But in much of the world, setting up PV is economically sound simply because it displaces a certain amount of kWh generated over the course of a year from other sources that are more polluting and more expensive.

In this regime, the dynamics of production over time don't matter yet.

At some point, when renewable generation has very high penetration, you'll reach a point where building more is uneconomical, and to then displace the remaining other power sources you'll need to overpay (ignoring externalities).

However, that's assuming no technological change on the way there, which is a whole separate topic.

This is imo currently the top chatbot failure mode. The insidious thing is that it often feels good to read these things. Factual accuracy by contrast has gotten very good.

I think there's a deeper philosophical dimension to this though, in that it relates to alignment.

There are situations where in the grand scheme of things the right thing to do would be for the chatbot to push back hard, be harsh and dismissive. But is it the really aligned with the human then? Which human?

That's a self-evident thing to say, but it's worth repeating, because there's this odd implicit notion sometimes that you train on some cost function, and then, poof, "intelligence", as if that was a mysterious other thing. Really, intelligence is minimizing a complex cost function. The leadership of the big AI companies sometimes imply something else when they talk of "generalization". But there is no mechanism to generate a model with capabilities beyond what is useful to minimize a specific cost function.

You can view the progress of AI as progress in coming up with smarter cost functions: Cleaner, larger datasets, pretraining, RLHF, RLVR.

Notably, exciting early progress in AI came in places where simple cost functions generate rich behavior (Chess, Go).

The recent impressive advances in AI are similar. Mathematics and coding are extremely structured, and properties of a coding or maths result can be verified using automatic techniques. You can set up a RLVR "game" for maths and coding. It thus seems very likely to me that this is where the big advances are going to come from in the short term.

However, it does not follow that maths ability on par with expert mathematicians will lead to superiority over human cognitive ability broadly. A lot of what humans do has social rewards which are not verifiable, or includes genuine Knightian uncertainty where a reward function can not be built without actually operating independently in the world.

To be clear, none of the above is supposed to talk down past or future progress in AI; I'm just trying to be more nuanced about where I believe progress can be fast and where it's bound to be slower.

The compression ratio is the vibe coding gain.

I think that way of phrasing it makes it easier to think about boundaries of vibe coding.

"A class that represents (A) concept, using the (B) data structure and (C) algorithms for methods (D), in programming language (E)."

That's decodeable, at least to a narrow enough distribution.

"A commercially successful team communication app built around the concept of channels, like in IRC."

Without already knowing Slack, that's not decodable.

Thinking about what is missing is very helpful. Obviously, the business strategic positioning, non technical stakeholder inputs, UX design.

But I think it goes beyond that: In sufficiently complex apps, even purely technical "software engineering" decisions are to some degree learnt from experiment.

This also makes it more clear how to use AI coding effectively:

* Prompt in increments of components that can be encoded in a short prompt.

* If possible, add pre-existing information to the prompt (documentation, prior attempts at implementation).

If you've recently used AI tools for professional coding work, tell us about it.

What tools did you use? What worked well and why? What challenges did you hit, and how (if at all) did you solve them?

Please share enough context (stack, project type, team size, experience level) for others to learn from your experience.

The goal is to build a grounded picture of where AI-assisted development actually stands in March 2026, without the hot air.

Comments URL: https://news.ycombinator.com/item?id=47388646

Points: 434

# Comments: 616

I'd be curious to hear your thoughts on how the "fixer", who sounds rather ineffective as an executive, came into this position, in what sounds like overall a rather effective organization.

I've been personally thinking quite a bit about what makes organizations work or not work recently, and your story is quite interesting to me as a glimpse into a kind of organization that I've never seen from the inside myself.

People do change their minds when the pain becomes too intense to ignore, but that is what it takes.

Doesn't exclude the possibility of short term distribution, though.

Happy to learn more about this if anyone has more information.

But then, the writing is also never great. I've tried a couple of times to get it to write in the style of a famous author, sometimes pasting in some example text to model the output on, but it never sounds right.

You asked people what their project was for and you'd get a response that made sense to no one outside of that bubble, and if you pressed on people would get mad.

The bizarre thing is that this time around, these tools do have a bunch of real utility, but it's become almost impossible online to discuss how to use the tech properly, because that would require acknowledging some limitations.

Walk! At 50 meters, you'll get there in under a minute on foot. Driving such a short distance wastes fuel, and you'd spend more time starting the car and parking than actually traveling. Plus, you'll need to be at the car wash anyway to pick up your car once it's done.