Peak Automation

During a walk with friends yesterday, we got onto the topic of DevOps automation (don’t worry, I don’t bore all my friends with tales of CI/CD). This particular friend does work in IT consultancy, so we have a common interest.

I was talking about how code commits can trigger automated deployments, environment provisioning, regression tests and so on and (if you’re feeling brave) even deploy to production.

Today this friend sent me a link to this video. I must have explained automation well, this is spot on!

Online Morse decoder

I’ve been working on the Communicator badge with my Scout Group and one of the tasks that we’ve been working on is Morse Code. I found a great ad-supported site called Morse Code Translator | Morse Decoder which allows you to press dashes and dots into one window and get an immediate text translation in the other window. It also works in the other direction.

Here’s a screenshot showing it in action.
On the left you type in text and the Morse code appears on the right.
Alternatively, you can type dots and dashes on the right and see plain text appear on the left hand side.

Morse decoder screenshot
Morse decoder screenshot

To make sure that my Scouts don’t see inappropriate ads, I’ve hosted an iframe containing this code that I can use in my next meeting. The plan is for Scouts to send Morse using little LED flashers that we’re making, whilst other Scouts key in the dots and dashes that they see into the decoder. My “version” of this decoder is here: Morse Decoder –

The LED “flashers” are made with a cheap Lithium Cell and LED and a bit of sellotape to act as an insulator and hold the LED against the bottom of the cell. You need to bend the wire slightly so that it only touches the top of the cell when you press it.

Total cost per “flasher” is less than 15p per Scout (I bought 500 LEDs for £7.99 and 50 CR2032 button cells for £6.47 from Amazon.

Other useful links:
Make an Easy and Extreme LED Throwies | Make: (
How to Make Batteries From Spare Change | Instructables

Lies, damn lies and statistics

The world seems to be full of daft theories based on poor statistical analysis at the moment. In a chaotic world, humans have evolved to see and recognise patterns where they exist. Patterns and order rarely appear by chance and as we have evolved we’ve become really good at recognising order amongst disorder and deducing correlations that help us to make sense of the world around us.


Most of the world is “confined to home” due to COVID-19 at the moment and regular news sources are proving to be unreliable. News sources are increasingly driven by their own agendas and clickbait and sensationalist headlines are competing for the attention of an increasingly sceptical audience and as a result people are looking further afield for information. People are spending more time than normal browsing FaceBook and other social media and in this environment, disinformation spreads.

Add into this the fact that different groups of people are playing the blame game and as a result, those involved are keen to shift the blame elsewhere. The WHO hasn’t come out of this well and they face allegations that they may have colluded with China to downplay the severity of COVID-19 in January. Brexiteers are keen to highlight the EU’s inability to force nation states to work together in a crisis. Remainers are keen to highlight any failing of the government and use it to delay Brexit. The US is blaming China and in the meantime China is trying to draw the world’s attention away from their wet markets, biowarfare research and death figures.

Every faction is using every statistic that they can to “prove” that they are correct. In doing so, they are demonstrating confirmation bias by latching onto every theory or piece of information that appears to support their theory and discounting contradictory information.

Venn diagram showing confirmation bias
We undervalue what the facts say and overvalue anything that confirms our beliefs

All of the above is causing ridiculous theories to to circulate unchecked online. All that is needed is an audience that “wants/needs” to believe, a medium to spread information and that pre-disposition that we all have to seek patterns.

The other day I was perusing Twitter (no doubt seeking to confirm my own confirmation biases) when I came across this interesting Thread :

The UK population is about 66.5 million
The number of people who work in the NHS is about 1.5 million Therefore the number of people who don’t is about 65 million
The number of people who have died with COVID-19 is currently 10,612. 37 of those people worked in the NHS.

Therefore 10,575 people who do not work in the NHS have died. 10,575 of the 65 million who do not work for the NHS = 0.02%
37 of the 1.5 million who do work in the NHS = 0.002%
So what’s the bogus conclusion we can draw here?


That’s right. If you fail to consider any variables or wider context, it looks to be that not working for the NHS is 10x more dangerous than working for the NHS. Of course, this is complete nonsense. Fact is the demographics most likely to die are unlikely to work for the NHS.

RockboltG (via Twitter)

It’s a humorous look at how statistics can be warped to suit whichever position you want to adopt in an argument. Similar claims are made when comparing morbidity rates in dissimilar countries. Variations in demographics, urban vs rural population, relative age of population and population density all have an effect.

For those of you who are desperate to leap to some “interesting” conclusions drawn from statistics, I’d like to recommend Tyler Vigen’s “spurious correlations” website.

For example, who’d have guessed that per capita cheese consumption in America appears to correlate with the number of people who die annually through “bed sheet entanglement”?

In the meantime, enjoy your spurious correlations and conspiracy theories and remember, keep fact checking.