| alias: pixelclock_spotify | |
| description: "" | |
| trigger: | |
| - platform: state | |
| entity_id: media_player.spotify_vistagamer | |
| condition: [] | |
| action: | |
| - choose: | |
| - conditions: | |
| - condition: state |
| alias: pixelclock_spotify | |
| description: "" | |
| trigger: | |
| - platform: state | |
| entity_id: media_player.spotify_vistagamer | |
| condition: [] | |
| action: | |
| - choose: | |
| - conditions: | |
| - condition: state |
| [Desktop Entry] | |
| Comment[en_US]= | |
| Comment= | |
| Exec=bash $HOME/xone_install_or_update.sh | |
| GenericName[en_US]= | |
| GenericName= | |
| Icon=preferences-desktop-gaming | |
| MimeType= | |
| Name[en_US]=Install⁄Update Xone | |
| Name=Install⁄Update Xone |
| import pandas as pd | |
| from math import sqrt | |
| from manim import * | |
| def create_model() -> tuple: | |
| data = list(pd.read_csv("https://bit.ly/2KF29Bd").itertuples()) | |
| m = ValueTracker(1.93939) | |
| b = ValueTracker(4.73333) | |
| ax = Axes( |
| #!/bin/bash | |
| #--------------------------------------------------------------------------------# | |
| # # | |
| # Fix WSL DNS resolution with Cisco AnyConnect # | |
| # # | |
| # ! Don't forget to set this configuration in /etc/wsl.conf: # | |
| # [network] # | |
| # generateResolvConf = false # | |
| # # | |
| # Based on: # |
| More recent resolution: | |
| 1. cd ~/../../etc (go to etc folder in WSL). | |
| 2. echo "[network]" | sudo tee wsl.conf (Create wsl.conf file and add the first line). | |
| 3. echo "generateResolvConf = false" | sudo tee -a wsl.conf (Append wsl.conf the next line). | |
| 4. wsl --terminate Debian (Terminate WSL in Windows cmd, in case is Ubuntu not Debian). | |
| 5. cd ~/../../etc (go to etc folder in WSL). | |
| 6. sudo rm -Rf resolv.conf (Delete the resolv.conf file). | |
| 7. In windows cmd, ps or terminal with the vpn connected do: Get-NetIPInterface or ipconfig /all for get the dns primary and | |
| secondary. |
- painlessMesh: https://gitlab.com/painlessMesh/painlessMesh
- easyMesh: https://github.com/Coopdis/easyMesh
- ESP-Mesh: https://www.espressif.com/en/products/software/esp-mesh/overview
- ESP8266 WiFi Mesh: https://github.com/esp8266/Arduino/tree/master/libraries/ESP8266WiFiMesh
For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.
After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft
Training TensorFlow models in C++
Python is the primary language in which TensorFlow models are typically developed and trained. TensorFlow does have bindings for other programming languages. These bindings have the low-level primitives that are required to build a more complete API, however, lack much of the higher-level API richness of the Python bindings, particularly for defining the model structure.
This file demonstrates taking a model (a TensorFlow graph) created by a Python program and running the training loop in C++.