All packages, except for Tini have been added to termux-root. To install them, simply pkg install root-repo && pkg install docker. This will install the whole docker suite, left only Tini to be compiled manually.
FreddieOliveira
/ docker.md
Last active
May 18, 2024 11:17
This tutorial shows how to run docker natively on Android, without VMs and chroot.
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
probonopd
/ Wayland.md
Last active
May 18, 2024 04:21
Think twice about Wayland. It breaks everything!
Hence, if you are interested in existing applications to "just work" without the need for adjustments, then you may be better off avoiding Wayland.
Wayland solves no issues I have but breaks almost everything I need. Even the most basic, most simple things (like xkill) - in this case with no obvious replacement. And usually it stays broken, because the Wayland folks mostly seem to care about Automotive, Gnome, maybe KDE - and alienating everyone else (e.g., people using just an X11 window manager or something like GNUstep) in the process.
Wayland proponents make it seem like Wayland is "the successor" of Xorg, when in fact it is not. It is merely an incompatible alternative, and not even one that has (nor wants to have) feature parity (missing features). And unlike X11 (the X Window System), Wayland protocol designers actively avoid the concept of "windows" (making up incompr
(C) 2015 by Derek Hunziker, (C) 2017 by AppsOn
As of releasing MongoDB 3.4 and C# Driver v2.4, original cheatsheet by Derek is outdated. In addition, it has some deficiencies like connecting to MongoDB, creating indexes, etc. This updated version works fine with C# Driver v2.4.7 and MongoDB v3.4.
Note: Defined models and collections will be used in entire cheatsheet.
17 Sep 2014 - This is a post on my blog.
MapReduce is a powerful algorithm for processing large sets of data in a distributed, parallel manner. It has proven very popular for many data processing tasks, particularly using the open source Hadoop implementation.
The most basic idea powering MapReduce is to break large data sets into smaller chunks, which are then processed separately (in parallel). The results of the chunk processing are then collected.
