Joseph Pajos

AI ENGINEER | Fashion Designer | Sound Engineer | #Arsenal Fan

[Joseph Pajos @PajosTM]

I was in class last week. One of my students raised their hand mid-lecture and asked something that stopped me for a second.

"Why is every AI tool built on Python? C++ is faster. Rust is faster. Even Java is faster. So why Python?"

Honestly it’s a fair question. And the answer reveals something really interesting about how the AI industry actually works.
Let me explain this properly. 🧵

[Joseph Pajos @PajosTM]

@letsfindx_ Niceee..

[Joseph Pajos @PajosTM]

Quick question for data folks:
What is the key difference between NULL, an empty string, and None?
How should each be filtered correctly?

[Joseph Pajos @PajosTM]

@letsfindx_ Yea, it's kinda confusing.....

[Joseph Pajos @PajosTM]

A Nigerian startup says their average salary is ₦800,000.
Sounds great.
Then someone leaks the breakdown.
CEO - ₦5,000,000.
Co-founders - ₦3,000,000 each.
47 junior staff - ₦150,000 each.
The average was correct. But it was lying.
This is why mean, median and mode exist. 🧵

[Joseph Pajos @PajosTM]

@prosper_amed Yea. We need to know the difference between them and also when to use them.

[Joseph Pajos @PajosTM]

A Nigerian startup says their average salary is ₦800,000.
Sounds great.
Then someone leaks the breakdown.
CEO - ₦5,000,000.
Co-founders - ₦3,000,000 each.
47 junior staff - ₦150,000 each.
The average was correct. But it was lying.
This is why mean, median and mode exist. 🧵

[Joseph Pajos @PajosTM]

@prosper_amed It’s very very important

[Joseph Pajos @PajosTM]

Tip for the day:
Every Python object stores attributes in a dictionary by default.
Flexible. But memory heavy.
Create 100,000 instances and your crawl slows down fast.
One fix - slots

[Joseph Pajos @PajosTM]

A RAG system is only as good as its retrieval. If the wrong context goes in the wrong answer comes out.

[Joseph Pajos @PajosTM]

Black definitely.

[Thelma Etuk @officialladi_T]

Which workspace suits your personality?

White Or Black

[Qua @KareemQuadrio]

Documentation is underrated.

The engineer who documents today saves hours for everyone tomorrow.

[Femi The Analyst . @Femiforge]

Common mistakes in data analysis usually look small at first, but they quietly ruin your conclusions.

Here’s what I watch out for. When you skip proper cleaning, missing values, and random errors twist your results in ways you won’t notice until it’s too late.

When you start without a clear question, you end up with fancy charts that don’t answer anything useful.

When your visuals are confusing or exaggerated, people walk away with the wrong picture.

And when you trust data without checking its quality, every insight that follows becomes shaky.

these mistakes are avoidable, fix them to make your analysis sharper and your decisions stronger.

[Willie The SRE Guy @willie_sre]

Your Kubernetes deployment is failing.
You've checked the pods. Checked the logs. Everything looks fine.
But the service still can't communicate.
99% of the time, it's a networking issue nobody fully understood when they set up the cluster.
Here's how K8s networking actually works 🧵

[Joseph Pajos @PajosTM]

The startup average was not a lie.
It was a carefully chosen truth that hid a bigger truth.
Mean. Median. Mode.
Know all three. Know when to use each one. Know when someone is using the wrong one to mislead you.

[Joseph Pajos @PajosTM]

Back to the startup.
Mean salary - ₦800,000. Makes the company look generous.
Median salary - ₦150,000. Represents what the typical employee actually earns.
Mode salary - ₦150,000. Confirms that most employees earn the same amount.

Three numbers. Same dataset. Completely different stories. This is why data literacy matters.
The wrong summary statistic does not just mislead. It can be used deliberately to hide the truth.

Always ask: which measure of central tendency is being used here and why?

[Bonafide_cakes_n_events🌹❣️ @Tranquil_Nai]

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