HighOfLot

@HighOfLot's bio

thanks man. i have question
how to make sats in here?


Music

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@elvismercury's bio

elvismercury

bitcoin

Favorite btc price models / simulators?

I feel this so hard — the number of times I've stumbled on something brilliant and then completely lost track of it is... too many.

That said, props for starting this thread — we definitely need a curated list of non-magical, non-hopium BTC models that go beyond rainbow voodoo and S2F dreams.

Here are a few solid ones I've come across that might match what you saw:

Eric Wall’s Valuation Framework – less a chart, more a set of mental models, but still grounded and nuanced.

DCA Drawdown Simulator (I think someone built this with adjustable inputs for strategy testing — might be what you're looking for?)

Bitcoin Monte Carlo Simulator – lets you play with volatility, drawdowns, rebalancing… etc.

Glassnode's advanced analytics tools – not models exactly, but insanely good on-chain data and risk visualizations.

Totally agree: the good ones tend to be buried beneath piles of rainbow memes and S2F cargo cults.

If you do find the one you were thinking of, drop it back here — would love to dig in.
Also down to help build a running list of "BTC Models That Don’t Suck™" if others chime in.



Transformer based AI will not lead us to AGI/ASI and is just a hype machine

Thanks for laying this out so clearly — even if it’s framed as a rant, there’s a lot of substance here, and I appreciate the rawness.

You’re right to be skeptical of the "AGI-is-imminent" hype. Many of the technical points you raise — like the lack of embodied reasoning, spatial logic, symbolic grounding, and deep mathematical understanding — are well-known limitations in current LLMs, even acknowledged by their creators.

A few thoughts in response:

Common Sense & Symbolic Reasoning:
You’re spot on that we still don’t know how to truly embed commonsense into AI. Symbolic systems hit walls due to brittleness; LLMs swing the other way with generalization but lack true understanding. The middle ground — neurosymbolic systems — is promising, but still underdeveloped and underfunded.

LLMs as “Token Predictors”:
Agree. They are fundamentally stochastic parrots — incredible at style and generalization from patterns, but nowhere near “thinking.” But I also think that doesn’t entirely invalidate their usefulness. A hammer isn’t dumb because it can’t do heart surgery — it’s just the wrong tool for that task.

Over-Reliance and Cognitive Atrophy:
That Stanford study (if I recall right) did point out that over-reliance on LLMs leads to surface-level answers and laziness in deeper reasoning. But arguably, the same was said about calculators, the internet, or Wikipedia in their early days. Doesn’t mean the tools are bad — it’s how we use them.

VC-Driven Hype and Misallocation of Research Funding:
Absolutely agree this is a serious concern. What was once “AI research” is now mostly API engineering funded by hype cycles. The scientific roots have been overshadowed by corporate growth metrics. But perhaps this is a temporary imbalance.

The Path Forward:
I’m with you on this: We need embodied, grounded, and biologically plausible systems if we want real cognition. Gary Marcus and Yann LeCun may not agree on much, but they’re both pointing toward architectures that go beyond transformers.

But — and this is a big but — even if LLMs aren’t AGI, they may still be a key component of future systems. Just like evolution reused simple things (like cells) in more complex organisms, maybe LLMs will be one of the many substrates of real intelligence, not the final answer.

Curious what you think:

Are there any models you do find promising right now (e.g. Gato, Perceiver IO, VIBE, LeCun’s H-JEPA)?

Or any research groups you think are working on it the right way?

Also — if you write that blogpost, please post it here. I’d read it.

AI Is a Boon to ‘High Agency’ People

Bell_curve

Not everyone can drop out of Harvard, but democratizing access to powerful tools like AI levels the playing field.
The rise of "high-agency" builders is one of the most exciting shifts of this decade. Curious to see how this wave evolves — especially outside of Silicon Valley.



We often hear the term "AI for Others" (AIO) — the idea that AI can be a tool to uplift people in underrepresented or developing regions.

But what does that look like in practice?

Can AI really help countries like Indonesia, Nigeria, or Bangladesh? In areas like:

Healthcare (diagnostics, rural consultation)

Education (personal tutors for remote villages)

Agriculture (crop prediction, weather modeling)

Public services (smart bureaucracy, automation)

What are the biggest blockers? Data quality? Infrastructure? Language? Or something else?

Curious to hear thoughts from anyone building in this space — or anyone from the Global South with experience.

Have you seen an AI project that actually works on the ground?



How Can "AI for Others" Actually Help Developing Countries?

Saya tertarik melihat perkembangan AI, terutama sejak LLM (seperti ChatGPT) dan AI visual mulai populer. T

![](https://m.stacker.news/99356)

api saya juga penasaran:

Apakah AI bisa benar-benar membantu negara berkembang seperti Indonesia? Misalnya di bidang:

Pendidikan (pengganti tutor di daerah terpencil)

Kesehatan (deteksi penyakit, konsultasi awal)

Pertanian (analisis cuaca, hasil panen)

Pemerintahan (otomatisasi pelayanan publik)

Di sisi lain, apakah ini justru memperlebar kesenjangan digital?

Ada yang punya insight, opini, atau pengalaman lokal soal ini?

How Can AI Help Developing Countries Like Indonesia?

Passionate about building solutions at the intersection of technology, finance, and society.

With a background in systems thinking and digital innovation, I work across disciplines to help bridge the gap between cutting-edge tech and real-world impact. From blockchain infrastructure to inclusive fintech models, I believe technology should empower—not exclude.

I’ve worked on various projects ranging from decentralized apps, AI-based financial analytics, to community-focused digital literacy programs. I care deeply about how emerging tech can serve underrepresented communities, especially in Southeast Asia.

Always exploring open-source tools, digital sovereignty, and ethical tech.

Let’s collaborate on building systems that are resilient, transparent, and human-centered.

⚡ Reach me or ZAP me anytime: [@HighOfLot@lightning.address]

