Us

Usbatlas – simple CLI utility to see what USBIP devices are attached

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Usbatlas – simple CLI utility to see what USBIP devices are attached

Hello HN, This is a simple CLI that I vibe coded to scratch an itch at work. The problem we had was multiple USBIP devices attached to a single Linux server, and we were having a hard time seeing who connected what. Enter `usbatlas`. It's a basic app that lives on top of the Linux device file system and depends on `usbip` [0][1]. It allows you to see what USB devices are attached to your Linux box and where they come from. Currently the device source IP resolution has some issues. I'm working out the kinks right now. Here's an example output: $ usbatlas --usbip xHCI Host Controller [serial: xxxx:xx:xx.x] +- USB2744 +- Lenovo New Calliope USB Keyboard +- /dev/hidraw0 +- /dev/hidraw1 +- /dev/input/event3 +- /dev/input/event4 +- /dev/input/js0 +- Lenovo USB Optical Mouse +- /dev/hidraw2 +- /dev/input/event5 +- /dev/input/mouse0 +- Bluetooth 9460/9560 Jefferson Peak (JfP) +- USB2.1 Hub +- USB2.1 Hub +- Saleae Logic +- FT4232H MiniModule [serial: xxxxxxxx] +- /dev/ttyUSB4 +- /dev/ttyUSB6 +- /dev/ttyUSB7 +- /dev/ttyUSB8 +- USB2.1 Hub +- USB2.1 Hub +- FT4232H MiniModule [serial: xxxxxxxx] +- /dev/ttyUSB0 +- /dev/ttyUSB1 +- /dev/ttyUSB2 +- /dev/ttyUSB3 xHCI Host Controller [serial: xxxx:xx:xx.x] +- USB3.1 Hub +- USB3.1 Hub +- USB3.1 Hub +- USB3.1 Hub +- J-Trace PRO [serial: xxxxxxxx] USB/IP Virtual Host Controller [serial: xxxxxxxx] +- FT4232H MiniModule [serial: xxxxxxxx] [usbip] +- /dev/ttyUSB9 +- /dev/ttyUSB10 +- /dev/ttyUSB11 +- /dev/ttyUSB12 +- FT232R USB UART [serial: xxxxxxxx] [usbip] +- /dev/ttyUSB13 +- FT4232H MiniModule [serial: xxxxxxxx] [usbip] +- /dev/ttyUSB14 +- /dev/ttyUSB15 +- /dev/ttyUSB16 +- /dev/ttyUSB17 USB/IP Virtual Host Controller [serial: xxxxxxxx] [0] https://github.com/dorssel/usbipd-win [1] https://wiki.archlinux.org/title/USB/IP

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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.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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: new, single, code · Missing: mac, agents, macos
54%54% 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
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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