seanjensengrey

class StackModule:
    def __init__(self):
        self.items = []

    def __repr__(self):
        return repr(self.items)

    def push(self, value):
        self.items.append(value)

seanjensengrey

Survey of Carrier Boards for the CM4 compute module

• https://www.raspberrypi.org/products/compute-module-4-io-board/
• https://hackaday.io/project/177626-mirkopc-cm4-carrier-board
• VERY COMPREHENSIVE https://pipci.jeffgeerling.com/boards_cm.html
• https://www.gumstix.com/community/blog/raspberry-pi-cm4-carrier-boards-from-gumstix/
• https://store.oratek.com/products/tofu
• creating your own carrier boards in kicad https://www.digikey.com/en/maker/projects/creating-a-raspberry-pi-compute-module-4-cm4-carrier-board-in-kicad/7812da347e5e409aa28d59ea2aaea490
• smallest carrier board https://www.cnx-software.com/2021/08/10/mincab-smallest-raspberry-pi-cm4-carrier-board/
• https://lim.loonix.ca/

seanjensengrey

source: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

The Bitter Lesson

Rich Sutton

March 13, 2019

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need n

seanjensengrey

apt-get remove docker docker-engine docker.io containerd runc
 
apt-get install \
  apt-transport-https \
  ca-certificates \
  curl \
  gnupg \
  lsb-release

seanjensengrey

Instance level metadata urls

Both AWS and GCP, probably Azure as well, offer the ability to query instance level metadata from within the guest (and container)

Both cloud use the same IP address (169.254.169.254) internally to handle the request.

tl;dr, do a get request against the metadata url and look at the response headers.

seanjensengrey

from https://github.com/detailyang/ipc_benchmark

make all
python2 run_test.py

Note that this compiles with -g but now opt level.

seanjensengrey

# https://stopa.io/post/243

from collections import Counter
import random

a = ['r'] * 99 + ['w']
b = ['w'] * 99 + ['r']
t = [a,b]

seanjensengrey

#! /bin/bash
# from https://www.facebook.com/notes/netways/wenn-das-macbook-zu-hei%C3%9F-ist-cpulimitsh/10150313173463753/
# Description:
# 
# If you want to decrease the CPU demands for an application
# you can use this very simple and ugly "hack". 
# I use it when I convert videos on my MacBook Pro
# to prevent it from getting to warm. Even if the CPU is IDLE this hack
# will prevent the application to use the CPU during Sleep Time
# (in contrast to renice/nice commands which will take all IDLE time). 

seanjensengrey

In [17]: (a, (b)) = (1, (2,3, ([4,5,6])))                                                                                                                     

In [18]: b                                                                                                                                                    
Out[18]: (2, 3, [4, 5, 6])

In [19]: (a, (b,e,(f))) = (1, (2,3, ([4,5,6])))                                                                                                               

In [20]: a                                                                                                                                                    
Out[20]: 1

seanjensengrey

$java -XX:+PrintFlagsFinal -version

[Global flags]
ccstrlist AOTLibrary                               =                                          {product} {default}
      int ActiveProcessorCount                     = -1                                       {product} {default}
    uintx AdaptiveSizeDecrementScaleFactor         = 4                                        {product} {default}
    uintx AdaptiveSizeMajorGCDecayTimeScale        = 10                                       {product} {default}
    uintx AdaptiveSizePolicyCollectionCostMargin   = 50                                       {product} {default}
 uintx AdaptiveSizePolicyInitializingSteps = 20 {product} {default}