Lately we’ve found an easy way around that resistance. We no longer need to try over and over to reach the solution, or review the solution in detail to see what we’re missing; language models take care of lightening that challenge. What the calculator once was, an agent is now, with the difference that the calculator did the math, but the reasoning was still ours. The agent promises to do both.

We have feared that technology would affect our intelligence ever since tools have existed. And I don’t want us to fear agents. On the contrary, used the right way they can speed up learning. AI took away the friction that used to come free with learning. Now we have to seek it out on purpose. The good news is that AI itself can help us, if we ask it for questions instead of answers.

The value of mistakes

Learning happens in the struggle to solve a problem, getting it wrong and trying again. Our brain predicts and looks for patterns by instinct. It does so with fast thinking, the intuitive kind, the kind that doesn’t ask permission. To activate slow thinking, the deliberate kind, we need to stop assuming our model is the right one. Get it wrong. This forces us to stop and think seriously.

This has a name: desirable difficulties. They make studying slower and more uncomfortable, but what we learn lasts longer. Something similar happens with what we generate ourselves. We remember it better than what we only read, even if our first attempt is wrong. Mind you, afterwards you have to check the correct answer. Otherwise, the mistake stays with us.

Beware of the illusion of competence. We read something, understand it effortlessly and think we already know it. Until the test comes and we can’t explain it. Rereading your notes feels productive, but almost never is.

In practice, this translates into three habits:

1. Get it wrong first. Before starting a topic, a class or a tutorial, try to answer without knowing. You’ll fail, and that’s what gives the theory the weight of an alarm bell.
2. Don’t reread, explain. After reading, close the material and explain what you understood in your own words, out loud, like you’d explain it to a five-year-old. Wherever you get stuck is the path to follow.
3. Answer before looking. When you test yourself, always write down what you think the answer is before seeing the correct one.

Learning should feel clumsy. Clumsier than doing it with your feet.

Using AI to learn

These practices take discipline, and discipline is hard when the answer is one message away. While AI can be what blocks us, it can also be the answer. An AI that answers takes the effort away from us, whereas an AI that asks gives it back.

We can learn from many sources or from a single tutor. Many sources give more perspectives, but they scatter our attention and force us to find material, organize it and verify it. An AI tutor can gather those perspectives and hand them to us in one place. That way the friction leaves the logistics of studying and stays where it belongs: in the moment of learning.

A cycle that works:

1. Diagnosis. Ask the agent to quiz you until it finds where what you know ends.
2. Material. With that, have it generate study material adjusted to your level: not so easy that you get bored, not so hard that you give up.
3. Test. Have it test you, and you answer before seeing the solution.
4. Repeat. If you fail, go back to the material. If you get it right, move on to the next topic.

This way we can use an agent as a private tutor and aim the difficulty at the right spot.

Closing

For centuries, learning hurt without anyone asking for it. Now the effort is optional and, like almost everything optional, it’s very easy to skip.

AI can give us all the answers. The hard part is asking it for the questions. So the next time you open a chat to understand something, do the counterintuitive thing: ask it to test you, get it wrong with confidence and explain the topic to it as if it were a five-year-old.

Let’s not outsource our critical thinking. And if learning feels clumsy, you’re doing fine.

Even so, the path to landing your first job still has the same steps as when I started. What changed is how you get evaluated.

How I got started

When I started, web development seemed like the safest option. I learned React and Node.js, because using the same language on the frontend and the backend made sense to me. I built the classic to-do lists and, since freeCodeCamp was all the rage, I jumped into the “365 days of code” challenge. There I put together a simple online store with HTML, CSS and JavaScript. It was pretty basic, but it helped me understand how the web works.

Back then I read a lot about how to get a job, and the advice was always the same:

1. Apply to everything you can.
2. Go to meetups and events to meet people.
3. Build projects that show what you know.

I was doing two out of three. I applied like crazy and practiced with React nonstop on side projects.

In the end the job showed up on my university’s Slack. Someone posted that they needed a Meteor developer, a framework that joined the backend and the frontend through methods. First came a technical test, and that’s where the projects paid off: it went well, I moved on to the interview and they saw that I was excited about the technology. I had never heard of Meteor, but knowing the fundamentals it wasn’t hard to adapt. It was just a matter of knowing how to write JavaScript. But this was more than 5 years ago.

What didn’t change

Today it’s still the same: you apply a lot and get rejected a lot. And having someone who recommends you is still what helps the most for the first job.

But something did change. I have classmates with six years of university who still can’t find a decent job in Chile, and it’s not because they don’t know their stuff.

What did change

When you apply, the first thing that checks your CV is how many years of experience you have with each technology. An ATS or a person. If the posting asks for three years of React and you don’t have them, you’re out. The problem is obvious: a junior has no years of experience, because nobody has given them their first job.

I don’t think it’s the recruiters’ fault. Finding out whether someone codes well takes time, so people filter by what’s easy to check: keywords and years. That leaves out precisely the people who are just starting. And with so many companies betting on AI instead of hiring, there’s even less room.

I think this will get better at some point. In the meantime, it makes sense to adjust how we present ourselves.

What I would do differently today

Tailor your CV to each posting. Your CV doesn’t have to tell everything you are. It has to get past the filter. Use the same words the posting uses and leave the rest for the interview. Sometimes I’ve even seen the recruiters themselves edit your CV so that it includes the technologies.

Prioritize referrals. If someone at the company recommends you, your CV goes straight to a person who understands what you do, without going through the years-of-experience filter. That’s why it’s worth investing time in getting to know people in the industry.

Build projects to learn, not to show off. Most likely nobody will look at your projects before calling you, so don’t stress about having a perfect profile. Projects are for learning and for walking into the technical interview relaxed, which is where someone who actually knows programming finally evaluates you. If you understand the fundamentals well, you can hold your own even if the posting asks for a technology you haven’t used.

To wrap up

The process is broken and we’re not going to fix it ourselves. What we can do is understand how it works and not take every rejection personally. Almost always it says more about the filter than about you.

Tailor your CV, find someone to recommend you and keep learning with your projects. There will be rejections and uncertainty, but that’s part of the process, not a sign that you’re not cut out for this.

Every day that passes, AI is more capable. I see how code agents are pushing junior developers out of the equation. In graphic design, generative tools package logos and illustrations in seconds, making the work evaporate slowly but surely. It’s a shadow relentlessly spreading over marketing, audiovisual production and, needless to say, translation.

But there’s a gap that still hasn’t been filled. We’re building systems that can understand language but not the purpose or value of what they say, at least not in a human way. Artificial Intelligence doesn’t achieve the perspective of will, and it can’t deal with the unknown except by hallucinating.

The illusion of will

In case you didn’t know yet, what we call AI these days is LLMs (large language models), systems that learned language patterns by reading an enormous amount of text. At an atomic level it started with the idea of predicting the next word.

Roses are …

Now, predicting the next word requires building a complex representation of the context. Building multiple contexts and relating them to each other in sophisticated ways. How this new word influences the whole conversation. Over time these models have grown in size, and so have their training sets, reaching capabilities that resemble reasoning, communication and problem solving.

And with that came people’s fear and anxiety. But these models don’t cover important aspects of our infrastructure as a society. Not all of our activities can be extrapolated into a training set.

Visualizing patterns, applying probabilities, finding sequences of tokens or words and statistical association is how models see the world. Experience, intent, responsibility, context and values are left to our side of the court.

Because every model categorically confuses frequency with truth, we forget that every dataset it is trained on has errors. That these models touched the internet. And the internet lies, right? Condemned to replicate and optimize the world as we describe it to them. With all the garbage and fiction, all the fanfiction, wattpad and absurd conspiracies. We have to tell the truth apart from the garbage it hands us and connect the wires to get on the right track, our track.

We went to A and then C, no problem getting to B. Now how do we get to Z

What is finite and easy to capture, the common events that feed these immense machines, shapes a perfect and efficient model of thought, 100 times more efficient at transactional tasks, where the details are so well defined that we could find a needle in a haystack.

But the world contains much more entropy than even we are willing to accept. It’s much messier and more chaotic. Strange events, atypical combinations or creative scenarios could leave a long tail that goes on to infinity. Or rather, they would have found the end, because they always do, but phenomenally wrong:

“Eat at least one small rock per day”… (BBC news story)

Also, in an imperfect environment like our reality, there’s another dimension that has to do with combinatorics: adding variables quickly leads to exponential combinations. For the way these models work to capture that dimensionality, we would need an enormous amount of data.

That’s why we should be careful with the illusion of having in front of us an image of ourselves, and its estimates. We still don’t have a model capable of “instant learning”. We won’t find any model that knows the art of improvising to get out of a jam.

RANT

What I wanted to say is that our work, and that of many others, will still be there: dealing with human ambiguity, battling legacy systems, struggling from one version to the next because someone decided to build on an unstable base. Designing discrete systems with an immense reality in mind.

There’s no machine that solves for us the problem of uncertainty, of long-term complexity. What does a code agent care whether the application is still working in a couple of years? The only thing that changes is that now we’re going to be buried under a mountain of code. And who is going to save us from that 80% of garbage code?

I hope you and I aren’t too lazy by the time there’s nobody left to hold accountable; when the concept of “AI first” has us digging through a cesspool that looks like it was written by an alien with attention deficit. And investors are going to love it, because it “was built in two days”. But who knows what the hell it summoned, and why it decided to import three libraries, cross dependencies and import half the internet.

So, here, with a fridge, a couple of pieces of furniture and some kitchen stuff, this adventure begins.

I have to take the metro every day. The number of people is incredible. Rush hour gets packed with long faces, shoving and bad moods. But I get it, distances are much greater, and so are prices. Centralization. At least I have everything next door. If I want to shop, I have two Lider supermarkets a couple of minutes away. Are two Lider necessary? The truth is both are packed after 6 in the evening. There are also several pharmacies, bookstores, cafés, café chains, specialty coffee shops, small ones, big ones, themed ones, bad ones, good ones… The point is there are a lot of coffee shops. On top of that, there are restaurants and bars, a Domino’s that serves completos with the tomato on top, among many other stores of every kind with their own quirks, and there’s still a lot left to explore.

Job hunting in 2026

My job search was long. At one point I was torn between taking the full cloud path and going with AWS and its different specializations, since Python in the industry seemed like something forgotten or left to certain creatures hundreds of years old. But a hybrid worked: at its core it’s the ability to work with constant streams of data. Ensuring systems stay up and having the speech ready when it comes to interviews. Another decision was between on-site and remote. There, reality showed me how remote jobs have been disappearing. That pandemic-era industry no longer exists, it has been slowly dying out.

Worse still, a lot of the people who built their lives around remote work have had to go back to the offices. Imagine: a life built around that, the house, the kids’ school, transportation. Your whole spending flow changes completely between one way of working and the other. Because it’s obvious, a 20-minute trip is not the same as a 2-hour one. Having to take the metro or not. Having to share spaces or not. I think working on-site has its advantages, but I think a company that works remotely should keep betting on it.

Developing as I go

This post has been more on the reflective side. I hope to give my more technical opinion on things soon. I’d like to wrap up with an idea of what I think being a developer in 2026 is like. A lot to say, little to code. I think I spend more time talking to an agent and refining my instructions than defining patterns, classes or functions. More time paying down technical debt than leaving it behind. Understanding the domain and the business rules. Deciphering the intentions of other developers or PMs.

An industry that seems made for generalists, where anyone can program (wink, wink).

I spend most of my time translating logic between frontend, backend and infrastructure languages, and not deciding whether to use a frozenset or a tuple as a dictionary key. Specialized jobs probably still exist, I don’t know about in Chile. Even less so with the arrival of AI agents, something I plan to write about in another post, since this mess is already starting to look like Frankenstein.

When I started, I was driven by an interest in video games, an affinity for math and the discovery of the magic that programming makes possible; financial security was also an incentive. At university, chasing the same goal alongside my classmates helped me keep going despite how exhausting the road was.

But passion naturally fades. Time doesn’t forgive, and our brain gets used to stimuli, reducing the emotional response and the joy we get from tasks that used to produce dopamine spikes. Stagnation also shows up: as our skills grow without the complexity growing, we fall into a technical boredom where work no longer demands the cognitive effort that used to be rewarding.

Facing software development without enjoying it isn’t just tedious, it’s a trap. Spending hours in front of the screen without a single line of code sparking any enthusiasm is a problem of work philosophy. Programming without those little shots of dopamine is simply unsustainable. We usually talk about requirements and quality attributes for our systems, but we forget that our own process has to be maintainable.

This career is a long haul. If moving forward is hard for you today, tomorrow it will be impossible; so look after your motivation with the same rigor you look after your code, and treat your passion with the same determination you use to try to master your favorite language.

The Blacksmith Mindset

To find motivation again, I had to resist the idea that it’s enough for things to simply “work”. Although my experience lets me be efficient, there’s a gap between the functional and the “perfect”, understood not as an unreachable goal, but as the willingness to look beyond the obvious solution: separating the patterns that make up the structure and exploring alternatives until I’m sure the path I chose is the right one. There’s a deep joy in this technical mastery; being an expert is also fun.

However, this craftsman’s view clashes with the fast-paced world we operate in. We live in a society (insert Joker image here) hungry for speed and results, full of deadlines, story points and product managers. We ourselves want to see those results fast and without frustration, but learning comes with frustration. To reach excellence, there must be room for introspection, concentration and analysis. We have to look for alternatives and embrace the frustration of learning, accepting that to be good at something I first have to be bad at it. Making mistakes isn’t avoidable, it’s part of experimenting, and we shouldn’t treat that time as wasted time.

That pressure for speed is what breaks the bond with our work. When nothing belongs to us and our work becomes something foreign, a disconnection appears that directly affects our mental health: that’s where excessive workloads, difficulty setting boundaries and the importance of learning to say no come in. There are many ways to lose the joy in what we do, which is why we can’t leave our motivation to chance.

Adopting a craftsman’s mindset is claiming a purpose of your own. When the environment doesn’t offer us a clear sense of meaning, our task is to find satisfaction in the doing itself, rediscovering that the value lies not only in the result, but in the quality of our attention. To dig deeper into this concept, I recommend reading authors who explore “deep work”, like Cal Newport in Deep Work, who argues that regaining our ability to concentrate is the ultimate act of resistance in this era.

In the end, reconciling the efficiency the market demands with the excellence we seek is the only way to make this career sustainable. It’s not just about writing code, but about how we inhabit the hours in front of the screen.