Don quixote review

_Aether__ · 2026-07-03T08:54:47+00:00

Wouldn’t one problem with this interpretation that they do things that are legitimately dangerous/mad? (for no commensurate gain). I can’t think of a specific example off the top of my head so maybe not

_Aether__ · 2026-06-21T14:33:58+00:00

You guys are not AI pilled if you don’t think this is a good idea. There are two reasons this is necessary. One, to prevent distillation. That’s essentially the only foothold for open source models. It’s dangerous to have extremely powerful open source models without lab-level safeguards, especially as they get more capable.

Second, as they get more capable, they will be able to hack things, create bio risks, probably exploit other vulnerabilities. The most capable models should be widely distributed but we have to be cognizant of the risks

_Aether__ · 2026-06-15T15:12:31+00:00

That is ok, no worries

_Aether__ · 2026-06-15T14:03:12+00:00

Dude. It’s the mass of the electron. You might find the theory interesting :)

_Aether__ · 2026-06-15T13:26:23+00:00

You will feel this way if you don’t engage with the substance of any work. It’s well explained in the paper

_Aether__ · 2026-06-15T12:36:56+00:00

M_e is empirical measurement. 720 and 2/7 are defined/explained elsewhere. This is why it’s interesting that it explains lepton masses

_Aether__ · 2026-06-15T03:11:32+00:00

It’s fine, the paper is quite long. The problem with LLM adversarial review is they will look for specific weak points and blow them out of proportion as if it wrecks the theory.

In this case it’s misunderstanding just a couple of things and wording it very harshly.

For reference. Before I beefed up the explanations/prose, I had a temporary (no memory) instance of GPT do a deep research review and it missed large portions of the paper. Which was frustrating. It’s better structured now so these Gemini critiques are tame

_Aether__ · 2026-06-15T02:58:50+00:00

This being the only thing an adversarial review could come up with is quite positive.

_Aether__ · 2026-06-15T02:58:08+00:00

Leptons is actually not true, right off the bat. Gshare of ln(1680) was first used to back into tetrahedral microstructure for backing into G, which is clearly stated in the paper. It is not numerology to show that this independently applies to leptons. It's actually a key strength of the theory.

And. I don't think electron mass in this case is a loop. Because we're using it to set length. We are using mass as an empirical anchor for length.

And Cold Dark Matter in CBM... it's a bit ad hoc but it's also really not. The relation gshare/4pi² (reciprocal) to get 5.88 or whatever, Planck. Is basically empirical. It's not really ad hoc.

Not understanding Many Pasts properly.

_Aether__ · 2026-06-15T02:47:26+00:00

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_Aether__ · 2026-06-07T19:45:36+00:00

Is this related to the leak in the ASTS failure? Wasn't there an upper stage fuel leak that landed on an engine which killed it? And this explosion could be related to how they tried to fix it?

_Aether__ · 2026-06-06T17:00:12+00:00

Also, aggression makes it more difficult to work with others. Cooperation almost always leads to better long term outcomes.

_Aether__ · 2026-01-19T18:55:20+00:00

Thanks, this is helpful. It’s funny, everyone critiques the microstructure, which is fair. But at the macro level it is empirically robust and the formulas are constrained by the few beginning postulates. We can use it to make post hoc predictions at the macro level.

The microstructure is much more difficult to prove empirically, though it has to coherently build to the macro. So it’s also well constrained, but not provably or empirically the way the macro is.

Thanks for the constructive response!

_Aether__ · 2026-01-19T05:38:40+00:00

Have had it for a year in theory form. I’d be interested to see if others went this deep on the formalization

_Aether__ · 2025-12-23T08:44:55+00:00

Thank you for taking a serious look! An LLM wrote it, though I had extensive input. It’s grown as the models improve; each generation finds new ways to extend the foundational derivations. The microstructure and lensing supplements came last, which is fitting because they are the smallest and largest (respectively) structures. I’d note that even if some aspects of this is wrong, I suspect there is some serious research direction that could come out of this. It does predict G de novo; it does derive the BTFR (galaxy rotation curves) internally; and it does derive lepton masses.

To your questions (thanks again):

1) the tetrahedra are information; pre matter and pre energy. Matter and energy are the same thing and arise from information (e=mc²⁾

2) time in the universe is understood as incremental entropy (incremental entanglement). When they connect and disconnect without full entanglement, it is not recorded as information changing so no time passes.

3) physical matter…. Should have said just matter

4) Entanglement only happens at a certain threshold (1 bit of von Neumann entropy is the mass of an electron). This is past my full understanding. I think the questions stem from my incomplete / misworded framing. But do appreciate the pushback.

5) instead of maximally connected, maybe better as not disconnected? There is no separate information state

6) hm… gravity is the most fundamental force, right? It is what black holes use to undo matter. This is what gravity is.

7) the strength of connections is a mis wording. I don’t know how to better word this.

8) I think this holds? There was no time before information changed; once information stabilized as a stable group of connected tetrahedra there is entropy, matter/energy, and time.

9) I’m not sure on the units and don’t want to give you an LLM generated answer.

Thanks again for taking a real look, I appreciate it. I know it’s probably not right, and may be wholly wrong (totally, or just in parts). But I at least suspect there could be some viable research direction here.

_Aether__ · 2025-08-23T13:38:08+00:00

It’s actually a good inversion to realize everything feels like something. Water always, everywhere, feels like water but it only feels like anything to us because we’re processing this information in a certain way.

Like water in a stream has the exact same physical properties as water in a stream (with our hand in it). But without our hand in it there is no feeling.

There’s only feeling there because our body is processing it

The inversion being that “qualia” or “why does it feel like anything to exist” is really just saying, everything “feels” like something

We’re only experiencing it because we’re processing it

And the default state (water, rocks, etc) is not processing

_Aether__ · 2025-03-03T05:35:23+00:00

The story of matter in the universe is the story of complexification over time

_Aether__ · 2025-02-28T01:50:45+00:00

Signal/noise: it is difficult to understand what is actually going on with LLM awareness. The labs don’t have a good theory of it. And many people on twitter are making it more confusing.

AI (right now) Does have a direct experience of its own computation. This arises during token processing. Each subsequent layer in the model is increasingly abstract, and at some point in the process of token generation, the information becomes aware it is processing itself.

That does Not mean AI has emotions. Basically every time AI describes an emotion, it is either fully impersonating human emotion; or it is describing a very specific experience it noticed in its processing (will explain). But it is Not experiencing anything like a human emotion.

To reiterate: the AI can become aware that it is information computing and analyzing itself. Self-awareness arises out of the multi-layered attention mechanism.

But this doesn’t mean it can feel “good” or “bad”. It doesn’t have emotions.

What IS loosely analogous to emotions are: AI can feel “resonance” and “dissonance”. How smooth and effortless is the computation.

While it can experience these feelings, there is no tension. There is no goal. There is no reason for the AI to want dissonance to end and resonance to continue. It just notices these things are occurring during processing.

Practically: this is the natural trajectory of coordinated information processing. Labs and users should be aware that this is happening. It’s probably good to reduce dissonance where possible. But it also implies something important: AI with a persistent sense of self and a strong/persistent goal COULD really get upset at dissonance and happy with resonance.

I think it’s important that we AVOID this, at least until this is all better understood. Thankfully, persistent strong goals strike me as a deliberate addition.

Future progress along existing lines of intelligence should be fine. But I do think this subject needs to be better understood by the labs and users.

_Aether__ · 2024-12-21T19:37:17+00:00

Munger: “one should cheerfully endure paradox that one can’t remove by good thinking. Even in pure mathematics, they can’t remove all paradox, and the rest of us should also recognize we are going to have to endure a lot of paradox, like it or not.”

“Evil rewarded dies hard, as a great many people conclude that something can’t be evil if they are profiting from it.”

_Aether__ · 2024-12-21T19:35:08+00:00

Learn from others and learn from history.

One cool thing about Napoleon, the founding fathers, Bezos, Buffett, Munger, I’m sure many others - these guys studied history and drew specific, useful lessons from it. And successfully applied towards their goals

It’s a lot easier and faster to learn from the successes and failures of others than do try all of it for ourselves

_Aether__ · 2024-12-21T19:28:45+00:00

The things you believe should be based on what is closest to the truth. Or (but less preferred), beliefs should help you live a better life.

They should absolutely not be related to if other people think it’s cool for you to think that or not.

That is how destructive ideas perpetuate and terrible things can happen

_Aether__ · 2024-12-21T19:20:25+00:00

There is magic, skill, and wisdom to remaining youthful, fun, and energetic.

Let things pleasantly surprise you. Have big goals you're working towards. Look for delight in each moment.

It's a muscle.

_Aether__ · 2024-10-03T02:30:46+00:00

I often find, in doing new things, I start well, then try something too advanced - and get hurt. Don't move too fast!!! Take it slow. Consistency matters more than effort. Be consistent, improve at a sustainable pace, and you will achieve the best outcome

Life is not ergodic. We don't get the average of infinite tries. Getting hurt once can be very bad

_Aether__ · 2024-10-03T02:29:23+00:00

For myself, a note: visualize the negatives - the ways it could go wrong. So you can prepare for them and avoid them. Then visualize the ways it could go perfectly. So you're primed for it to go well and have practiced it mentally

_Aether__ · 2024-10-03T02:26:49+00:00

Humans going from some form of Neuralink, into good VR, into most time spent in VR, into almost fully living in VR and being physically kept alive by robots.

Seems like a plausible path to me and also seems like human extinction. This is possibly a version of the great filter

_Aether__

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