Sn

Snmpy – A Pure Python SNMP Library with Zero Dependencies

Hacker News

Snmpy – A Pure Python SNMP Library with Zero Dependencies

Hi HN, I’m the creator of snmpy – a lightweight, pure Python library for SNMP v1/v2c/v3. It supports GET, SET, WALK, traps, and SNMPv3 authentication and encryption. There's even built-in support for UPS monitoring (battery, voltage, etc.). It’s built from scratch with zero dependencies (not even PySNMP or net-snmp), and is meant for developers who want to integrate SNMP monitoring into their tools or scripts without relying on native libraries or C extensions. Just `pip install snmpy` and you're ready. The CLI is also included for quick usage. The code is clean, readable, and fully open-source under the MIT license. I'd love your feedback — feature requests, use cases, missing things, or even tough critiques. GitHub: https://github.com/snmpware/snmpy Thanks!

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
67%67% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% 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
19%19% 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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