Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
#!/bin/bash | |
gdb -p "$1" -batch -ex 'set {short}$rip = 0x050f' -ex 'set $rax=231' -ex 'set $rdi=0' -ex 'cont' |
After looking for alternatves to the suggested Router from Telekom (AVM FritzBox and HUawei Speedport), I've discovered the possibility of configuring my existing OpenWRT Router to act as gateway to the Telekom FTTH (Fiber To The Home) Magenta Zuhause package.
The WAN interface must be configured as follows (see your Telekom letter):
PPPoE
Note: This gist may be outdated, thanks to all contributors in comments.
adb
is the Android CLI tool with which you can interact with your android device, from your PC
You must enable developer mode (tap 7 times on the build version in parameters) and install adb on your PC.
Don't hesitate to read comments, there is useful tips, thanks guys for this !
#!/bin/bash | |
# inspired by a part of: https://github.com/cjweeks/tensorflow-cmake/blob/master/build.sh | |
# | |
# Assumes Tensorflow libraries libtensorflow_framework.so and libtensorflow_cc.so were built with Bazel, | |
# and the ${TF_ROOT}/tensorflow/contrib/makefile/download_dependencies.sh script was executed. | |
set -o nounset | |
set -o errexit | |
set -o pipefail |
A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.
This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea
#include <stdexcept> | |
#include "xtensor/xarray.hpp" | |
#include "xtensor/xio.hpp" | |
#include "xtensor/xbuffer_adaptor.hpp" | |
#include "Eigen/Dense" | |
auto to_xarray(Eigen::VectorXd& in) { | |
using buf = xt::xbuffer_adaptor<double>; | |
using xbuf_arr = xt::xarray_container<buf, xt::layout_type::column_major, std::vector<std::size_t>>; |