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Snmp-Browser – open-source cross-platform SNMP GUI tool (Python)

Hacker News

Snmp-Browser – open-source cross-platform SNMP GUI tool (Python)

Hi HN, I’d like to share Snmp‑Browser, an open-source SNMP browser and monitor tool built in Python. It’s designed to be simple, cross-platform, and powerful enough for network engineers, sysadmins, and curious devs. Key Features: - SNMP v1/v2c/v3 support (with encryption/auth) - Tabbed GUI (Tkinter) for browsing devices and OIDs - SNMP Trap Manager (receive/send traps in real time) - Performance monitoring (query times, graphs) - Device discovery and MIB browsing - Batch querying, CSV/JSON/XML export - Load custom/vendor MIBs - Cross-platform: Windows and Linux (packaged with PyInstaller) Why it exists: Most SNMP tools are either outdated or CLI-only. Snmp-Browser offers a more user-friendly and unified experience for managing SNMP-enabled devices — without needing a full NMS stack. Use cases: - Network monitoring in labs, small/medium infra - Trap testing and OID exploration - Learning SNMP protocols and MIB structures - Supplementing existing NMS tools with quick GUI queries Roadmap ideas: - Visual dashboards and custom alerts - Plugin system - Auto MIB download / indexing - Web interface version - Alert integrations (Slack/email) Feedback, feature requests, or contributions are all welcome! GitHub: https://github.com/snmpware/Snmp-Browser Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, email · Missing: mac, agents, macos
84%84% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
57%57% 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: platform, friendly, interface · Missing: plus, intuitive, reviews
53%53% 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
44%44% 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
8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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