If you read that Wirth 1995 paper (A Plea for Lean Software) referenced by the OP, following paragraphs answered your question:

“ To some, complexity equals power

A system’s ease of use always should be a primary goal, but that ease should be based on an underlying concept that makes the use almost intuitive. Increasingly, people seem to misinterpret complexity as sophistication, which is baffling — the incomprehensible should cause suspicion rather than admiration.

Possibly this trend results from a mistaken belief that using a somewhat mysterious device confers an aura of power on the user. (What it does confer is a feeling of helplessness, if not impotence.) Therefore, the lure of complexity as sale incentive is easily understood; complexity promotes customer dependence on the vendor.”

I am typing (no screenshots or copy and paste) this 30 year old wisdom in to reply here as an archived reminder for myself.

After leaving my previous day job, I have some downtime to get back to thinking and realizing how much I love reading and thinking.

Contemplating the old RTFM, I started a new personal project called WTFM and spends time writing instead of coding. There is no agenda and product goals.

https://wtfm-rs.github.io/

There are so many interesting things in human generated computer code and documentation. Well crafted thoughts are precious.

That is a great point. People in this group are programming at a different abstraction level, i.e., allocating computing resources, both human and machine resources.

Now AI agents are cheap but they generate a lot of slop, and potential minefields that might be costly to clean. The ROI will show up eventually and people in the second group will find out their jobs might be in danger. Hopefully a third group will come to save them.

I have been diving deeply in the Rust community and ecosystem and really enjoyed reading the decade of real engineering poor into it, from RFCs to std, critical crates such as serde, and testing practices. What a refreshing world.

Compared to the mess created by Node.js npm amateur engineers, it really shows who is 10x or 100x.

Outsourcing critical thinking to pattern matching and statistical prediction will make the haystacks even more unmanageable.

The writing experience on Kindle Scribe is pretty good as well. I only use it for quick marginal notes and underlines. Sometimes drawing diagrams.

But I used it mostly for reading.

Thanks for sharing, appreciate it.

Would you say it is convenient to use it for writing, say, 500 words at a go?

If you’re good at hand writing, there is no difference between it and a real paper notebook. There are also many notebook templates.

I use a Kindle Scribe. The iPhone can share any .txt, .html, .epub, .PDF content from the web to Kindle App so I can sync on the Kindle Scribe and read offline later.

I read all my Rustdoc and mdBook documentation this way. Even the ones I made.

I am in the middle of reading a fascinating book about the early days of computing: Turing’s Cathedral by George Dyson. It really opened my eyes to American engineering craft post WWW II.

We seem to take everything for granted now and forget what real engineering is like.

This review is 13 years old by itself:

https://www.theguardian.com/books/2012/mar/25/turings-cathed...

24 hours later, I finally found a little time and energy to write down some thoughts before they become information fat.

https://ontouchstart.github.io/manuscript/information-fat.ht...

How much effort would it take GenAI to write a browser/engine from scratch for GenAI to consume (and generate) all the web artifacts generated by human and GenAI? (This only needs to work in headless CI.)

How much effort would it take for a group of humans to do it?

I'm not sure about what you mean with your first sentence in terms of product.

But in general, my guess at an answer(supported by the results of the experiment discussed on this thread), is that:

- GenAi left unsupervised cannot write a browser/engine, or any other complex software. What you end-up with is just chaos.

- A group of humans using GenAi and supervising it's output could write such an engine(or any other complex software), and in theory be more productive than a group of humans not using GenAi: the humans could focus on the conceptual bottlenecks, and the Ai could bang-out the features that require only the translation of already established architectural patterns.

When I write conceptual bottlenecks I don't mean standing in front of a whiteboard full of diagrams. What I mean is any work the gives proper meaning and functionality to the code: it can be at the level of an individual function, or the project as a whole. It can also be outside of the code itself, such as when you describe the desired behavior of (some part of) a program in TLA+.

For an example, see: https://medium.com/@polyglot_factotum/on-writing-with-ai-87c...

That is a wonderful write up.

“This is a clear indication that while the AI can write the code, it cannot design software”

To clarify what I mean by a product. If we want to design a browser system (engine + chrome) from scratch to optimize the human computer symbiosis (Licklider), what would be the best approach? Who should take the roles of making design decisions, implementation decisions, engineering decisions and supervision?

We can imagine a whole system with human out of the loop, that would be a huge unit test and integration test with no real application.

Then human can study it and learn from it.

Or the other way around, we had already made a huge mess of engineering beasts and machine will learn to fix our mess or make it worse by order of magnitude.

I don’t have an answer.

I used to be a big fan of TDD and now I am not, the testing system is a big mess by itself.

> That is a wonderful write up.

Thanks.

> what would be the best approach?

I don't know but it sounds like an interesting research topic.

There should be a volume about maintenance of our bodies and minds (without depending on technologies that consume a lot of energy and resources).