Comments URL: https://news.ycombinator.com/item?id=47554615

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=47402524

Points: 3

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Comments URL: https://news.ycombinator.com/item?id=47171980

Points: 2

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As to disposable software, it's harder to get traction/adaption when things constantly break or are slow or the experience is crappy in general.

To make it simpler - all else being equal - as a user would you prefer using highly reviewed/vetted/reliable software, or otherwise?

My bet is reliability is an invariant -- nobody wishes for software that crashes, leaks your private info, gives faulty output, is laggy to use and so on.

Comments URL: https://news.ycombinator.com/item?id=47073351

Points: 46

# Comments: 18

Consider something like TLA+. How can we make things such as that - be useful in an LLM orchestration framework, be human friendly - that'd be the question I ask.

So the developer will verify just the spec, and let the LLM match against it in a tougher way than it is possible to do now.

Many times, these GenAI tools can delete/modify code mistakenly.

I use LiveReview's git precommit features - so the review happens right before I commit code automatically. And it has saved me many (100s of) times.

Give LiveReview's Precommit checks a try.

Comments URL: https://news.ycombinator.com/item?id=46913797

Points: 4

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Once you look at the present engineering org compositions see what's the error in thinking.

There are other analogy issues in your response which I won't nitpick

Quick check: Do you want to go back to pre-industrial era then - when according to you, you had better options for clothing?

Personally, I wouldn't want that - because I believe as a customer, I am better served now (cost/benefit wise) than then.

As to the point about recursive quality decline - I don't take it seriously, I believe in human ingenuity, and believe humans will overcome these obstacles and over time deliver higher quality results at bigger scale/lower costs/faster time cycles.

What the role of an engineer in the new context - I am not speculating on.

I'd think there'll be a dip in code quality (compared to human) initially due to "AI machinery" due to its immaturity. But over-time on a mass-scale - we are going to see an improvement in the quality of software artifacts.

It is easier to 'discipline' the top 5 AI agents in the planet - rather than try to get a million distributed devs ("artisans") to produce high quality results.

It's like in the clothing or manufacturing industry I think. Artisans were able to produce better individual results than the average industry machinery, at least initially. But overtime - industry machinery could match the average artisan or even beat the average, while decisively beating in scale, speed, energy efficiency and so on.

Comments URL: https://news.ycombinator.com/item?id=46745968

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=46597654

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=46401925

Points: 1

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https://hn.algolia.com/?query=Chomsky%20and%20the%20Two%20Cu...

The oldest submission is from 15 y.o ago - that is 2010.

I resubmitted it - thinking - with the success of LLMs - felt this was worth a revisit from "how real-world scientific progress works" point of view.

Pearl defines a ladder of causation:

1. Seeing (association) 2. Doing (intervention) 3. Imagining (counterfactuals)

In his view - most ML algos are at level 1 - they look at data and draw associations, and "agents" have started some steps in level 2 - doing.

The smartest of humans operate mostly in level (3) of abstractions - where they see things, gain experience, and later build up a "strong causal model" of the world and become capable of answering "what if" questions.

Comments URL: https://news.ycombinator.com/item?id=46322626

Points: 2

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