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Spotify Engineering Culture

This couple of videos helped me to understand how agility can be implemented into complex software development environments.

A lot of agility knowledge shared in these videos, and so many topics related to SAFe,

Video #1, this is more technical oriented, you will find things as agility, squads, continuous deployment, how teams are organized, events…

Video #2, this is more cultural oriented, where you will find the relationship with failure, innovation, hacking time, hacking events, innovation/predictability, chaos/bureaucracy

In video #2 there is an explanation about how to lead with chaos and bureaucracy that is really interesting (starts in minute 9:00), and it is represented by this picture:

 

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SAFe Scaled Agile Framework

Time to learn, time to read, now working on a deal where the customer is implementing their own version of SAFe.

There are a lot of documentation and intelligence on all of this, very interesting time learning about it.

Something I do not like is that is a closed community, I understand that organizations need ways to finance themselves, but to impose a training payment is a stopper for the expansion of these practices.

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Tradingeconomics.com

I was yesterday night navigating onto the different charts and data that are available in this web: https://tradingeconomics.com

The purpose is to define a set of macro indicators that enable me to contrast macro trends into a sector, so I can advance in general terms the trend of the sector or identify a divergence.

For instance, the manufacturing sector has a lot of dependency on:

  • Consumer expenditure.
  • Energy price (electricity, gas, Brent barrel…)
  • and Raw material prices.

Here I can check all this. The difficulty is going to define the right indicators to check, and simplify the amount of data to be reviewed.

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Tarificanator

¿qué plan de electricidad me conviene más?

Esta es una pregunta que muchos en España nos hacemos con el incremento progresivo de la tarifa de la luz.

Han surgido mucha competencia con los cambios legislativos y esta solución es muy interesante para responder la pregunta y para entender las variables que influyen en el precio final de la factura.

El Tarificanator, la solución para saber qué tarifa de la luz es mejor para tu casa: PVPC o mercado libre

El Tarificanator, la solución para saber qué tarifa de la luz es mejor para tu casa: PVPC o mercado libre

Este artículo, que merece varias lecturas para entender bien los detalles, contiene un enlace a una excel que ayuda a realizar el ejercicio y que ayuda a entender las variables que influyen.

Gracias a la consultora Ingebau por su publicación.

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Open-source Twitter Intelligence

I continue being curious about OSINT, and I found this project named Open-source Twitter Intelligence in GIT Hub .

It’s a set of code that do scraping on Twitter without using Twitter’s API, allowing you to scrape a user’s followers, following, Tweets and more.

Apparently Twitter’s API has some limits and this approach avoid them.

It’s written in Perl, so I cloned with PyCharm, I uploaded some libraries and I created a test.py file that enabled me to test it:

import twint

c = twint.Config()

c.Username = "joa_pen"
c.Custom["tweet"] = ["id"]
c.Custom["user"] = ["bio"]
c.Limit = 10
c.Store_csv = True
c.Output = "none"

twint.run.Search(c)

 

 

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Open-source intelligence, first try

Leyendo documentación sobre seguridad y tratando de entender como se ponen en práctica los conceptos de OSINT, encontré un vídeo sencillo de como usar EO-Ripper.py para rastrear correos electrónicos y sus contraseñas:

Me pareció curioso probarlo, de manera que encontré un hueco días más tarde.

Los pasos que di esta vez han sido:

  • Instalar python 3.6.
  • He instalado PyCharm (https://www.jetbrains.com/) en vez de Eclipse, así pruebo un entorno distinto.
  • Me pide instalar las siguientes librerías:
    • beautifulsoup4, que viene en PyCharm
    • mechanize: solo disponible para python 2.x
    • cssselect: que viene en PyCharm
    • cookielib, no la encuentro
  • Instalo GIT (https://git-scm.com/download/gui/windows) para poder clonar el proyecto.
  • Instalé el proyecto https://github.com/thibauts/duckduckgo,
  • He creado un fichero de test para el duckduckgo y funciona.
  • No se sido capaz de encontrar alternativas rápidas al hecho que el código está preparado para python 2.7 y he instalado python 3.6, con lo que he desistido.
  • Necesito encontrar más tiempo, lo aparco aquí.

 

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S&P Predictions

The beginning

On August 3rd 2018 I wrote about some behaviors of S&P. These behaviors and my desire to develop reports to understand trends at monthly level took me to draw this figure the same day:

How things happened in the calendar

At the end of August I took the decision that I was going to sell the majority of my positions. I did it.

In September I was astonished with the defiance to gravity of the market and a little bit pissed-off with the trend.

Now in October, specially in the second half of the month, I have seen how this has been evolving. The Q3 closing reports seemed to be the flutter of the butterfly that changed the trend.

The feeling

It’s just a graphic, it’s just a figure, it’s just a coincidence, but I’m happy about all I learned to be able to draw it.

Update November 23rd 2018