-
Make sure you have
wget
,curl
,dpkg
,tar
,cgpt
andpv
installed on your system (useyay -S vboot-utils
to installcgpt
on arch). -
Download brunch
-
Run
bash brunch-toolkit-main.sh
(script from https://github.com/WesBosch/brunch-toolkit) and select "2) Compatibility Check". Note the codename (e.g. Rammus) then download the latest brunch release (the archive is saved inDownloads
directory !) -
If it didn't work, download the latest release from https://github.com/sebanc/brunch and save it in
Downloads
directory (no need to extract the archive).
-
Discover gists
***Pi-Mox setup on raspberry pi 4b (cm4 you will need add the appropriate steps for your hw setup) | |
***None of this is "Prod" ready so use at your own risk, your VM's/Containers are your own responsibility. You should already have adequate backups etc. | |
***Raspberry PI OS setup | |
Install raspbian x64 lite on raspberry pi | |
pull the latest copy of Raspberry PI OS x64 lite based on debian 11 bullseye from here: https://www.raspberrypi.com/software/operating-systems/#raspberry-pi-os-64-bit | |
open imager, click choose os, scroll to the bottom and select custom. open the image "2023-05-03-raspios-bullseye-arm64-lite.img.xz" |
Your company's GPU computing strategy is essential whether you engage in 3D visualization, machine learning, AI, or any other form of intensive computing.
There was a time when businesses had to wait for long periods of time while deep learning models were being trained and processed. Because it was time-consuming, costly, and created space and organization problems, it reduced their output.
This problem has been resolved in the most recent GPU designs. Because of their high parallel processing efficiency, they are well-suited for handling large calculations and speeding up the training of your AI models.
When it comes to deep learning, GPUs can speed up the training of neural networks by a factor of 250 compared to CPUs, and the latest generation of cloud GPUs is reshaping data science and other emerging technologies by delivering even greater performance at a lower cost and with the added benefits of easy scalability and rapid deployment.
I should preface this by saying that I got a Withings Smart Body Analyzer for Christmas last year and I’ve been generally happy with it. It purports to be able to take my heart rate through my bare feet and that seems not to work for my physiology, but overall I’m a fan. If if their Wikipedia page is to be believed they are having a pretty rad impact on making the Quantified Self movement more for normal people and they only have 20 full time employees. Also they try hard to use SI units, which I can get behind. Anyway, on to the rant.
I originally called this post “Everything wrong with the Withings API” and I meant it. For every useful field I can extract from their “award winning” app, I have spent an hour screaming at the inconsistencies in their implementation or inexplicable holes in their data
;; Auto-scrolling ============================================================== | |
(defn scroll! [el start end time] | |
(.play (goog.fx.dom.Scroll. el (clj->js start) (clj->js end) time))) | |
(defn scrolled-to-end? [el tolerance] | |
;; at-end?: element.scrollHeight - element.scrollTop === element.clientHeight | |
(> tolerance (- (.-scrollHeight el) (.-scrollTop el) (.-clientHeight el)))) | |
(defn autoscroll-list [{:keys [children class scroll?] :as opts}] |
(ns views.infinite-scroll | |
(:require | |
[reagent.core :as r])) | |
(defn- get-scroll-top [] | |
(if (exists? (.-pageYOffset js/window)) | |
(.-pageYOffset js/window) | |
(.-scrollTop (or (.-documentElement js/document) | |
(.-parentNode (.-body js/document)) | |
(.-body js/document))))) |
#!/bin/sh -e | |
start() { | |
cat "/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq" > "/sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq" | |
cat "/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq" > "/sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq" | |
} | |
stop() { | |
cat "/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_boot_freq" > "/sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq" | |
cat "/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_boot_freq" > "/sys/devices/system/cpu/cpu0/cpufreq/scaling_max_freq" |
NAME TITLE | |
abusiveexperiencereport.googleapis.com Abusive Experience Report API | |
acceleratedmobilepageurl.googleapis.com Accelerated Mobile Pages (AMP) URL API | |
accessapproval.googleapis.com Access Approval API | |
accesscontextmanager.googleapis.com Access Context Manager API | |
actions.googleapis.com Actions API | |
adexchangebuyer-json.googleapis.com Ad Exchange Buyer API | |
adexchangebuyer.googleapis.com Ad Exchange Buyer API II | |
adexchangeseller.googleapis.com Ad Exchange Seller API | |
adexperiencereport.googleapis.com Ad Experience Report API |
#from http://www.pyimagesearch.com/2015/03/30/accessing-the-raspberry-pi-camera-with-opencv-and-python/ | |
# import the necessary packages | |
from picamera.array import PiRGBArray | |
from picamera import PiCamera | |
import time | |
import cv2 | |
# initialize the camera and grab a reference to the raw camera capture | |
camera = PiCamera() |