buffalo uses dominion scoreboard software so not really

DEAD PEOPLE SCORED FOR BUFFALO!
A truck delivered off a suitcase full of points at halftime from Canada for Buffalo.
#StopTheSteel !!!!
I’ll be submitting sworn affidavits from Steelers fans than they saw the Buffalo rigging the game but I want to emphasize that I’m not under oath.
The first 11 games of the Steelers season were fair. The past two were RIGGED!!!!
STEELERS ARE UNDEFEATED!!

🕧 This claim is disputed
STEELERS ARE 13-0!!!

🕧 Sources Have Called Differently
The only difference between my fight and the President’s is I haven’t lost 57 lawsuits, but you just watch me.
It’s on.
Look at that random point SURGE!!!

The NFL had that on its score dashboard but they TOOK IT DOWN!!

More from Software

Developer productivity, y'all. It is a three TRILLION dollar opportunity, per the stripe report.

Eng managers and directors, we have got to stop asking for "more headcount" and start treating this like the systems problem that it is. https://t.co/XJ0CkFdgiO


If you are getting barely more than 50% productivity out of your very expensive engineers, I can pretty much guarantee you cannot hire your way out of this resourcing issue. 😐

(the stripe report is here:

Say you've got a strategic initiative that 3 engineers to build and support it. Well, they're going to be swimming in the same muddy pipeline as everyone else at ~50%, so you're actually gotta source, hire and train 6, er make that 7 (gonna need another manager too now)...

...which actually understates the problem, because each person you add also adds friction and overhead to the system. Communication, coordination all get harder and processes get more complex and elaborate, etc.

So we could hire 7 people, or we could patch up our sociotechnical system to lose say only 25% productivity to tech debt, instead of 42%? 🤔

By my calculations, that would reclaim 3 engineers worth of capacity given a team of just 17-18 people.

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Nano Course On Python For Trading
==========================
Module 1

Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...

... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit
https://t.co/EZt0agsdlV

This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!

In Module 1 of this Nano course, we will learn about :

# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)

# Using Google Colab

Intro link is here on YT: https://t.co/MqMSDBaQri

Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb

You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want

# Importing Libraries

Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.