Yong yongen9696

Here is the best setup (I think so :D) for K-series Keychron keyboards on Linux.

Note: many newer Keychron keyboards use QMK as firmware and most tips here do not apply to them. Maybe the ones related to Bluetooth can be useful, but everything related to Apple's keyboard module (hid_apple) on Linux, won't work. As far as I know, all QMK-based boards use the hid_generic module instead. Examples of QMK-based boards are: Q, Q-Pro, V, K-Pro, etc.

Most of these commands have been tested on Ubuntu 20.04 and should also work on most Debian-based distributions. If a command happens not to work for you, take a look in the comment section.

Make Fn + F-keys work (NOT FOR QMK-BASED BOARDS)

Older Keychron keyboards (those not based on QMK) use the hid_apple driver on Linux, even in the Windows/Android mode, both in Bluetooth and Wired modes.

Tips and Tricks for Jetson AGX Xavier

This document contains useful software and links to documentation that we have found to be useful when working with Jetson AGX Xavier.

Copied from: https://git.its.aau.dk/WW82ZE/docs_xavier/src/branch/master

Links

Nvidia Developer forum Jetson AGX Xavier topic

https://forums.developer.nvidia.com/c/agx-autonomous-machines/jetson-embedded-systems/jetson-agx-xavier/75

Jetson Community Projects

NONE OF THESE CLIENTS ARE VERIFIED BY ME FOR SECURITY OR ANYTHING ELSE! USE AT YOUR OWN RISK!

Update 2025-07-16: Use either Revanced, NewPipe or Firefox with add-ons. Revanced is my personal pick, due to it being a mod of the official App like Vanced, but better (e.g. it has integration of Sponsor Block, Return YouTube Disklike and more).

I wouldn't recommend Kiwi Browser anymore as it has been discontinued. AFAIK it has been accuired by Microsoft and the extension support has been merged into Edge (Canary). But you'd have to use Edge. Yuck.

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	import pandas as pd
	import numpy as np
	from sklearn.linear_model import LinearRegression
	import matplotlib.pyplot as plt

	df = np.load('archive/olivetti_faces.npy')

	df = df.reshape(400, 64*64)

	y = np.random.randn(40000).reshape(400,100)