Every PCB engineer using KiCad should know about CERN's open-source libraries.
CERN has released the same KiCad component libraries used by its electronics engineers for designing scientific instruments and research hardware.
Why it's worth using
• Production-quality symbols & footprints
• Auto-generated from CERN's Altium libraries
• Nightly updates
• Database-driven library management
• Compatible with KiCad 9 and KiCad 10
• Licensed under CERN Open Hardware Licence v2
Perfect for building:
• Embedded Systems
• FPGA Boards
• Robotics
• Industrial Electronics
• Research Hardware
• Custom PCBs
If you use KiCad, this is one of the highest-quality open-source component libraries you can add to your workflow.
GitLab: https://t.co/nzBv8SrA8T
Early in my career, I couldn't tell a good manager from a bad one. Now I know why: a good manager does most of their best work where you never see it.
What they're doing behind the scenes:
- Fighting for your team's time, budget, and people
- Pushing back on unreasonable demands from above
- Protecting you from the chaos so you can focus
- Letting you make implementation decisions, and protecting that ownership
- Letting you know what's coming up so nothing surprises you
- Investing in your growth even when it doesn't help them today
You usually only notice any of this once you have a manager who does none of it.
And it goes both ways. Part of your job is making the relationship work too.
"Basic Mathematics" by Serge Lang is an excellent book for readers who want to build solid mathematical foundations.
It covers algebra, geometry, trigonometry, complex numbers, matrices, and other introductory topics with clarity and rigor. It is a very useful resource for readers who are approaching advanced mathematics for the first time.
https://t.co/dQbi7mfokx
A freelance journalist who had never taken a statistics course wrote a 142-page book in 1954 that professional statisticians still hand to students before anything else, because nobody before him had bothered to explain the tricks in plain language.
His name was Darrell Huff. The book is called How to Lie with Statistics.
I read it in one sitting and spent the next three days noticing the tricks everywhere.
Over 1.5 million copies have sold in English alone. It became a standard college textbook in the 1960s and 70s. Seventy years later it is still in print, still assigned, still the first thing a working statistician reaches for when they want to teach someone to think clearly about numbers.
The man who wrote it was not a researcher. He was a freelancer who wrote how-to articles for magazines. He had no PhD, no academic post, no institutional affiliation. He just understood that numbers could lie without technically being wrong, and he thought someone should explain how.
His opening line sets the whole tone of the book.
"The crooks already know these tricks; honest men must learn them in self-defense."
That one sentence is the entire argument. The manipulation is not coming. It already happened. It happened this morning in the article you read and the chart someone showed you at work and the study your doctor quoted. The only question is whether you know what to look for.
Huff called the first trick the Well-Chosen Average.
When someone tells you the average salary at a company is $80,000, they have told you almost nothing. If the CEO earns $2 million and the 20 employees earn $30,000 each, the mean is $80,000. The median is $30,000. Both are technically correct. One is a lie. The person reporting the number chose which average to use, and they almost always chose the one that served their argument. Huff's rule: whenever you see an average with no description of which average it is, ask.
The second trick he named the Gee-Whiz Graph.
A line chart shows company profits rising. The line shoots nearly vertical, almost doubling in height across the chart. You feel impressed. Then you look at the y-axis and notice the chart does not start at zero. It starts at 94. The actual increase in profits was 3 percent. The dramatic visual was produced entirely by cropping the bottom of the chart. Nothing in the data changed. The picture changed everything.
Every news organization on earth still does this every day.
The third trick is the one that should change how you read every study you ever encounter. Huff called it Post Hoc Rides Again, which is short for the Latin phrase post hoc ergo propter hoc. After this, therefore because of this.
Cities with more churches have more violent crime. Therefore churches cause violence. The logic is airtight. The conclusion is absurd. Both church attendance and crime go up as population grows. The two numbers track each other because a third variable drives both. The correlation is real. The cause is invented.
Huff showed that this structure is not a rare mistake. It is the default pattern of almost every study reported in a newspaper, because causation is a boring word and because proves is a better headline than correlates with.
The fourth trick was the one that floored me. He called it the Semi-Attached Figure.
A headache pill company claims their product is twice as fast as the competition. The study behind the claim is real. The product was tested and the numbers are accurate. What the advertisement does not mention is that the study measured absorption rate into the bloodstream, not relief of headaches. The two things are related but not identical. The statistic is real. It is attached to the wrong conclusion.
Huff said this is the most dangerous trick of all because the number is never fabricated. You cannot fact-check a semi-attached figure by verifying the statistic. You have to ask whether the statistic actually measures what the claim requires it to measure.
Almost nobody asks.
There is one part of Huff's story that most people who recommend the book leave out.
Years after he wrote it, he was hired by the tobacco industry. He worked on a follow-up manuscript called How to Lie with Smoking Statistics, designed to cast doubt on the research connecting cigarettes to cancer. The book was never published. He testified before Congress in an attempt to undermine the statistical evidence against tobacco.
The man who wrote the clearest guide to spotting statistical deception spent the end of his career deploying those same tricks against evidence that was killing people.
That detail does not make the book wrong. The tricks he described are real and the defenses he taught are still the right ones. But it is a reminder that the tools in the book are neutral. Understanding how lies are built does not protect you from choosing to build one.
The crooks already know these tricks.
Some of them wrote the manual.
What is one statistic you have seen recently that you now think deserves a second look?
Thinking of starting a community for people who want to learn hardware, work in hardware, or just enjoy tinkering with things in general.
Reply to the tweet if you want in.