> Now, the kinds of changes that were simply unrealistic before are now entirely realistic. A refactor/migration that would have taken 6 months before, you can now get a working version done in a day, and another few days of QA.

Significantly changing a system implementation is a function of understanding, not time.

Given a starting solution state being A and a desired functionality state of A', the tooling used to codify the A -> A` delta is largely irrelevant. Specifically, in order to successfully deliver major product realignment requires awareness of what exists currently and understanding of what needs to be.

Code is neither stone nor clay.

  When making software, remember that it is a snapshot of
  your understanding of the problem.  It states to all,
  including your future-self, your approach, clarity, and
  appropriateness of the solution for the problem at hand.

  Choose your statements wisely.

Honestly, I don't see how this is a problem.

> Which in the end is kinda the point of it.

Absolutely.

> Like your post says, with programming, it's mostly just a race to what achieves the goal ...

Unless the joy of whatever programming one does is found in the path taken.

> I started learning ARM assembly so I could make a lisp implementation in it. It will be useless, no one will use it, and an AI could bang one out in a few minutes ...

Who cares if anyone uses it or if some other tooling could replicate what you choose to do if, in doing so, you enjoy it.

Much like the "damn good croissants" you make, it doesn't matter what others could make. You make damn good croissants.

Your happiness is yours. Enjoy the path you take to find it.

If you perfer using the same language for both frontend and backend work, yet would like to have one more semantically rich than TypeScript, consider Scala.

For the frontend, ScalaJS[0].

For the backend, Scala[1].

0 - https://www.scala-js.org/

1 - https://scala-lang.org/

The funny thing about Servant Leadership[0] is that it demands a leader see themselves as an equal with those whom they lead. Thus requiring acknowledgement that the title a person has does not establish their value, no matter what an org chart proclaims.

0 - https://en.wikipedia.org/wiki/Servant_leadership

0 - https://www.ia.nato.int/niapc/Product/BCWipe_178

It is also possible that the proverb I provided to you is the origin of the oft quoted:

  Better to remain silent and be thought a fool than to speak 
  and remove all doubt.

Any reasonable definition of understanding is not dependent upon "whatever vibe you are going for", but instead must include at least an English dictionary definition of "understand" such as:

  to grasp the meaning of[0]

  to lay hold of with the mind[1]

  to lay hold of with the mind the meaning of

>> Understanding is a state of mind

> This is meaningless, it is a circular definition at best.

See above to as to why there is meaning in what I wrote.

>> It exists entirely within the individual and nowhere else

> Then why are we talking about it? What is the point if it is something that can only be defined per individual?

I like to think analyzing fundamental premises, often implicit, explicitly can help to identify fallacious positions.

> We are still not getting anywhere because you have not prescribed criteria to determine whether [an LLM] understands.

My apologies for being opaque. Let me clarify:

  LLMs do not "understand".  People interpreting LLM output
  are the only entities involved which can "understand",
  because "understanding" exists strictly within each
  person who possesses it.

1 - https://www.merriam-webster.com/dictionary/grasp

"Might be out of sync" is a unique spelling of "crucified online". :-D

Understanding is a state of mind. As such, it exists entirely within an individual and nowhere else.

For example, take any two university professors who teach the same subject where one only speaks Arabic and the other only speaks Vietnamese. Each will not be able to understand what the other says, regardless their understanding of the shared topic.

> I argue that for any proper definition [of understanding] you provide which humans satisfy, a strong LLM is very likely to satisfy that as well.

This is demonstrably incorrect as detailed above. There is no "understanding" LLMs can satisfy as we know it, since to certify said "understanding", it requires interpretation by a person to "know" an LLM "understands."

> I also would not argue that humans are "simple token generators". That is not what I said.

That is the essence of what you wrote, unless you object to my use of "simple" instead of "statistical". In this context, I postulate this is a distinction without difference.

> I said that just about everything can fall under the classification of "statistical token generators" at an abstract level, so it isn't a useful distinction.

This only holds if one subscribes to statistical token generators being a/the fundamental underpinning of "everything". Here is a proof by contradiction:

  If everything can be classified as a derivative of
  statistical token generation, how does one explain
  quantum physics?

The onus is not mine to disprove a hypothesis you have chosen to mention in passing. The responsibility is yours to prove said hypothesis or at least contribute meaningfully with some amount of credible research.

Or try to learn from Proverbs 17:28[0]:

  Even fools are thought wise if they keep silent, and 
  discerning if they hold their tongues.

0 - https://www.biblegateway.com/passage/?search=proverbs%2017:2...

> You can say that about everything in a human brain.

> What's the most significant, strongmanned, core difference that makes silicon doing number crunching "nothing more" and brains "something more"?

The fact that you formulated this question, in and of yourself, without "prompting" from me or anyone else.

Cogito, ergo sum.[0]

0 - https://en.wikipedia.org/wiki/Cogito,_ergo_sum

  [LLMs] are statistical token generators whose results are 
  dependent upon their training data set and involve a degree 
  of randomness.

  During conversation, we are statistical token generators 
  whose results are dependent upon our training set.

> That’s not what they said.

How did I misquote and/or mischaracterize any the above?

It is always a joy when a person, such as yourself, finds the irony in my moniker.

Thank you for this.

In this media (comments in HN threads), all I can do is interpret what people write. ;-)

> And "hallucinating" to mean "outputs plausible sounding gibberish that doesn't hold together consistently". Of course there's no actual hallucination going on.

This may very well be what you know to be true and I have no reason nor desire to assume otherwise. The problem is... Many people use the word "hallucinating" in this context literally and not metaphorically.

Since I do not know you, how am I to tell the difference?

> Whatever you might think about your own abilities, most individuals can't tell the difference.

I have yet to see an LLM say "hello" to a neighbor. I have done so and can definitively assure you "most individuals" can tell the difference.

Yes you are, regarding LLMs at least. Here's why:

  just for fun I asked a reasonably smart LLM to ...
  [be] capable of understanding if it's gone off on
  a hallucinatory path ...

You then went on to state:

> If you know something rare and the LLM does not, you'll immediately see when it's hallucinating an answer or answering factually.

Again, "hallucinating" is not something an algorithm can do. Also, determining factuality is again subjective based on the person assessing the information.

>> Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans ...

> This doesn't make any sense at all. Was this supposed to be a gotcha?

No, it was meant to be an explanation as to the difference between "memorization" and "understanding." In this context, people pick the algorithm they determine applicable and then the question of memorization is relevant.

> An LLM is trained on problems defined by other humans, and identifies which algorithm it must use based on pattern recognition.

Funny that you make this argument here, where when I wrote elsewhere in this thread:

  [LLMs] are statistical token generators whose results are
  dependent upon their training data set and involve a
  degree of randomness.

  Nothing more.

  ...

  It is pattern recognition, a task in which ANNs excel.

  During conversation, we are statistical token generators 
  whose results are dependent upon our training set. 
  Seriously, write that definition out rigorously. It 
  encompasses virtually everything. It is totally 
  meaningless. So to say "nothing more" is effectively also a 
  tautology.
  
  This argument was asinine in 2024. It is insane to be 
  saying these things in 2026. Where have you been?

  ...

  It absolutely understands how to do math, by whatever
  reasonable definition you want to provide to the word
  "understand".

Are LLMs ANNs? Which themselves are pattern recognition algorithms (hint: they are)?

OR (setting aside the ad hominems you kindly provided)

Do LLMs possess "understanding" of concepts such as abstract mathematics (defined and interpreted by humans) and we, as simple humans, nothing more than statistical token generators as you assert?

Because it cannot be both.

If you do not see a difference between humans conversing (known consciousness as defined by humans) and the output of an LLM (known algorithms as defined by humans), I don't know what to say.

LLMs are neither smart nor stupid. They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.

> You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path ...

Again, LLMs do not "hallucinate." They are statistical token generators whose results are dependent upon their training data set and involve a degree of randomness.

Nothing more.

See also anthropomorphism[0].

> More precisely it's that [LLMs] can't do the math internally but they're quite capable of producing the tool that does the math.

This still falls under the purvey of statistical token generation. To wit, given enough variations of:

  bc -e '1 + 2'
  bc -e '41 + 1'
  ...

It is pattern recognition, a task in which ANNs[1] excel.

0 - https://en.wikipedia.org/wiki/Anthropomorphism

1 - https://en.wikipedia.org/wiki/Neural_network_(machine_learni...

> How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?

Humans identify which "algorithm they have memorized" to use beforehand, due to the problem to be solved being defined by other humans, which leads to...

Wait for it...

Understanding.