ensocode

I get the point and I’m thankful for the article. I just find “middle of nowhere” and Switzerland a bit hard to reconcile.

This is basically Covey's "sharpen the saw" for knowledge workers. [1]

When you're cutting trees, sharpening the saw looks like you're not working. When you're doing software or organizational work, figuring out what actually matters can also look like you're not working.

The hard part is distinguishing between thoughtful idleness and ordinary procrastination. [1] https://www.franklincovey.com/books/the-7-habits-of-highly-e...

Interested as well. Are there any recommended guidelines or blog posts with more details. Thanks.

Interested as well!

right, I think DHH's "Majestic Monolith" is another good approach here

This resonates a lot if you’ve ever worked in system integration.

In practice, most of the complexity comes exactly from what’s described here: every system has a rich internal model, but the moment data crosses a boundary, everything degrades into strings, schemas, and implicit contracts.

You end up rebuilding semantics over and over again (validation, mapping, enrichment), and a lot of failures only show up at runtime.

I’m skeptical about “one model to rule them all”, but I strongly agree that losing semantics at system boundaries is the core problem.

I sometimes think the same, but look at it this way: you’re an IT worker—probably a specialist, at least a bit of a nerd in something. The days when we were seen as “wizards” are gone, sure. Right now a lot of people in business, and at the interface between business and IT, are riding a wave of confidence because of AI. But the difference is: you’re a critical thinker. They’re just using the tool at face value. Anyone can ask AI: “write me this code.” But you understand how things actually work in production—trade-offs, edge cases, long-term consequences. Thats the real leverage. So use AI differently. Don’t just generate code, ask better questions: How does the business actually work? Why is this process done this way? What are we optimizing for? In my perspective it's the normies knowledge that's at risk. A critical thinker with technical skills will have the edge and should get far more out of AI than someone who just feels empowered by it.

small scale farming to get your kids fed

Second this. Just because you're aware doesn't mean everyone else even realizes they're talking to a SalesmanGPT.

insane story!

No worries. You are in the right place. This is how others feel as well and how software engineering will feel for new generations so yeah the bicycle comparison fits well.

What if Elon Musk had been born in a much poorer region/family instead?

I like your comment I think this is a pragmatic way to see oneself

I don't know if consuming world news makes you ethically better. That sounds a bit much and yes you can make something better in your society by what ever ... opening a hacker space in your village... I just do not get the relation between scrolling through news and affecting fellow human beings

You may be right. I just wanted to add that there are probably people stranded in Dubai right now because they booked a Berlin–Sydney flight with a stopover six months ago. Sometimes you simply can’t predict these things.

Yes, staying informed can help in some cases. But there are also many situations where it wouldn’t have made a difference. The amount of noise you have to sift through to “stay updated” is huge. At some point, it becomes a trade-off: consume a constant stream of news to maybe avoid a rare edge case — or tune it out and accept that very occasionally you might get unlucky.

sounds unrealistic to me. For military and industrial use cases yes but I think we are at least 5 probably 10 and more years away from that moment. I think there is not enough data, and hardware is not software so the scaling will be more difficult and use cases are so wide that I can not imagine a humanoid cooking and mowing the lawn and repairing the roof. Look how long the autonomous driving takes and this is just moving a car.

I wouldn't see it positive that we just need to rethink and offer platforms. As this platforms already exist Azure, AWS, GCP

I think this guy is a marketing genius!

dang on position 4

every day waking up, reading hn and thinking "This new AI xy is insane - it will change everything" then start my daily work in low code integration and thinking "How long will this last?

There is a great book about this from Rolf Dobelli "Stop Reading The News" https://www.goodreads.com/book/show/48581422-stop-reading-th...

Gave GPTZero a random ChatGPT text about finances. It was 84% confident, that it was entirely human writing

I think the opposite, they're great for skipping 'learning' and just get the results. yes, and cars skip the hours of walking, planes skip weeks of swimming, calculators skip the calculating ...

I feel the same. Do you think this is because the ADHD brain has so many ideas or is it the same for neuro-normal people?

Maybe it’s not that we’re getting stupid because we don’t use our brains anymore. It’s more like having a reliable way to make fire — so we stop obsessing over sparks and start focusing on building something more important.

I can relate. I started contracting, and the added sense of responsibility really helps me. I still work with teams within the organization and interface with them, but having some distance makes a difference. Wouldn’t it get lonely without a team?

Interesting thought, thanks. So what would we need? What if everyone effectively had a permanent microphone — via smartphones, smart speakers, cars, wearables — and all of that lived, spoken, emotionally charged data were fed into future LLMs?

On the surface, that sounds like a path toward richer models: less elite-written text, more everyday language, more non-academic thinking, more embodied culture. But it also raises a deeper question: whose reality would actually be learned?

Because even if the data were global, the selection, labeling, weighting, and training objectives would still be controlled somewhere.

And then there’s preference. Would people eventually choose their models the way they choose media ecosystems today? A Californian-progressive LLM. A post-socialist Eastern European LLM. A Palestinian LLM for discussing geopolitics. A deeply conservative, tradition-preserving LLM that treats modernity itself as suspect.

If that happens, AI wouldn’t homogenize thought — it would solidify worldviews into software as it is done in media today. Dialogue might actually become harder, not easier.

So the think may not be “AI Californication” alone, but AI Balkanization, ...

The open question is whether we can build models that don’t just represent cultures, but can genuinely inhabit multiple, conflicting ontologies without collapsing them into a single moral frame. That may be the hardest problem of all — and one that current LLMs, trained mostly on English-speaking upper layers of the internet, are nowhere near solving yet.

Same here. Starting to automate my homestead. Good luck!

very true! Mine are still working and after all are unable and unwilling to dedicate the whole time. They have plans or want to relax. Children are tiring. The west seems to be not only aging but also getting a bit lazy sometimes

Nice project! Was thinking if the Yolo library could be used for detection?