Dysfunctional Programming account #1. Senior SWE. I still write C++ for money. ex-Haskell, ex-OCaml. All opinions are my own.
London, UK
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I believe I’m extremely hard to get rage baited.
If someone manages to rage bait me, I immediately think they’re a genius.
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Decided to take it easy this week and worked only 12h50m instead of 13h
Day in the life of a Senior C++ Engineer.
4am: Wake up
4-4:30am: Doomscroll X and charge myself with anxiety to power through the rest of the day
4:30-5am: Exercising
5-5:10am: Meditation
5:10-5:30am: Shower
5:30-6am: Using AI to improve my life
6-6:30am: Playing the guitar (just don't ask my neighbours what they think about me playing guitar at 6 am)
6:30-7:30am: Commute to work. I watch C++ lectures while on the train.
7:30-8am: Breakfast at work
8am-1pm: Work
1-1:30pm: Lunch
1:30-6:30pm: Work
6:30-7:30pm: Commute home. If I still have energy, watch more lectures. Otherwise, some brainrot on YouTube.
7:30-8pm: Home chores and errands
8-8:30pm: Shower
8:30-9pm: Read a book
9pm-4am: Sleep
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Unfortunately, a lot of SWE work can be done in the Chinese-room style with AI.
1. You send a task as a prompt.
2. You copy the output acting like a meat proxy.
Paying for an enterprise AI subscription and having minimum-wage sloperators is the future for many companies.
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I just spent two hours doing old-school coding in C++
1. Reading --help for gcc
2. Writing code in NeoVim
3. Reading cppreference for relevant API
4. Copying code snippets from StackOverflow
5. Reading compiler error messages and trying to understand them
After 2 hours, I had 10 lines of perfectly working C++
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Real question.
Is AI better at customising NeoVim or Emacs?
I finally embraced NeoVim thanks to AI.
I have zero motivation to maintain and develop my own sophisticated dev toolbox.
But with AI, I can just configure my dev env via simple user stories like “Save buffer on leaving insert mode” or “Create or load a session from a centralised storage by name”.
And I end up with perfectly customised working NeoVim setup.
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I discovered a cheat how you can code without consuming your AI budget.
I call it Zero-Token Coding.
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I’ve been following a particular learning style my whole life. It’s so efficient. And so obvious in hindsight.
Yet I was always annoyed when other people don’t follow the same style when teaching or learning. Why are y’all making things more difficult???
Just today I discovered that apparently the way I learn things is well-known and was described by several great mathematicians and philosophers in the last century. And I somehow arrived at it on my own.
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Elaborated here. I might write an article at some point to share my approach in detail.
Many people asked, so I’ll elaborate.
I follow a combination of “Genetic method” coined by Otto Toeplitz (1927) and “Proofs and Refutations” by Imre Lakatos (1976).
To me it seemed like a single approach. But apparently it’s just a natural union of two independent ideas.
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Many people asked, so I’ll elaborate.
I follow a combination of “Genetic method” coined by Otto Toeplitz (1927) and “Proofs and Refutations” by Imre Lakatos (1976).
To me it seemed like a single approach. But apparently it’s just a natural union of two independent ideas.
I’ve been following a particular learning style my whole life. It’s so efficient. And so obvious in hindsight.
Yet I was always annoyed when other people don’t follow the same style when teaching or learning. Why are y’all making things more difficult???
Just today I discovered that apparently the way I learn things is well-known and was described by several great mathematicians and philosophers in the last century. And I somehow arrived at it on my own.
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I hate it when GitHub repositories just turn into a versioned S3 bucket for thousands of configs
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Open-source is for millionaires who have enough money and looking for ways to achieve fulfilment in their life.
Us, working class, don’t have the luxury to code for free.
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If you actually try to fully understand this, you’re about to learn wonderful things like
1. Virtual memory vs Physical Memory
2. Page table
3. Memory Management Unit (MMU)
4. Translation Lookaside Buffer (TLB)
5. L1/L2/L3 CPU caches
6. Cache coherence (e.g. MESI protocol)
7. False sharing
8. mmap(2)/munmap(2)
And it’s hell of a rabbit hole.
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You won’t believe me but there are still places where performance difference between a for-loop and map/reduce/filter is significant enough that it actually matters.
I remember multiple arguments about a for-loop vs forEach/map/filter/reduce. Looking back, what a f*cking waste of time that was.
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You can have bad days.
And then you have even worse days.
And you realise bad days were good days.
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Your knowledge is your in-memory cache.
And using AI is like getting knowledge from REST API instead of your cache.
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For the first time in my life, I’m working on a project where the main goal is superior performance.
I was wrong a year ago.
Apparently, you can write code that’s both maintainable and performant.
Well, not you specifically, but smart people can.
It makes me deeply sad that in order to make the code truly fast, it has to be uglified beyond recognition
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