Kiboko to Sherehe(polite): From Strict Go Rules to JavaScript Chaos

Amon Ochuka — Thu, 07 May 2026 17:53:56 +0000

Growing up in a typical strict African household always had a very clear structure, well at least for me.
There were rules. Not suggestions. Not “best practices.” Rules. You never really negotiated them, you just executed them.

You woke up early. You went to school. You came back on time. You didn’t always get the chance to “try things.” You mostly followed instructions.

If you messed up, there was usually a very immediate feedback loop.
Let’s just call it… real-time debugging. ("Kiboko")

Now fast forward to adulthood (or campus life, which feels like a soft launch into adulthood).
Suddenly:

No one is asking where you are
You can sleep at 3AM and call it “freedom”
Food is no longer provided;it’s a budgeting problem
And somehow, people are going out on weekdays like it’s normal("sherehe polite")

It feels like moving from a tightly controlled environment into an open-world sandbox with no manual.

And that’s exactly how learning JavaScript felt after coming from Go.

Go felt like home

Go is structured, strict, and predictable.It feels like that kind of upbringing where:

There is a correct way to do things
Mistakes are immediately pointed out
You don’t get “creative freedom” with rules
Everything has a clear, disciplined path

If you write something wrong, Go doesn’t argue with you.
It simply refuses to proceed.
Very “strict parent energy.”

JavaScript feels like campus life

Suddenly, everything is allowed.
Almost too allowed.

You can write things that technically work but feel emotionally wrong:

"5" + 1 // "51"
"5" - 1 // 4

And the language just… accepts it.
No questions. No intervention. No moral guidance.(This is where I would have been properly debugged)

Coming from Go, it feels like moving from mama's hot home-cooked meals, to a hostel where someone is boiling noodles in a sufuria at 2AM, while watching YouTube at full volume.

It works.But you’re not always sure it should.

The adjustment phase

At first, JavaScript feels messy.
Too flexible. Too forgiving. Too many ways to do the same thing.
But slowly, something changes.
You realize the language isn’t enforcing discipline on you, you’re expected to build it yourself.

That’s the shift.Go gives you structure upfront.
JavaScript forces you to learn structure through experience.

One is guided discipline.The other is learned discipline

Final thoughts

Growing up strict teaches you order.Campus life teaches you independence.
Go gives you rules.
JavaScript gives you freedom, and occasionally lets you suffer for bad decisions.

And maybe that’s the real lesson:
Structure is important, but so is learning how to create it for yourself when no one is watching.

Why AI Is Unlikely To Be A Game-Changer In African Economies

Amon Ochuka — Tue, 17 Feb 2026 07:34:27 +0000

Everyone is talking about AI transforming Africa, but the reality on the ground is very different, especially in countries facing electricity shortages and monopolies in key sectors. While AI has enormous potential globally, in many African contexts it is unlikely to become the economic miracle some people hope for, at least in the near term.

Electricity Shortages Are a Major Bottleneck

AI systems, whether cloud-hosted or on-device, consume a significant amount of electricity. Training even a single large AI model can use as much power as a small town in a month. Many African countries struggle with unreliable grids and frequent load shedding. Even highly skilled developers cannot run AI consistently without stable electricity, and relying on generators or solar power adds both cost and operational complexity, making many AI projects financially unviable.

Infrastructure Needs Go Beyond Power

AI requires more than electricity. Fast internet, reliable data storage, cloud computing, and ongoing maintenance are essential. Many regions still lack basic connectivity, and data centers are often concentrated in a few major cities. This makes AI adoption uneven and prevents smaller startups from scaling their solutions effectively.

Monopolies Stifle Innovation

Key sectors like telecommunications, banking, and energy are dominated by a few large players. Small AI startups often struggle to access APIs or market opportunities, as monopolies prioritize existing profit models over risky new technologies.

Talent Gaps Limit Scale

AI requires skilled engineers and data scientists. While Africa has a growing developer community, high-level AI talent remains limited, and access to training and compute resources is uneven, slowing adoption.

Localized Opportunities Exist

AI can succeed where infrastructure is reliable, such as mobile money in Kenya or agri-tech in parts of Nigeria. These are notable exceptions, not the rule.

Conclusion