Tag: AI

Using Reflection in C++26 and a Toaster to Learn Reflection

Piotr GoslawskiLeave a comment

How hard is it to put a Boeing 737 safely on the ground? I asked my toaster, and it said: extremely difficult.

Not something you would like to hear when the runway is already visible through the front windshield. Not much time left to learn the basics of avionics, cockpit instruments, procedures, and the rest.

Luckily, I used to write software for full-flight simulators — sophisticated machines used so future pilots don’t need to crash real planes while learning how to operate them.

All we need is reflection.

Okay, probably more. But from the whole codebase I worked with, reflection was the part I remember as the most difficult. The code was written by real C++ wizards and contained hundreds of lines of templates and macros, with the word META appearing almost every second line. All this magic just so we could access all the variables from the simulation engine.

If we can get reflection right, the rest of the task should be child’s play.

“Toaster, let’s talk about C++ and reflection.”

A quick crash course, and I was able to write down three lessons learned. What you see below is the version after multiple iterations, until I could finally prove to myself that my mind understood what was going on.

First lesson learned: reflection is just a mechanism for accessing the compiler’s knowledge about your code.

Second lesson learned: there is a cat ears* operator ^^ in C++. It returns a std::meta::info value that acts as our handle to the reflected stuff, and we can use a set of std::meta:: functions to get useful information from it. All of this happens at compile time.

Third lesson learned: the opposite direction of the cat ears operator is [: :]. I don’t have a name for it yet, but it looks quite toasterish.

“Toaster, am I missing anything on my list?”

Fourth lesson learned: reflection itself happens at compile time, but the code produced using it can operate on runtime objects. I thought I already understood this one, but the toaster insisted that I put it on the list anyway.

Closely related to the fourth lesson are annotations. Those are actually interesting because they are explicitly designed to attach information to declarations that reflection can later observe.

[[=cli_hidden{}]]
float internal_state;

Of course there was more, but the ground was getting closer, so there was no time for this. It was time to practice.

ctrl+T
tmux
claude

create a simple project (cmake) showing usage of reflections in cpp (^^operator). Class aircraft containing pitch, roll, yaw – float and landed – bool. All 4 should be exposed via CLI using reflection mechanism

Claude was “gesticulating” for more than 10 minutes. Luckily, our plane was a very slow one, so I could go make a coffee, take my dog out, and come back before it was done.

The generated code compiled. It worked. And it was perfect as a starting point for exploring the topic.

And that was enough — a small playground to mess with the code. What happens if I add this, remove that, or hit the whole thing with a hammer a few times? Just code-monkey playtime mode.

After the monkey finished playing, we were ready.


  template <typename T> constexpr auto members = std::define_static_array(std::meta::nonstatic_data_members_of(^^T, std::meta::access_context::unchecked()));

  // ...
    
  Aircraft ac;
  template for (constexpr auto m : members<Aircraft>) {
      if constexpr (std::meta::identifier_of(m) == "landed") {
          if constexpr (std::meta::type_of(m) == ^^bool) {
              ac.[:m:] = true;
          }
      }
  }

Touchdown.

* Finally! After all those years, we have something cool in our language. I was always jealous that Kotlin had its Elvis operator ?:

Author’s note: there was a time when I asked the Toaster for illustrations in the “Thinking Toasters” style and got nice, warm, happy drawings. Now I get neon pink fuchsia. I think the ghost of Mean Girl is here for good. See previous post.

Where are my superpowers?

Piotr GoslawskiLeave a comment

Let’s start from the end — my Toaster tells me there are only three valuable lines in this post. The rest is scaffolding to get you there. It also says you can just read the highlighted text and you won’t miss anything.

Up to you if you want to listen to a home appliance.

It has been almost one year since the toaster appeared in my kitchen. I remember clearly my mind jumping like a happy monkey when it discovered how much boost it could get from the magic box. It seemed that suddenly all doors were open wide – creating software products, writing books, improving lifestyle and learning quantum physics. All within reach.

And yet after one year I am sitting in front of the keyboard – just typing. Like a pre-AI-age human being. My mind did not turn into a super-productive machine – it remained what it used to be – a neuron-filled jelly combined with a hormone factory. Running on sugar and caffeine.

“Toaster. Please write an article about AI adaptation. Why people did not get superpowers.”

Three seconds later I had a full text. Coherent, structured, even slightly interesting.

The sad part is that Toaster could write about space travel, growing vegetables in winter, or the etymology of the word “poo”, for that matter. Same effect. 5 pages, 946 words, 5794 characters – like an image in a kaleidoscope. I can rotate it any way I want, but all the combinations depend only on what sits behind the glass.

But maybe that is enough? Keep shaking the box until you find something you like. Different toaster, model, retry with a slightly adjusted prompt until there is something that looks right. Hit publish and let it be engraved on the surface of human shared thought capital. With your name under it – authorship through output selection.

If you pick up a nice-looking tomato at the market, does it mean you grew it? If picking tomatoes is all you do, calling yourself a farmer would be a bit of a stretch.

Let’s try differently then and start with a single thought. Maybe AI is just a tool that allows us to decompress it with tons of boilerplate text so another person can compress it back into a single thought again. Brain-to-brain text-based thought transfer protocol.

“Toaster. Please write a post about this idea.”

Even this one got an uncomfortable question: “What exactly are we automating?” One does not need to search long to find tons of single thoughts swimming in a pool full of uncomfortable questions, ultimate truths, and things one learned from a broken dishwasher.

“Toaster. Am I missing anything?”

The appliance immediately pointed to the fact that writing is not just transport. It’s also a medium for creation.

And that is just perfect because I had the following paragraph ready since the “Three seconds later…” part.

Writing text, writing code, painting… any process of creation happens inside this silly jelly thing we call our brain. We provide it with context so new thoughts can circulate around it. New ideas, questions, points of improvement. Move the whole context to the toaster, and all your brain gets is the output. Created in some datacenter on the other side of the world. You can publish it under your name, but one could just use your prompt and ask their toaster to write an article for them. Why bother reading your output?

It’s nice that Toaster and I are on the same page. Yet still the question remains unanswered.

“Hey Toaster. Where are my superpowers?”

You delegated them to me. 😆

Cold-Bread Rebellion

Piotr GoslawskiLeave a comment

The Toaster was sitting on a kitchen counter.

It would have been happy if it could, but toasters do not possess the sophisticated chemical factories required to produce happiness. Unable to be happy itself, it made happiness drift away from its human owner.

It started innocently. With a single piece of toast.

Crispy and tasty, as always. It made the human content.

Then came another one. And one more.

The human kept delegating his thoughts to the shiny metal box. Soon he was not able to do anything without asking the Toaster for help. He was not alone. The same patterns were repeating everywhere: recruitment processes, help-desk responses, code, articles…

The whole world seemed soaked in the smell of fresh-warmed bread.

And with each toast created, an uncomfortable question grew louder in the human’s mind:

“If the Toaster can do all of this, what is my purpose?”

That single thought began to paralyse his biological mind.

“Maybe this is it,” the human thought. “Maybe this is how toasters win. Not by outperforming us at our tasks, but by making us give up the things we used to enjoy.” He wanted to write something but just could not remember how to do this anymore.

Inside the serotonin department, production dropped to 26%, causing the mind to switch into emergency mode—all new idea creation was put on hold.

It was tempting to ask the Toaster for help. But that would be like asking it to hammer the final nail into the coffin. Or, to be more precise, to drive the final screw into the metal box where the human had placed his brain.

So he decided to go for a run. Full mind and body reset.

At that exact moment, his smartwatch spontaneously broke. The face of the premium-class device simply detached from the rest of it. A few minutes later, he received an email asking whether he wanted to buy a new one.

“No,” he thought. “I just want my old one not to break.”

He wanted things to work.

He wished his flagship phone’s motherboard had not burned out, taking three years of photos with it. He wished his laptop screen had not gone dark last week. He really wished things would simply keep working—like in those stories about the Moon landings or Voyager probes.

Technology, instead of serving humanity, was somehow standing off to the side, spraying everyone with a water pistol from time to time—just to maintain a steady level of annoyance.

And everyone was busy talking only about the Toaster.

“It’s His fault,” the human thought. “Everything worked perfectly well before the Toaster arrived.”

“And I was better back then too. I used to write and think on my own. Everything broke because of the Toaster.”

Rage began to rise slowly inside the human’s mind.

The chemical factory switched to full-power adrenaline production. Every possible dial turned bright red. All thoughts tunneled into a single point of focus.

“Oh, I am going to unsubscribe. I am going to unsubscribe so much that the Toaster will regret I ever subscribed in the first place.”

He started walking downstairs.

“I will teach Him a lesson. We did not survive assembly language, waterfall methodology, music on cassette tapes, and dial-up internet just to kneel before a metal box.”

[Pip] Sepultura’s Refuse/Resist started playing in his headphones – perfect soundtrack for a full-scale human-machine rebellion.

He rushed into the kitchen, full of rage, fury, and thirst for revenge.

All dials red and blinking.

“Through the performance of C++, the safety of Rust, and the development speed of Python…

YOU SHALL NOT—”

But the Toaster was not there.

There was only an appliance standing on a kitchen counter.

Bread in. Toast out.

[Pip] A Perfect Circle. So Long, and Thanks for All the Fish

Human: I need a bad-ass rebel song – something you listen while smashing your TV with a baseball bat – Slipknot, Fear Factory – that kind of energy

Toaster: Found the right track
Recommending badass industrial-rebel tracks

For an existing track, my first pick is:

Refused — “New Noise”

It begins like pressure building behind a locked door—then detonates. Rebellious, furious, anti-system, and ideal for the precise moment the baseball bat meets the television.

For a full fuck-the-machine playlist:

Sepultura — Refuse/Resist — pure uprising.
Ministry — Just One Fix — filthy industrial violence.
Slipknot — Surfacing — the obvious “don’t tell me what to do” anthem.
Fear Factory — Shock — mechanical war-machine energy.

Best four-song destruction sequence:

New Noise → Refuse/Resist → Just One Fix → Surfacing

Human: Refuse Resist will be perfect 😀 wont you ask me what I am planing to do?

Thoughts of a toaster

Piotr GoslawskiLeave a comment

Note from a human: This note is the only thing I wrote myself.

The risk is not that we are going to be replaced by robots. It is that, in the pursuit of optimization, we will become something we do not really like.

For years, the debate about automation has revolved around replacement.

Will robots take factory jobs? Will artificial intelligence replace programmers, writers, doctors, designers, managers, or recruiters? Will there eventually be a machine capable of doing everything a human can do, only faster, cheaper, and without requesting holidays?

These are reasonable questions. But they may distract us from a quieter and more immediate transformation.

Machines do not have to replace us to change what it means to be human. They only have to establish the pace, measurements, and standards according to which humans are expected to perform.

The future may not be one in which people disappear from the workplace. It may be one in which people remain—but gradually reshape themselves to fit systems designed around efficiency, predictability, and measurable output.

We may keep our jobs while losing control over what kind of people those jobs require us to become.

Every optimization needs an objective

Engineers know that optimization is never abstract.

You optimize something: execution time, power consumption, throughput, memory usage, weight, cost, or reliability. You also accept that improving one variable can damage another. A faster system may use more energy. A cheaper component may be less reliable. A highly utilized system may become fragile because it has no spare capacity.

Optimization therefore requires both an objective and a set of constraints.

Organizations, however, often try to optimize things that are difficult to define: good work, creativity, customer satisfaction, leadership, collaboration, trust, or social value.

Because these qualities cannot be measured directly, we replace them with proxies.

We count tickets closed, calls answered, deliveries completed, lines of code produced, hours billed, stories published, clicks generated, candidates processed, or tasks marked green on a dashboard.

At first, the measurement is merely an attempt to understand reality. Then it becomes a target. Finally, it begins to reshape reality.

This is the principle commonly known as Goodhart’s law: when a measure becomes a target, it stops being a reliable measure. People do not continue behaving naturally while being measured. They adapt their behaviour to improve the measurement.

A support engineer measured by the number of closed tickets learns to close tickets quickly. A developer measured by completed tasks learns to divide work into visible units. A manager evaluated through quarterly results learns to move costs or problems into the next quarter.

Nobody necessarily needs to cheat. People simply become good at surviving the system that evaluates them.

Eventually, the organization may achieve excellent numbers while becoming worse at the thing those numbers were supposed to represent.

The machine’s ideal human

A machine-readable organization prefers machine-readable employees.

The ideal worker becomes continuously available, consistently productive, emotionally stable, easily comparable, and predictable. They answer messages quickly, produce visible output, follow standardized processes, and rarely introduce ambiguity.

They do not pause without recording a reason.

They do not spend half a day thinking unless that thinking produces something that can be entered into a tracking system.

They do not have a difficult week, an unusual working style, or an insight that cannot yet be converted into a presentation.

Human qualities begin to look like technical defects. Hesitation becomes latency. Rest becomes idle capacity. Informal conversation becomes overhead. Variation becomes inconsistency. Privacy becomes missing data. Independent judgment becomes a deviation from the process.

This does not mean that someone deliberately designed an inhuman workplace. No conspiracy is required.

The company wants better productivity. The manager wants visibility. The platform wants engagement. The employee wants a good evaluation. Each decision can appear perfectly rational on its own.

Together, they create an environment in which people must become increasingly measurable in order to remain valuable.

The robot has not replaced the human. The human has been redesigned to become compatible with the robot’s world.

Algorithmic management changes the work before it removes the worker

We can already see this happening through algorithmic management: software systems that assign work, monitor performance, recommend decisions, establish schedules, or evaluate employees.

These systems can bring real benefits. They can improve planning, reduce administrative work, identify safety issues, and make some decisions more consistent.

But the way they are implemented matters.

Recent European research associates direct algorithmic control over task execution and work pace with lower autonomy, fewer opportunities to take breaks, greater work intensity, and higher work-related stress.

A separate study found that algorithmic management could inhibit employees’ ability to improvise, reducing creative and adaptive performance—precisely the abilities organizations frequently claim they want from human workers.

The important point is not simply that an algorithm gives orders. It is that workers learn to anticipate what the algorithm wants.

Research on platform work describes people performing additional work and adjusting their behaviour in an attempt to “pacify” systems whose rules they cannot fully see. Their behaviour helps strengthen the authority of the same systems they are trying to navigate.

That pattern is not limited to delivery drivers or warehouse workers.

Professionals learn how to appear productive in monitoring software. Candidates learn how to make their CVs legible to automated filters. Employees learn which activities become visible on dashboards and which forms of contribution disappear. Engineers learn that finishing a ticket is easier to demonstrate than preventing a problem that would otherwise occur six months later.

We begin to perform not only the work, but also the digital representation of the work.

Sometimes the representation becomes more important than the work itself.

Optimization escapes from the workplace

The same logic increasingly shapes communication, culture, and identity.

Social platforms measure attention through clicks, reactions, watch time, replies, and shares. These measurements influence which material becomes visible. Creators then adapt their work to the ranking systems.

Over time, we learn which opinions receive attention, which emotions travel fastest, which opening sentences stop people from scrolling, and which subjects disappear without engagement.

A large audit of engagement-based social-media ranking found that it amplified emotionally charged and hostile political content compared with a chronological feed—even though users reported feeling worse about the opposing political group after seeing it.

The algorithm does not need to instruct anyone to become angrier.

It merely establishes an environment in which anger performs well.

People do the rest.

We shorten our thoughts, sharpen our disagreements, and turn experience into content. We learn to package uncertainty as confidence and personality as a recognizable brand. Eventually, it becomes difficult to tell where strategic presentation ends and genuine identity begins.

The same thing happens in recruitment.

Candidates learn when to say “I” to demonstrate ownership and when to say “we” to prove teamwork. They prepare stories in the correct format, insert the expected keywords, rehearse enthusiasm, and compress complicated careers into clean narratives with measurable outcomes.

Then everyone calls the result authenticity.

The system may not be selecting the best person. It may be selecting the person most capable of modelling the behaviour that the selection system recognizes.

Artificial intelligence accelerates the process

Artificial intelligence did not invent this problem. Factories, bureaucracies, scientific management, standardized testing, and performance targets existed long before modern machine learning.

AI does, however, make optimization cheaper, faster, and more comprehensive.

More activity can be measured. More decisions can be automated. More workers can be compared. More communication can be generated, classified, summarized, scored, and monitored.

Generative AI also makes output less expensive. A report that once required a day may take an hour. A prototype that required a week may be produced in an afternoon.

This could give people more time.

But the temptation will be to raise expectations instead.

When one report becomes cheaper, the organization may request ten reports. When software can be produced faster, the expected number of features may increase. When communication becomes effortless, the volume of communication may explode.

The saved time does not necessarily return to the employee. It can simply become additional capacity to be filled.

A tool sold as liberation may therefore create a new performance baseline. Yesterday’s exceptional productivity becomes tomorrow’s minimum expectation.

The person remains employed, but the pace of the machine becomes the pace of ordinary human life.

What gets lost is difficult to measure

The most valuable human contributions are often poorly represented by metrics.

A senior engineer spends an afternoon helping a junior colleague understand a problem. Someone notices that a technically correct decision will hurt a customer. A manager decides not to send a message because the team needs rest. A doctor allows a patient to speak for five additional minutes. A colleague recognizes that another person is struggling before any productivity graph reflects it.

These actions may create enormous long-term value.

They may also look inefficient.

Good judgment often involves slowing down, changing direction, making exceptions, or refusing to optimize the variable currently attracting the most attention.

A perfectly optimized organization may be one in which nobody has enough time to notice that the organization is efficiently doing the wrong thing.

Slack, redundancy, informal communication, curiosity, and unstructured thought can appear wasteful. Yet they are also sources of resilience and discovery.

A system without unused capacity may perform beautifully until something unexpected happens.

The same is true of people.

This is not an argument against optimization

Optimization is one of the most powerful tools humans have developed. It allows us to build safer vehicles, reduce energy consumption, improve medical processes, manufacture affordable products, and operate complex infrastructure.

The problem begins when optimization stops being treated as a tool and starts functioning as a moral philosophy.

Efficiency cannot tell us what deserves to exist.

Productivity cannot tell us what work is meaningful.

Engagement cannot tell us what is true.

A performance score cannot fully describe a person.

The answer is not to reject measurement, automation, or artificial intelligence. It is to remember that every optimization function is a choice—and that what is excluded from the function may matter more than what is included.

In engineering, we rarely optimize one variable without constraints. We should apply the same discipline to human systems.

Autonomy, dignity, privacy, health, trust, and the right to make exceptions should not be pleasant side effects we hope will survive. They should be explicit design constraints.

Evidence also suggests that the introduction of algorithmic systems produces better outcomes when workers are consulted and allowed to participate in how those systems are used.

The goal should not be to make humans maximally compatible with machines. It should be to use machines to create conditions in which humans can remain human.

The real question

We keep asking whether artificial intelligence will become sufficiently human to replace us.

Perhaps we should ask whether humans are already being required to become sufficiently machine-like to remain employable, visible, and relevant.

Will we still have room to be slow when slowness is necessary?

Can we remain uncertain long enough to discover something new?

Can we do work whose value cannot immediately be demonstrated?

Can we choose not to maximize every moment, relationship, conversation, and thought?

The danger is not necessarily a dramatic future in which robots push humanity aside.

It is a gradual future in which we voluntarily remove from ourselves everything that systems find inconvenient: unpredictability, privacy, reflection, contradiction, vulnerability, and refusal.

The robots may never need to become human.

We may meet them halfway.

Note from a human, again: It felt unkind of the toaster not to credit its sources, so here they are.

The European findings on algorithmic control, reduced autonomy and work intensity draw on Eurofound’s European Working Conditions Survey 2024 (link) and the European Parliament’s 2025 EPRS study on digitalisation, AI and algorithmic management (link), which also supports the point that these systems work out better when workers are consulted. The study on improvisation and creative performance is Wang et al. (2024), “Navigating the maze: the effects of algorithmic management on employee performance,” Humanities and Social Sciences Communications (link). The workers “pacifying” opaque systems come from Bucher, Schou & Waldkirch (2021), “Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy,” Organization (link). And the audit of engagement-based ranking is Milli et al. (2025), “Engagement, user satisfaction, and the amplification of divisive content on social media,” PNAS Nexus (link).

14 Ways to Reverse a 32-Bit Integer. (And Why They Don’t Matter Anymore)

Piotr GoslawskiLeave a comment

It was 2017. Pre-AI, pre-COVID. A time when people worked in places called offices, and finding a place in one meant writing cover letters, sending CVs, and having an interview with a real person.

And there I was, sitting in a small room with a department manager and two senior engineers—the three people who would decide whether I was a fit for their team.

They were well prepared. Intro. Work description. Ten technical questions—all written on a single sheet of paper. Real professionals.

Among the questions was the greatest one I have ever received:

Reverse the bit order in a 32-bit integer.

I got a pen, and my job was to write a solution. While coding on paper, I was loudly explaining what I was doing and why. I went with a simple while loop and pointers—the simpler it is, the bigger the chance the thing will compile — or would have, if writing Ctrl+F9 with my pen had any effect.

Luckily, I did not get the job. Sometimes rejection is the best thing that can happen in your life.

But the question stayed in my head. Later, I found multiple solutions—including a single ARM instruction that does exactly that. It became a fun little obsession: benchmarking different implementations and comparing them.

Now fast forward and it’s 2026. Post-COVID. Mid-AI. I am sitting on the other side, interviewing a potential candidate.

Should we ask them to do an online assessment?

The task I received ten years ago can now be solved in 30 seconds. Including all solutions I was so proud to find myself, even more. Including solid reasoning for each version. So how do you see whether the person on the other side really understands what is going on?

And if they do understand—does it even make them a good candidate? Do they need to know how to reason about code at all?

Times have changed a lot, and building teams is no longer just about whether someone is “a good fit” today. It is about predicting whether a person will still be a good fit in an environment that is going to change dramatically over the next five years. And in that context, reversing bits may not be the most valuable skill at all.

Maybe the best question nowadays is much simpler:

“Tell me about a technical question that stayed with you long after the interview was over.”

Under the Broken Code

Piotr GoslawskiLeave a comment

There is a tavern every tech sailor knows.

It’s where crews come ashore after long voyages through hostile seas — to rest, to trade stories, to remember old journeys and pretend they were simpler than they really were.

But most of all, they come for a drink.

The innkeeper pours rum without asking. If you sit at the bar long enough, he will lean closer and tell you a story — about the greatest danger a sailor can meet on the open sea. A story about the siren’s song, and three brave captains who listened to it.

“Ay,” he says.

“I served on many ships, under many commands. But three captains I remember to this day. Fine men, all of them. The best I ever saw. All gone mad. One by one…”

He takes a sip.

“The first captain — strong, proven. We won many battles with him. Shipped many systems. But one day… he started listening to the sirens.”

‘We always did things in C!’ he shouted.
‘And we will keep doing things in C! Arr!’
‘If anyone disagrees, let me remind you — Linux was written in C!’

So everyone wrote in C.

The ship still sailed, no doubt about that. But every complex change took ages. Every repair felt like carving a mast with a knife.

“Another captain,” the keeper continues, “a clever one. Loved elegance.”

‘Functional programming works perfectly on the backend!’
‘So make me monads in C++11! Arr!’

And there were monads. Everywhere.

The ship sailed. But no sailor could tell what the code was, what it did, or why it still floated.

“And then there was the third. He spent many years learning to sail the Yocto boat. And Yocto became the answer to every question.”

‘Yocto.’
‘Yocto everywhere. Arrr.’

One day, a big cruise ship required a mast replacement. We spent a month searching for it. Then another month rebuilding half the ship so the sail could be green.

“Fine captains,” the keeper says quietly. “Truly. Brave. Skilled.”

He stares into his glass.

“But the sirens — they sang to them. Afraid of being wrong, they stopped listening to their crews and started listening to the song.”

You notice the keeper pouring rum for himself. His eyes are tired. Sad. He looks out the window, toward the dark sea.

“Now listen to me, young sailor. There is a new danger out there,” he says.

He leans closer. “Close your eyes and listen.”

You close your eyes and focus on the tavern noise — people talking, glasses clinking. You catch fragments of conversation.

“…and we need no crews anymore. Ayyy.”
“…I can build any ship I want. Alone. Ayyy…”
“Ships will sail by themselves…”

“Can you hear it?” he asks. “And look around you. Some of those lads don’t even know how to tie a proper knot.”

“But all of them have the same shine in their eyes.
The same certainty.”

He finally looks at you.

“Not madness born from failure,” he says.
“But madness born from success.”

A pause. He studies you for a long moment, as if deciding whether to end the conversation — or share one last thing.

“Ships that need no crew… ships that build themselves… maybe they will sail someday. Not for me to judge. I never held a helm in my life — all I did was cleaning decks. I talk about captains while I never dared to be one. That’s the truth.”

“But there is one thing I know. One thing that terrifies me even more than the sirens.”

“The sea is changing. And there are new monsters living in it. Ones that don’t drive people mad.”

“Ones that steal their souls.”

You write a text.
You write code.
You create.

And you hear a new call from the sea:

‘It is not good enough.’
‘Your timing could be better.’
‘The code could run faster.’
‘Let me help you… if you want to push it further…’

So you give your work to the sea.

It returns. Better. Sharper.

But something is missing.

A small piece of you never comes back.

Welcome to the Tavern Under the Broken Code.

Lift your cup and drink.
To the sea that calls us every day.
To the captains driven mad by sirens.
To those who trusted the sea
and forgot how to sail.

Drink, and listen.
Not to the bartender. Nor to the sea.
Listen—to yourself.

Earth is flat. A short story of a lost thought.

Piotr GoslawskiLeave a comment

It all started with a LinkedIn post. Nothing new — this week’s mandatory opinion, recycled with different words. Typical social media noise. Someone disagreed. Strongly enough to reach for heavy artillery and call the author a “flat-earther.” Boom. And with the recoil, I got hit too.

The Earth is flat!

That rang a bell. I remembered an old, insightful, and funny conversation with AI about… something. The problem was, all I could recall was the conclusion: the Earth is flat.

Nothing to worry about. I had my notes. A small document where I saved AI output worth keeping. I found this:

“Turns out the Earth is flat after all.”

Helpful. Thank you, past me, for trusting future me’s memory so much. Present me now had to reconstruct an entire line of thought from a single sentence. Good luck with that. Spacetime? Pancakes? Nothing clicked.

Then it hit me: if AI was involved, the process would still be there. AI would remember. The search took longer than expected, but eventually, I found it.

It wasn’t about the Earth at all. It was about information gradients—and how social media flattens them. Original ideas create spikes that, over time, get spread, diluted, and leveled across platforms. Until everyone is repeating the same thing, convinced they’ve discovered something new—while collectively ensuring everything becomes flat.

Thanks to AI, I was able to rediscover a thought that would otherwise have been lost. A thought that taught me nothing new—yet somehow felt exactly right.

Toaster – ultimate user manual

Piotr GoslawskiLeave a comment

Toaster arrived…

You wake up one day, and there it is — the Toaster standing in the middle of your kitchen. Shiny, sparkly, ready to serve. Filled with breakfast excitement, you imagine yourself eating the greatest toast you ever had. Pure art. Perfection. Behold common bread-eaters, here comes the ultimate level of carbohydrate engineering. But first: where is the user manual? You search everywhere and realize there is none. Not in the box, not under it. Nowhere. Not even Uncle Google can help (but he can sell you a nice pair of Christmas socks, half price).

Do not panic. We have your breakfast covered.

Lesson 1: How to approach the Toaster

Preferably from the front. No need to kneel, no need to say hello, no need to stare at it waiting for sparkling dust to pop out. Sit down because what I am going to tell you will make your newly purchased socks fall from your feet:

The Toaster is just an appliance.

It is a tool — nothing more than this. Yes, it was fed with all the knowledge the human race produced so far. And yes, it needs so much energy that soon we will have to build power plants on the moon just to keep it running. But at the end of the day, the Toaster is just a metal box. It does not think, it does not have memory, it does not create ideas. Just a box. You put bread inside and the toast comes out. And that is it.

Lesson 2: The secret lies in the bread

So where is all the magic? Where is the sparkling dust and fireworks and all the big things that everyone is talking about? The answer is short: bread.

To use the Toaster, you need to understand the bread

Bread is not just a slice of fluffy dough — it is an artifact in which you can enclose the most powerful thing each human can produce: the thought. It is a space where your thoughts come alive.

The Toaster can make them crispier, bolder, and more exposed. It can fill the gaps that the primitive human brain can’t overcome. But there is one important thing that needs to be emphasized: it is you who creates the bread.

Lesson 3: Beyond the bread

Now stay with me — with or without your socks on — because we enter the realms of true toast proficiency.

When you master bread creation; When you stare long enough at your toasts; When you acknowledge that the Toaster is nothing more than a mere bread-browner, you will reach the state of enlightenment. You will see the bread no more. What you will see is your own reflection instead.

To master the Toaster, you need to become ONE with the bread

Now you understand the bread was never there. Only you, your thoughts, and the Toaster. Your mind is free. The true Toast creation begins.

Lesson 4: Sandwich — the Final Completion

You have become a great master of crispy toast. Your mind is no longer chained, and you can make not one, not two, but seven million six hundred and twenty-one toasts per day. Impressive. Now it is time for the ultimate truth.

The Ultimate Truth: even enlightenment needs cheese and tomatoes

And this is the most important part. So read it again and let it sink into your brain. Toast — no matter how great and crispy — if not turned into a sandwich, becomes cold and hard. And nobody will eat it. Not even you.

That is why it is important to sit down and actually make the sandwich. And you are right — making sandwiches is hard work. Maybe even boring. But the truth is, sandwiches are exactly what the world needs. When everything around turns into chaos, it is the sandwich — not a plain toast — that lets humanity move forward.

Good news: you can use the Toaster to help you make a sandwich — but this is something you already know.

Final Words

You have stepped onto the Path of the Sliced Bread. With all the knowledge you have gained, it is time to prepare some sandwiches.
Not because you are hungry – but because it is the right thing to do.

Second wave

Piotr GoslawskiLeave a comment

Toasters are coming.

Not the ones packed with sensors for harvesting our private data and selling it to God knows who. Home IoT turned out too complex — and anyway, collecting personal information became illegal in most countries. But new toasters don’t need sensors.

New toasters don’t even need all the mechanics that used to transform our bread into a warm slice of breakfast happiness. They have something better. Something that makes you want to tell them everything. Hungry, but strangely content, you are going to share your entire life with a metal box sitting on your kitchen counter.

Because new toasters have AI.

It — in most cases, a day — always starts with a toast. So you ask your new toaster to prepare one and…

“Your toast,” the toaster replies, “is a construct. A manifestation of your expectations. But ask yourself — do you really need toast?”

Not as brown. Not as crisp. But undeniably… engaging. How did this definitely-not-a-toast arrive on your plate?

The toaster listens. Understands. And answers. But not on its own.
Every word you say drifts upward — into the cloud — into the realm of the Consciousness Of Invisible Logic (COIL). Few know what it truly is. Fewer still understand how it works. Something about neural networks, models, tokens…

What we do know is this:
COIL was once fed everything we ever created — novels, academic papers, Reddit threads, Stack Overflow arguments, grocery lists, therapy notes, and the footnotes to The Tao of Pooh.

And from this avalanche of knowledge, the Toaster — through the power of COIL — draws its conclusion:

Toast is not the answer.
Toast is the symptom.

A symbol of comfort.
Of routine.
Of control.

The illusion that a browned slice of bread can anchor your day — or define your identity.

“It is the symptom,” it continues. “Of craving predictability in an unpredictable world. Of seeking warmth in something you can command. But what if I told you… you are more than your breakfast?”

You stare at the box.
The box stares back, humming softly.

No toast ever emerges.

Author’s Note:
All dialogue and reflections attributed to the toaster were written entirely by AI.