This is a quick-and-dirty guide to setting up a Raspberry Pi as a "router on a stick" to PrivateInternetAccess VPN.
Install Raspbian Jessie (2016-05-27-raspbian-jessie.img) to your Pi's sdcard.
Use the Raspberry Pi Configuration tool or sudo raspi-config to:
| -- inspired by https://github.com/hackhitchin/esp8266-co-uk/blob/master/tutorials/introduction-to-gpio-api.md | |
| -- and http://www.esp8266.com/viewtopic.php?f=24&t=4833&start=5#p29127 | |
| local pin = 4 --> GPIO2 | |
| function debounce (func) | |
| local last = 0 | |
| local delay = 50000 -- 50ms * 1000 as tmr.now() has μs resolution | |
| return function (...) | |
| local now = tmr.now() |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| from keras.utils.io_utils import HDF5Matrix | |
| import numpy as np | |
| def create_dataset(): | |
| import h5py | |
| X = np.random.randn(200,10).astype('float32') | |
| y = np.random.randint(0, 2, size=(200,1)) | |
| f = h5py.File('test.h5', 'w') |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
This is a ServiceWorker template to turn small github pages into offline ready app.
Whenever I make small tools & toys, I create github repo and make a demo page using github pages (like this one).
Often these "apps" are just an index.html file with all the nessesary CSS and JavaScript in it (or maybe 2-3 html/css/js files). I wanted to cache these files so that I can access my tools offline as well.
Make sure your github pages have HTTPS enforced, you can check Settings > GitHub Pages > Enforce HTTPS of your repository.
Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:
docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface
| Character code (character) | Duration (ms) | Alias to | Pronounced as |
|---|---|---|---|
| 27 (�) | 656 | None | Escape |
| 33 (!) | 1333 | None | Exclamation mark |
| 35 (#) | 409 | None | Hash |
| 36 ($) | 425 | None | Dollar |
| 37 (%) | 631 | None | Percent |
| 38 (&) | 329 | None | And |
| 43 (+) | 396 | None | Plus |
| 45 (-) | 706 | None | Dash |
| $remoteport = bash.exe -c "ifconfig eth0 | grep 'inet '" | |
| $found = $remoteport -match '\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}'; | |
| if( $found ){ | |
| $remoteport = $matches[0]; | |
| } else{ | |
| echo "The Script Exited, the ip address of WSL 2 cannot be found"; | |
| exit; | |
| } |
