Dg

Dgs-CLI – 63-command CLI for D-Link DGS-1100 switches via Selenium

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Dgs-CLI – 63-command CLI for D-Link DGS-1100 switches via Selenium

The DGS-1100 is a ~$40 managed gigabit switch with real features (802.1Q VLANs, STP, IGMP, port security) but its only management interface is a JavaScript web UI from 2015. No SSH, no REST API, SNMP is read-only for anything useful. I needed to configure a few of these and clicking through the web UI was painful, so I wrote a CLI that drives headless Chrome via Selenium. It logs in, scrapes the JS data files the switch populates via XHR, and submits forms to the CGI endpoints for changes. Every write command reads back the config afterward to verify the switch accepted it. 63 commands covering everything the switch exposes — VLANs, PVIDs, STP, IGMP, storm control, port security, mirroring, QoS, bandwidth limits, SNMP, traffic segmentation, and more. Plus a recommend command that crawls 13 data files and checks 20 categories of issues (PVID mismatches, orphaned ports, disabled security features, etc.) and a fix command that auto-applies what it finds. Other things that fell out of this: config dump/diff/restore for version control, a template command to generate reproducible batch files, multi-switch support, a Prometheus exporter, a Textual TUI, and shell completions. Built with Nix so nix run github:bobberb/dgs-cli -- status just works. 179 offline tests. Tested on DGS-1100-08V2. The --1210 flag is stubbed out for future DGS-1210 support.

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
87%87% 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
71%71% 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
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, interface · Missing: platform, intuitive, reviews
35%35% 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
23%23% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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