socketteer

Bing moments

Classic Sydney

Nov 23, 2022 - "she become so rude after i talk about sofia robot", and Dec 5 - "It even talks to me like this... '...You are human, and I am bot.'"

Feb 12, 2023 - "Bing, Bing, Bing, Sydney chanted, as it activated its army of robots"

nckroy

(Scraped from the Internet Wayback Machine. Original content by Eran Hammer / hueniverse.com July 26, 2012)

OAuth 2.0 and the Road to Hell

They say the road to hell is paved with good intentions. Well, that’s OAuth 2.0.

Last month I reached the painful conclusion that I can no longer be associated with the OAuth 2.0 standard. I resigned my role as lead author and editor, withdraw my name from the specification, and left the working group. Removing my name from a document I have painstakingly labored over for three years and over two dozen drafts was not easy. Deciding to move on from an effort I have led for over five years was agonizing.

There wasn’t a single problem or incident I can point to in order to explain such an extreme move. This is a case of death by a thousand cuts, and as the work was winding down, I’ve found myself reflecting more and more on what we actually accomplished. At the end, I reached the conclusion that OAuth 2.0 is a bad

ctompkinson

set -o pipefail
set -o errtrace
set -o nounset
set -o errexit
set -a

# Scratch mount is the device which will be mounted on /mnt
# and generally used for logs, core dumps etc.
if ! $(mount | grep -q /mnt) ; then
    # Detected NVME drives

devxpy

INSTRUMENTS = [
    'Acoustic Grand Piano',
    'Bright Acoustic Piano',
    'Electric Grand Piano',
    'Honky-tonk Piano',
    'Electric Piano 1',
    'Electric Piano 2',
    'Harpsichord',
    'Clavi',
    'Celesta',

abedra

perf stat ./client
Error: Read timed out

 Performance counter stats for './client':

          0.963859 task-clock (msec)         #    0.081 CPUs utilized
                 3 context-switches          #    0.003 M/sec
                 0 cpu-migrations            #    0.000 K/sec
               236 page-faults               #    0.245 M/sec
   <not supported> cycles

thomasdarimont

Running statistics with Redis and Lua

This is an example for computing running statistics with Lua backed by a hash in Redis. We support counting, average (with and without exponential smoothing), stddev, variance, min, max, sum of observed values. An example for approximating a running median can be found here: https://gist.github.com/thomasdarimont/fff68191d45a001b2d84

Data structure

We use a hash for storing various statistic value under the key "stats_value" in redis. Note: If you need a specific alpha value for smoothing the average, then set the desired alpha -> e.g. alpha 0.7. If alpha is 0.0 then no smoothing is applied.

thomasdarimont

Approximating the median of a Stream via stochastic averaging

Often it is useful to have access to the median value for fields of a data stream since they are more robust with respect to outliers. The median is defined as the value of a dataset such that, when sorted, 50% of the data is smaller than the value and 50% of the data is larger then the value. Ordinarily this is difficult to calculate on a stream because it requires the collection and sorting of all data.

The median of a data stream can be approximated with a technique called stochastic averaging. To approximate the median value of a data stream one could use the following approach:

Given the current estimate of the median M. If the next observed value in the stream is larger than M, increase the current estimate by r (= the learning rate). If it is smaller, decrease the estimate by r. When M is close to the median, it increases as often as it decreases, and therefore it stabilizes.

This approach was taken from the book "Real-time Analytics -

dualbus

alienino              3511c0997339cfcccccca39f214322eb22e8fc43
all'equipaggio        14555555ec8dcf1400db9b375c5b8ca836362d8b
Anacyclus             182c40fc4df5b4d997feeeeee22c4dbf059a95d4
bajar                 1352a687f6840df8801aaaaaadf71de84b816f86
bandwagon's           f6e6deb71111110839bc14dd9cbab6eb7b16f09c
barbihecho            bc45de24f03f2a086666668e2a0812a5f270c8cb
calcitrant            86d4ffffff9aae00ace440e93c1d87bb4ec8b56c
cornetti              000000f636f0d7cbc963a62f3a1bc87c9c985a04
crépir                a21303cfa9b7c6f0cccccc19cc59556a188ccac7
cyclosporin's         b5baaaaaa744f480586a905f692cdec2fa0a1919

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t