model capabilities

xuan @xuanalogue

2026-09-24 — saved image

xuan (ʂyɛn / sh-yen) [verified] @xuanalogue · 14h

have to say I'm a bit in denial about how good the latest models have gotten at creating animated videos from scratch via html / blender / etc

definitely wouldn't have predicted they'd get this good this fast at both visual understanding and computer use

model capabilitiesanimationcomputer usevisual understandinghtmlblender

Danielle Fong @DanielleFong

2026-09-07 — saved image

Danielle Fong [bird and sun emoji] [verified] @DanielleFong · 1h

ok let me explain something to you guys. you have to understand that today, if the AI is a massive jump in capabilities in understanding vosual and 3d, then it is able to bootstrap thinking bridges from this massive corpus of reasoning, the real world, and other visual and mental models, and the ones we have connected to. it doesn't mean that it's *magic* and it's not instantaneous. it doesn't magically have all the abilities, all the permutations. you usually have to draw it out.

but once you do draw it out, it can be massively more able -- drawing on its trillions wide weights and further reasoning. it still has quirks, but it's quite general -- which takes work, but which it's happy to coordinate and have you do

anyway. you guys can argue if this is completely general or whatever but it already very general and frankly more general than your average human, and superhuman in coding.

suggestion: try making a 3d environment and work within that. see what internalization, physicalization evokes in the model, as a philosophical being.

[quoted post]
Aashish Reddy [verified] @_AashishReddy · 10h

Haven't tried it much yet but didn't get the sense that Astra is AGI wrt the kinds of philosophy and adjacent stuff I care about. (Curious if others have a different impression) x.com/teortaxestex/s...

astraagi3d understandinggeneralitycodingphilosophymodel capabilities

Psyho @FakePsyho · 3h
I threw a ton of various logic puzzles at Astra and... it logically (no code) solved ALL of them. I mean literally every puzzle that I tried, including: too hard for world championship, very large grids, unpublished puzzles, puzzles that are impossible to solve via backtracking alone.

For comparison, sol 5.6 was around 20-30%.

I analyzed the results and all of these were legit.

Honestly I don't care if OpenAI RLed logic puzzles to death. Astra is now better at explaining solving paths than I am.

astralogic puzzlesmodel capabilitiesopenaireasoning evaluation

Lisan al Gaib @scaling01

2026-08-27 — saved image

Lisan al Gaib  [verified badge]  @scaling01 · 2h
By looking at this you would think that models today
aren't that much more capable than half a year ago.

But models are much more capable at math and
computer science compared to everything else.

To me it feels like we are trying to fit a circle that
describes something like the median capability,
while the capability envelope of models is a highly
non-convex shoggoth

[Two attached images: left, a scatter chart of model results over release date (x-axis ticks Apr. 2025, Oct. 2025, Apr. 2026; labelled points include o1, o3, GPT-5, GPT-5.2 Pro, GPT-5.4 Pro, GPT-5.5 Pro, Claude Fabl[e] [cut off]; a '213 F...' label is cut off); right, a grey square with a black spiky star-shaped blob with long arms and a red circle drawn around its centre.]

capability trendsjagged frontiershoggothmodel capabilitiesmath and cs

Lisan al Gaib @scaling01

2026-08-14 — saved image

Lisan al Gaib @scaling01 · 1h

We are flying mostly blind

Anthropic says there's low risk from Model 2, however they are not sure about it since most of their internal evals have saturated

[embedded document screenshot]
3 Autonomy threat model 2: Risks from automated R&D

3.1 Overview

Threat model | Highly capable AI models may be able to perform automated research and development (R&D) that rapidly accelerates progress in technical fields. Although there could be enormous benefits from this, these would come with corresponding risks. Under human control, such acceleration could disrupt the balance of power both within and between nation states. If combined with an AI system pursuing dangerous goals of its own, it could lead to catastrophic harm initiated by the AI itself. Rapid automated R&D in the field of AI research is of particular interest because of the potential to produce a variety of further AI-related risks.

Overall risk assessment | Low. We do not believe our models meet either RSP criterion for this threat model. However, we are less confident in this assessment than we were in prior risk reports, since our most concrete task-based evaluations have "saturated"—i.e., they no longer capture increases in models' capabilities—and because we are seeing early signs of potential acceleration.

Lisan al Gaib @scaling01 · 1h
[embedded document screenshot, partially cropped, highlighted text visible]
...consider any arguments about the risk[per?] bound for the risks of this model as further in the rest of this Risk Report except...
somewhat more capable than Mythos 5. Our model is a noticeable improvement on Mythos [?]al use but does not display a capability jump ...ude Opus 4.6 to Mythos Preview. We do not def externally, and have not run all of our ty ...ssessments, so we have somewhat lower conf ...ies. We discuss this model's applicability to o ...he following sections, though in Sections 3

Anthropic talking about a mysterious "MODEL 2" that is more capable than Mythos 5  x.com/AnthropicAI/st...[cut off]

anthropicai safetyrisk assessmentresponsible scaling policymodel capabilitiestwitter

Sauers @Sauers_

2026-08-01 — saved image

Danielle Fong reposted
Sauers @Sauers_ · 4h
Astra used prefix geometry, Sol and Fable used explicit matrix algebra. Both used bounded median normalization and co-area expansion as core strategies. Sol and Fable defined a new infinite nonsofic group using only finitely many generators and relations, whereas only Astra proved that the (much larger) unit group was nonsofic (which Sol proved too)

[quoted tweet]
Sauers @Sauers_ · 4h
Existing models, Fable and 5.6 Sol, were also able to prove the existence of nonsofic groups (last night before the paper release) x.com/SebastienBubec...

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ai modelsmathematicstwittergithubmodel capabilities

Sauers @Sauers_

2026-08-01 — saved image

Sauers @Sauers_ · 2h
For reference, Sol 5.6 thought for only 34 minutes before coming up with a valid proof of nonsofic groups, and Fable used most but not all of a single 5h session limit (20x Pro)

[quoted reasoning excerpt, "Thought for 15m 24s"]
There is a viable completion, but not through the proposed "third Cheeger collapse." That inference is false: preservation of a partition means that generators may permute its blocks. The repair is to restrict directly to one matched Γ-block. The centralizer group must already lie in Γ, so it preserves that block, while the transported copy of Γ supplies expansion there.

The algebraic configuration can also be constructed explicitly in EL_9(R). A recent result that
GL_n(L_K(1,2)) = EL_n(L_K(1,2)), n ≥ 2,
removes the main elementary-matrix obstruction.
[X · arXiv]

[quoted tweet]
Greg Brockman @gdb · 10h
ten significant advances in mathematics and theoretical computer science.

solved using an internal version of Astra, our next major model, for a total cost of about ...

ai modelsmathematicstwittermodel capabilities

@jukan05

2026-07-10 — quoting @finkd (Mark Zuckerberg) and @AndrewCurran_

ai industrymetarumormodel capabilitiestwitter