visit product View on Hacker NewsSep 16, 2026

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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

87%87% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, single · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

18%18% 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.

Incorrect prediction on native model

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