𝓚𝓸𝓴𝓸

✨ sustaining flow with gamified productivity @forgewaredev • CS & Linguistics • learning math for ML • 🌾 interested in offworld industries, agtech, biotech

[𝓚𝓸𝓴𝓸 @DevKokooo]

Offworld economies and industries are about to go wild

[Seth Bannon @sethbannon]

NASA just quietly published something incredible.

A map of how we build a permanent human presence off Earth.

It’s called the Moon Base User’s Guide.

Here's what's in it! 🧵

[𝓚𝓸𝓴𝓸 @DevKokooo]

Treat programming less like a craft and more like math that you can formally prove before you run it

First principles thinking > reasoning by analogy

An analogy is a transfer of a model from one context to another, with hidden assumptions, and without full validation of the conditions that made the original model work in the first place

"Google interviewed candidates like this, so we're going to do the same too" has created a whole industry of leetcode-ification that didn't prove actual skills on the job

"Forbes 100 company has these best practices" led to cargo cult programming mentality, assuming that copying those same practices would bring the same success

"We're building Uber for dogs and AI for your washing machine" -- wtf does this even mean?

It's the same trap I fell into years ago with Clean Code architecture

We have to remember that "best practices" is not law, is not religion, and everything depends on the context of your project / company

[Millie Marconi @MillieMarconnni]

A Dutch computer scientist gave one lecture in 1988 arguing that programming is unlike anything humans have ever tried to do before, and the reason most software on earth is broken is that we are still teaching it as if it were a hobby.

His name was Edsger Dijkstra. He won the Turing Award in 1972. He invented the shortest path algorithm that every GPS on earth still runs on.

He wrote the paper that killed the goto statement in modern programming languages.

He spent 50 years quietly being one of the most consequential thinkers in the entire history of computer science, and he was in a very bad mood by the time he stood up at the ACM Computer Science Conference in 1988 to deliver the lecture that almost nobody at the conference wanted to hear.

The lecture was called On the Cruelty of Really Teaching Computer Science.

It is now one of the most cited papers in the entire history of computing education. It was filed in his archive as EWD1036, handwritten in his careful fountain-pen calligraphy because he refused to use a typewriter and famously refused to use email for the rest of his life.

The argument was simple and uncomfortable.

Programming, Dijkstra said, is a radical novelty. Not a new tool. Not a new skill. Not a faster version of something humans already knew how to do. A genuinely new category of intellectual activity that has no real precedent in the entire history of the human species, and our brains have not been built to handle it.

Here is what he meant by that.

When a programmer writes a line of high-level code and presses run, that single line might trigger a billion operations at the level of the silicon.

The ratio between the abstraction you are working in and the physical events you are actually causing is roughly one billion to one. No engineer in history before computing ever had to reason about a system spanning that kind of ratio inside their own head.

A bridge builder reasons about steel beams and the physics of weight. A surgeon reasons about organs and the physics of tissue. A chemist reasons about molecules and the physics of bonds.

All of them are working inside ratios of physical scale where the largest and smallest things they need to think about are within a few orders of magnitude of each other.

A programmer routinely writes one line that orchestrates a billion physical events on a chip, and is expected to predict the behavior of all of them in advance.

Dijkstra argued that the human brain was simply not built for this. Every intuition we have evolved over hundreds of thousands of years comes from a world of medium-sized objects behaving in continuous ways. Computing is the opposite. It is discrete, not continuous.

A program that runs perfectly a billion times can crash on the billion-and-first iteration because of a single bit. A single character missing from a line of code can take down a power grid. There is no margin. There is no graceful degradation. The system either works or does not, and the only way to know is to actually run it.

This was the part of the lecture where Dijkstra made everyone in the room uncomfortable.

He said the way computer science was being taught in universities was a quiet disaster. Professors were teaching programming the way carpenters teach woodworking. With examples. With metaphors. With analogies to things students already understood. Files are like folders. Memory is like a desk. A function is like a recipe.

Dijkstra said this was actively making it harder for students to think clearly. The whole point of a radical novelty is that there is nothing in your past experience to compare it to.

The moment you start reaching for metaphors, you are smuggling in old intuitions that do not apply, and those intuitions will betray you the first time you try to reason about a system the metaphor was not built to describe.

His exact line was this: the usual way in which we plan today for tomorrow is in yesterday's vocabulary. And yesterday's vocabulary, he argued, was killing the field.

The reason most software is broken is downstream of this single misunderstanding. Programmers are taught to think of code as a craft. Something you get a feel for.

Something you pick up through practice. Something where intuition gets sharper with experience.

Dijkstra said this is exactly backwards. Programming is not a craft. It is closer to mathematics than to carpentry, and the moment you treat it as a craft, you guarantee that the software you produce will be full of the kind of bugs that craftsmanship cannot catch.

The fix, in his view, was to teach programming the way mathematics is taught. You should be able to prove your program correct before you run it.

You should reason about your code formally, the way a mathematician reasons about a theorem, not the way a carpenter feels their way through a joint. The students who learned this way, he said, would walk out of their classes with a kind of confidence that no amount of typing practice could produce.

The lecture was published in Communications of the ACM in 1989. The field did not listen. Universities kept teaching programming the same way.

Software kept getting bigger. Bugs kept compounding. By 2026, almost every piece of software on earth has known security vulnerabilities, undefined behaviors, and edge cases that nobody has ever proven safe. The doom that Dijkstra warned about in 1988 is now the default condition of the digital world we have built.

The deeper lesson is the one most readers miss the first time through.

Dijkstra was not just talking about software. He was making a much bigger point about how humans learn anything that is genuinely new. The instinct to translate the unfamiliar into the familiar is the most natural thing in the world.

It is also the single biggest obstacle to actually understanding something that has no precedent. If you keep reaching for analogies, you will never see the new thing clearly. You will only see your old framework projected onto it.

This is happening right now with AI. The same instinct that made people learn programming through metaphors of files and folders is making people understand large language models through metaphors of brains and people.

Almost every framework being used to describe AI in 2026 is borrowed from a previous domain. None of them quite fit. The few people who are actually building useful intuitions about how these systems work are the ones who have done what Dijkstra recommended forty years ago.

They have set down the old vocabulary. They have looked at the new thing on its own terms. They have accepted that the radical novelty is radical for a reason.

You are not slow. You were taught a discipline as if it were a hobby. The cruelty is real.

The fix is still available.

[𝓚𝓸𝓴𝓸 @DevKokooo]

@LowLevelTweets Kernel-level data breach

[𝓚𝓸𝓴𝓸 @DevKokooo]

@skydotcs Maybe they figured out how to sell offworld manufacturing insurance before it's built

[𝓚𝓸𝓴𝓸 @DevKokooo]

Also note that rocket technology isn't open sourced due to national security restrictions

So it's inevitable that rocket companies will go through the same trial and errors to reach the same capabilities as their competitors

[𝓚𝓸𝓴𝓸 @DevKokooo]

Honestly, if no one got hurt and they learn from valuable data, that's a win

This is probably the worst it'll ever get, before successful launches become routine again like SpaceX

"Err and err again, but less and less"

[Spaceflight Now @SpaceflightNow]

Here's our video of the explosion at Launch Complex 36. It happened about 9 pm ET (0100 UTC) as Blue Origin was beginning a static fire test of its New Glenn rocket.

Watch live views: https://t.co/tm2wZQmAVD

[Justin Skycak @justinskycak]

If you don't make things happen and build the life you want, a life you don't want will be built for you, and it will happen to you.

[𝓚𝓸𝓴𝓸 @DevKokooo]

@pronounced_kyle @nateliason Andy Weir books and The Expanse

[Harris Rothaermel @DeveloperHarris]

can confirm that building a factory completely satisfies and exhausts the factorio itch, highly recommend

[𝓚𝓸𝓴𝓸 @DevKokooo]

X premium will be gone by next month too

Goodbye @grok, you have been useful to me

[𝓚𝓸𝓴𝓸 @DevKokooo]

I've canceled around $780 in annual subscriptions so I can focus on the important stuff

MathAcademy, gym, and some domains are all I have left

[𝓚𝓸𝓴𝓸 @DevKokooo]

@Jonathan_Blow Braid was awesome

[𝓚𝓸𝓴𝓸 @DevKokooo]

@torgeirlysen They need you more than you need them

[𝓚𝓸𝓴𝓸 @DevKokooo]

@cneuralnetwork No amount of success can buy integrity.

Cheaters will be forced to pay the consequences eventually.

For some people it might manifest as being rich but alone and empty inside.

For others, they might just make it on the Forbes 30 Under 30 list.

[𝓚𝓸𝓴𝓸 @DevKokooo]

@anuatluru The Renaissance is back

[𝓚𝓸𝓴𝓸 @DevKokooo]

@i2cjak GitHub feels more like a social network that happens to host code

Some of these GH comments in issues and PRs are peak content

[𝓚𝓸𝓴𝓸 @DevKokooo]

@infinterenders I tried C++ when I was 10 and that made me quit for a few years

[𝓚𝓸𝓴𝓸 @DevKokooo]

I try to eliminate access to choices so that the only thing I could do is the thing I should be doing

i.e. I recently found out that taking my headphones off completely and putting them away decreases my likelihood of browsing YT or listening to music instead of studying in silence

I also have a browser extension called "Unhook" that disables recommended videos on YT, and I make sure that extension stays unpinned

I also make the good habits easier to access and the bad habits harder to access

i.e. getting a bag of already prepared chopped salad or fruit is easier for me than eating out or buying junk/fast food

Our minds reach for the easiest and cheapest access to dopamine

The more steps between temptation, the harder it will be to reach it

[𝓚𝓸𝓴𝓸 @DevKokooo]

@hunterlanier The more pictures the better

[𝓚𝓸𝓴𝓸 @DevKokooo]

@sunniekapar Canada's greatest export is still Waterloo grads

[𝓚𝓸𝓴𝓸 @DevKokooo]

If I kept my habit going from the start, I would have finished last April

But at least now I have access to the new M4ML content, and preparing for the ML1 course that's coming soon

I got too distracted by too many shiny things at the time

I could also go through this "AI from scratch" curriculum if ML1 isn't out yet

https://t.co/BhOyf23nOQ

[𝓚𝓸𝓴𝓸 @DevKokooo]

🌇 End of Day Recap — +43 XP ✨
📚 54% MF 2 — 17% MF 3 — 7% M4ML

✅ [MF2] Vertical Reflections of Exponential Functions — 9/7 XP
✅ (Review) [M4ML] Conditional Statements — 6/4 XP
✅ [MF2] Invertible Functions — 7/7 XP
✅ (Review) [M4ML] De Morgan's Laws for Logic — 4/4 XP
✅ [M4ML] Logical Equivalence with Conditional Statements — 8/9 XP
✅ [MF2] Calculating the Inverse of a Function — 9/7 XP

[𝓚𝓸𝓴𝓸 @DevKokooo]

🌇 End of Day Recap — +49 XP ✨
📚 40% MF 2 — 7% MF 3 — 4% M4ML

Weekend math studies

✅ [MF2] Solving Logarithmic Equations Containing the Natural Logarithm
✅ [MF3] Solving Quadratic Inequalities Using the Sign Table Method
✅ [MF2] Variance and Standard Deviation