After reading the wiki page more thoroughly, there were just a bunch of different issues. Goes to show what happens when you make a seemingly benign but fundamental change in a system full of parts designed for something different.

1. https://en.wikipedia.org/wiki/Aluminum_building_wiring

This is a direct conflict in framing. They clearly do not see Claude Code as a "tool in a workflow" but instead as a service that will eventually replace all programmers.

I think the self-evident quality of the various parts of the Claude Code universe is a pretty obvious indicator of the problems with that approach. It is still important to understand a party's thinking if you want to understand their position.

> They're swimming upstream. Trying to maintain a rapidly shrinking moat and not being very creative about it. Making enemies of your users is often a failing strategy.

Time will tell, but I agree that they are indeed in a tough spot. Probably not for the reasons that they think.

- Charles Babbage, https://archive.org/details/passagesfromlife03char/page/67/m...

EDIT: This is a new iteration of an old problem. Even GIGO [1] arguably predates computers and describes a lot of systemic problems. It does seem a lot more difficult to distinguish between a "garbage" or "good" prompt though. Perhaps this problem is just going to keep getting harder.

1. https://en.wikipedia.org/wiki/Garbage_in,_garbage_out

I was simply replying to the original statement that China doesn't kill protesters in the street. The notion is so risible that in hindsight it may well have been sarcasm or bait.

But, if you wish to expand the scope of this discussion, sure. There are several clear distinctions between the (horrible) events you list and what happened in Tiananmen Square.

The most obvious is that we are free to talk about them now. I submit that the Chinese state's continued censorship of the subject is a sign that (1) the state is still complicit in these crimes and (2) it is not "doing great".

The scale of brutality is also just incomparable. I say this fully agreeing the events you list were terrible. The horrors committed at Tiananmen Square were simply on another level.

It's the prototypical example of authoritarian crackdowns and mass slaughter of innocent protestors.

Discussion or even mention of it is still forbidden in China.

1. https://en.wikipedia.org/wiki/1989_Tiananmen_Square_protests...

To paraphrase the quote, quantity has a quality of its own.

1. https://en.wikipedia.org/wiki/Quis_custodiet_ipsos_custodes%...

EDIT: or to rephrase, this proposal is opt-in (device attests the user is a minor) not mandatory (device is required to attest the user is an adult)

This setting is stored in a secure enclave and survives factory resets.

I will note that these two systems are not mutually exclusive. There are plenty of ways to "think of the children" that don't trample on everybody's freedom.

But I (clearly) must be wrong, sorry to disagree with the spokesman of America.

For this to be a "classic motte and bailey" you will need to point us to instances where _the original poster_ suggested these (the "bailey", which you characterize as "rust eliminates all bugs") things.

It instead appears that you are attributing _other comments_ to the OP. This is not a fair argumentation technique, and could easily be turned against you to make any of your comments into a "classic motte and bailey".

Rust just feels natural now. Possibly because I was exposed to this harsh universe of problems early. Most of the stupid traps that I fell into are clearly marked and easy to avoid.

It's just so easy to write C++ that seems like it works until it doesn't...

To be cheeky, there are at least three possibilities you are writing off here: we build _less_ software, we hire _more_ engineers, or things just kinda stay the same.

More on all of these later.

> I am not convinced that software has a growing market

Analysis of market dynamics in response to major technological shocks is reading tea leaves. These are chaotic systems with significant nonlinearities.

The rise of the ATM is a classic example. An obvious but naive predicted result would be fewer employed bank tellers. After all, they're automated _teller_ machines.

However, the opposite happened. ATMs drastically reduced the cost of running a bank branch (which previously required manually counting lots of cash). More branches, fewer tellers per branch... but the net result was _more_ tellers employed thirty years later. [1]

They are, of course, now doing very different things.

Let's now spitball some of those other scenarios above:

- Less "software" gets written. LLMs fundamentally change how people interact with computers. More people just create bespoke programs to do what they want instead of turning to traditional software vendors.

- More engineers get hired. The business of writing software by hand is mostly automated. Engineers shift focus to quality or other newly prioritized business goals, possibly enabled by automating LLMs instead of e.g traditional end to end tests.

- Things employment and software wise stay mostly the same. If software engineers are still ultimately needed to check the output of these things the net effect could just be they spend a bit less time typing raw code. They might work a bit less; attempts to turn everyone into a "LLM tech lead" that manages multiple concurrent LLMs could go poorly. Engineers might mostly take the efficiency gains for themselves as recovered free-ish (HN / Reddit, for example) time.

Or, let's be real, the technology could just mostly be a bust. The odds of that are not zero.

And finally, let's consider the scenario you dismiss ("more software"). It's entirely possible that making something cheaper drastically increases the demand for it. The bar for "quality software" could dramatically raise due to competition between increasingly llm-enhanced firms.

I won't represent any of these scenarios as _likely_, but they all seem plausible to me. There are too many moving parts in the software economy to make any serious prediction on how this will all pan out.

1. https://www.economist.com/democracy-in-america/2011/06/15/ar... (while researching this, I noticed a recent twist to this classic story. Teller employment actually _has_ been declining in the 2020s, as has the total number of ATMs. I can't find any research into this, but a likely culprit is yet another technological shock: the rise of mobile banking and payment apps)

The art of knowing what work to keep, what work to toss to the bot, and how to verify it has actually completed the task to a satisfactory level.

It'll be different than delegating to a human; as the technology currently sits, there is no point giving out "learning tasks". I also imagine it'll be a good idea to keep enough tasks to keep your own skills sharp, so if anything kinda the reverse.

The current crop of models do not "sleep" in any way. The associated limitations on long term task adaptation are obvious barriers to their general utility.

> When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

One of the key functions of REM sleep seems to be the ability to generalize concepts and make connections between "distant" ideas in latent space [1].

I would argue that the current crop of LLMs are overfit on recall ability, particularly on their training corpus. The inherent trade-off is that they are underfit on "conceptual" intelligence. The ability to make connections between these ideas.

As a result, you often get "thinking shaped objects", to paraphrase Janelle Shane [2]. It does feel like the primordial ooze of intelligence, but it is clear we still have several transformer-shaped breakthroughs before actual (human comparable) intelligence.

1. https://en.wikipedia.org/wiki/Why_We_Sleep 2. https://www.aiweirdness.com/

It feels accidental, but it's definitely amusing that the models themselves are aping this ethos.

1. https://en.wikipedia.org/wiki/Black_start 2. https://en.wikipedia.org/wiki/2025_Iberian_Peninsula_blackou...

Blender has methods and tools to _approximate_ doing this. It has a revolve tool... where the key parameter is the number of steps.

This is not a revolution, it's an approximation of a revolution with a bunch of planar parts.

BREP as I understand it allows you to describe the surfaces of this operation precisely and operate further on them (e.g. add a fillet to the top edge).

Ditto for things like circular holes in objects. With blender, you're fundamentally operating on a bunch of triangles. Fundamental and important solid operations must be approximated within that model.

BREP has a much richer set of primatives. This dramatically increases complexity but allows it to precisely model a much larger universe of solids.

(You can kinda rebuild functionality that geometric kernels have with geometry nodes now in blender. This is a lot of work and is not a great user interface compared to CAD programs)