eliancode

[eliancode @eliancodex]

GPT-6 Astra is the new #1 on BoardBench.

It scores 67.2 vs the previous SOTA Gemini 4 Argon at 66.3.

But it costs 2x as much ($0.56 per puzzle vs $0.28) for less than one point!

Check it out at https://t.co/ppfeCs6mzz

[eliancode @eliancodex]

do NOT use gemini 4 carbon on low efforts, it's often more expensive than the higher efforts

[eliancode @eliancodex]

@patience_cave google gemini 7 gluon

[eliancode @eliancodex]

@patience_cave i will say one letter to scare you:
g

[Theo - t3.gg @theo]

Remember open weight models? Those were fun

[eliancode @eliancodex]

Not frontier but it's nice

[DHH @dhh]

"We have frontier at home."

(GLM-5.3 Flash running on a pair of Sparks with Omarchy being accessed remotely via Tailscale from a MacBook Neo.)

[Taelin @VictorTaelin]

Since our pitch deck was uninformative, let me try to explain our whole thesis. Sorry if any part of it sounds obvious.

I argue that any reasonable definition of AGI must imply a system capable of learning as efficiently as a human. LLMs obviously don't pass this bar. When you talk to them, they don't learn anything, since the weights don't change. So, unless true infinite context lands, or GPUs become 10000x cheaper, the current methods won't scale to AGI, at least in that sense.

(Note that this has nothing to do with raw capabilities. Perhaps Fable 10 will be building Dyson spheres, yet still be unable to internalize new knowledge as efficiently as humans do. So, one might ask if we even *need* AGI in the sense above. I think we do, if only because a drastic drop in pre-training costs would make AI more democratic, and prevent one of the worst outcomes in my mind: over-concentration of power.)

As you know, modern AI is based on neural networks. What not everyone knows is that NNs are just very, very slow programs. Nobody in their right mind would sort a list using NNs, because that'd be millions of times slower than just using C. So, why do we implement *intelligence* with NNs?

As a thought experiment, imagine if we could take Fable's weights and distill them, not into a smaller model, but into a raw C program that was observationally identical to it. Literally, a C file with astronomically complex, tangled procedures that, when compiled and executed, behaved just like Fable 5.2.

If such a thing were possible, then we could have Fable at a million tokens per second. And here's the cool part: such a thing *is* possible. At least, as far as computer science is concerned, nothing says it isn't. So, why don't we do just that?

Because we don't know how to *train* a raw program.

Neural networks are slow, but they're differentiable, so we know how to train them. Step by step, slowly, but surely. Raw programs are fast, but we have no methods capable of training them. So we don't. We just stick to NNs, and accept that it takes $50 billion to train a model. We take that as "normal". But is it?

In theory, it shouldn't be like that. As the old saying goes: first you make it work, then you make it fast. We know AI works. Now, we will make it fast. That's the premise behind Symbolic AI. HOC's first milestone is to train a model comparable to GPT-2, with no NNs. If that works, we might have the fastest inference engine in the world. Not by 2x, but by 1000x.

So, in a way, HOC is like DeepSeek or Jev, in the sense that we're all about efficiency, but in a much more extreme way. People are aiming for hardware, but we believe there are still 1000x gains to extract in the software layers, if we're willing to rethink the whole stack from literal scratch.

Many laugh and say this is impossible, but they say so with no basis. They just observed that many failed to build symbolic AI models in the past, and concluded it is impossible, or very hard. That's not a formal proof. In fact, there are many reasons people in the past could've failed, yet the actual problem may end up being easier than most anticipate. Symbolic AI flourished half a *century* ago. Back then, they didn't have:

- As much knowledge as we do

- Massive GPU clusters

- Methods to scale Symbolic AI (Bend3, HVM5!)

So, just like everything that once seemed impossible, there's one core prerequisite to making it possible: *trying*.

That's what we'll do!

I don't think I'm smarter than all the brilliant researchers who worked on this problem in the past, and failed. I just think I'm equipped with tools, compute, and knowledge that they didn't have. So, standing on the shoulders of giants, and with enough dedication, I don't think it is so unlikely we stumble upon some symbolic algorithm from which, given enough compute, unexpected capabilities emerge.

If it does work, I do think business is straightforward. At the very least, we would be able to sell incredibly high quality datasets to big labs, but, at that point, just selling inference might work as well. Business is obviously not the wall, it is all on whether such massive software-based speedups exist, and whether we're able to discover them.

[Da7em @Da7_Tech]

A big Chinese lab reached out to me asking for help distilling Fable into their model. I said no.

I told them to use the millions of books that have no copyright instead. Offered to help pick them and to help train the model a different way. They only wanted distillation, which tells you a lot about the mindset there.

I've worked deep in this field and distillation is real. I wasn't convinced at one point. What I saw changed my mind completely.

I'm the biggest supporter of open source. I'm for releasing weights and for the freedom to research and build, because humanity only grows when knowledge gets shared. But let's be logical.

Nobody will convince me that labs shipping a model every month or less, each one a big leap, now can't ship a better one after three months.

Kimi K3.1 Pro isn't a big leap over Kimi K3 and it's been about 100 days.

DeepSeek still can't put out a respectable Pro model.

Zai used to release their strongest model in under two months. It's been three with nothing.

All I hear about Qwen 4 Max is that it's a letdown.

Kimi K3.1 Pro and DeepSeek Flash both show traces of Astra distillation. The leak showed Moonshot used it, and so did DeepSeek.

If this isn't the effect of Anthropic blocking distillation then what is it?

Chinese labs need to get serious about using the talented researchers they have and give their models an identity. Right now their models at their best are a cheaper alternative. I want them out of that box. I want to see what makes them different.

Anthropic has crossed lines too, no argument there. They destroyed millions of books, human knowledge that was in people's hands, and kept it for themselves. I'll never forgive them for that.

But Chinese labs rely on distillation more than they rely on better data.

I didn't work with them. Transparency and honesty matter to me, like you've known since I started here.

[eliancode @eliancodex]

was waiting on this, nice work

[eliancode @eliancodex]

@jukan05 including fable 5.2?

[eliancode @eliancodex]

@theaidesk_ no he loves hyping

[eliancode @eliancodex]

@AdamHoltererer congrats

[eliancode @eliancodex]

Fact checked by a real life ex-windows user:

[Screen @Screeendev]

Fact checked by a real life time windows users

[eliancode @eliancodex]

@R2Cdev_ @Angaisb_ interesting, for me Haiku 5.5 > Grok 4.7 and GPT-6.1 Terra ~= Gemini 4 Argon

[eliancode @eliancodex]

admittedly this is pretty much a redundant take nowadays

[eliancode @eliancodex]

monorepos are the way

[eliancode @eliancodex]

@AdamHoltererer holy shit

[eliancode @eliancodex]

@kamkmgamer actually, i used all my limits on opus 5.5, before fable even released :(

[eliancode @eliancodex]

black magic, my usage went back up to 6%🤣

[eliancode @eliancodex]

bro

[eliancode @eliancodex]

@R2Cdev_ @txbrraa unfortunately, gpt-image-2.6 still has terrible continuity
though it's obviously not as bad as in this port