You fail to learn if you don't learn to fail | seatedro

If all your time is spent watching output tokens, where do your input tokens come from?

Letting an agent rip on full auto is basically doom scrolling. Even worse if you're doom scrolling while the agent runs. We humans love frying our dopamine receptors. This feels great until you realize what you were offloading: the struggle. The part where you fail.

Failure is the entire point. You don't make progress in the gym unless you take a set at least close to failure. The muscle only adapts when it's forced to. It is no different for the brain.

Cognitive Atrophy

It is very hard to admit to yourself that your skills have atrophied. It is even harder to admit this to other people. I will admit that over the past several months my brain has gotten smoother (and I wasn't even on Twitter much!).

Recently, I had written an abstraction for my diff viewer (diffy), an element system with a macro that lets agents write html-like code in rust for native ui (they reason better with this). But it wasn't adopted everywhere in the repo yet, so when I asked for a new feature, the model decided to hand paint it straight to the viewport instead. Every behavior the element system gives you for free was just... missing. Text wasn't selectable. Hover highlights wouldn't go away. And since I wasn't looking closely, it iterated on the slop and produced more slop, more bugs. I just kept saying continue. I lost a whole day untangling it, and the funny part is that once I actually looked at what it had built, every bug was the same bug.

When you hit a roadblock and your immediate reaction is to reach for something else (previously, this used to be other people, but now it is a language model) you are essentially skipping the part where you actually learn to solve the problem.

It is funny how one of the best "learning tools" has turned out to be the number one cause (anecdotal. sue me) of the lack of learning!

It's been a few months since I started writing this, and things have gotten more dire. Several major software services barely work now, grown engineers I once respected are writing somber posts about missing a language model that was banned for a while. Mourning. For model weights. It's all so dystopian.

It didn't work, but boy was it beautiful.

As the agents get better, one is basically expected to produce code at an alarming rate. The timeline to get something done is compressed but the time it takes to come up with solutions to hard problems has not.

There are usually a few good abstractions one can come up with that balance the upsides and tradeoffs for most software problems. However it is currently trivial to turn your brain off and let the slop flow. The code will be complex. It might look like it all works, but something always breaks. And the solution to that? More slop. Software quality is collapsing as a result, and the societal expectation that engineers understand what they ship is disappearing. You never understood the code in the first place. So when you need to change it, you're asking the same stateless clanker to modify code it has no memory of writing. All output tokens and zero thinking tokens.

A lower barrier of entry to write software doesn't imply the standards for good software must be lowered.

The culture of doing things because they said you couldn't.

The growing trend is to do things because you now can (supposedly), but we used to try and do things because we could not out of sheer stubbornness.

Carmack and gang shipped QuakeWorld with client-side prediction over dial-up when the conventional wisdom was that twitch shooters over the internet were unplayable. This only happened because Quake's original netcode was laggy and everyone hated it. (They fixed it in a month.)

George Dantzig arrived late to class, mistook two "unsolvable" statistics problems for homework, and solved them. Nobody told him they were impossible, so he just did the work.

Andrew Wiles spent seven years alone in his attic working on Fermat's Last Theorem, a problem mathematicians had given up on for 350 years. He announced the proof, a reviewer found a hole in it, and he spent another year fixing that too.

Notice that all three of them became who they are because of the struggle, not despite it. The people benefiting most from generative tools today, say Terence Tao or Mitchell Hashimoto, already put in the time, so when they offload work they're just skipping the typing. When people like you and me (if this is not you, then I apologize) offload, we skip the grind itself. With language models, easy tasks got easier, hard tasks stayed hard. The hard part was never the task itself.

@codex how do I fix this

I don't know, I am figuring this out as I go. The amount of time I have spent actually programming has been dropping month over month this year. I used to have a coding stats section on my website that would track hours I spent writing code split by...