Yacıne

against all odds.

[Yacıne @serYacine]

Mistral be like:

[Ejaaz @cryptopunk7213]

what an absolute huge win for open source AI in america. 1st ever model from OpenAI's ex CTO and its a 1T open model that reasons across text, code, images, video - but the best part isn't even how good it is...

its how you can fine-tune it to create something way better:

> you can adjust the thinking effort

> you can influence the way it approaches a coding problem

> you can perfect token spending so your task costs pennies

this solves a big problem that companies are facing now: they're spending $100Ks - $10Ms when they could just be fine-tuning a "less intelligent" model to do the same task at a fraction of the cost (and often better)

thats why bridgewater's using Thinking Machine's tinker product

i expect to see a lot of folks playing around with this model, finally an alternative to chinese open source!

[Yacıne @serYacine]

@CardilloSamuel ca va run des modèles sur des Android chez Naijm en Septembre 👀 le laboratoire...

[Tim Carambat @tcarambat]

You can now run Qwen 3.6 27B on <10GB of memory and retain 90% of the intelligence.

Today, @PrismML dropped Bonsai 27B - which takes their unique compression approach they have already successfully applied to Qwen3.6 8B and Flux2-Klein and applied it to a properly smart model.

Here is it using an entire computer for deep research.

Long horizon agentic calls already are strong with Qwen3.6 27B and they still are here - just on 10x less memory.

[PrismML @PrismML]

Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone.

Bonsai 27B is the new multimodal flagship of the Bonsai family. Based on Qwen3.6 27B, it brings a new capability tier to local AI: multi-step reasoning, structured tool use, long-context workflows, and coherent agentic loops.

Until now, models in this class have been impractical to deploy locally. A 27B model occupies roughly 54 GB in 16-bit precision, and even a strong 4-bit build is around 18GB - too large for a phone and for most laptops.
Bonsai 27B changes that.

It comes in two variants:
• Ternary Bonsai 27B: 5.9 GB, 1.71 effective bits per weight, optimized for laptop-class quality.
• 1-bit Bonsai 27B: 3.9 GB, 1.125 effective bits per weight, optimized for phone-class footprint.

Everything is open-sourced today under the Apache 2.0 license.

[Yacıne @serYacine]

now every cat is happy to see me lol...

[Lumina @LuminaXspace]

🚨 New GPT-5.7/GPT-6 leak just surfaced

OpenAI appears to be prepping the next major leap:

• Targeting release in August
• 1.5M+ context window
• Brand new pre-train foundation beyond the old base, rumours of 10T scale
• Expected substantial agentic/reasoning gains to compete with Mythos/Fable tier and improve upon sol.

Are you looking forward to this model being released?

[Yacıne @serYacine]

@salimboujaddi @ManOfFocus_ Freedom !!!

[Yacıne @serYacine]

@erudictus @Massimokris @fdotinc App

[Yacıne @serYacine]

@DFintelligence @ChatGPTapp rend l'argent toi

[Yacıne @serYacine]

@EricBuess @erudictus

[Yacıne @serYacine]

@erudictus We should Cook some thing

[Yacıne @serYacine]

@erudictus @AnthropicAI Autism

[Yacıne @serYacine]

@erudictus @AnthropicAI ouh ouh ouh

[Thomas @erudictus]

yesterday @AnthropicAI showed there's a tiny "global workspace" inside claude: a little space where the concepts it's about to use actually live. i spent an evening reproducing it on an open model to understand it.

they open-sourced the tool that finds it (the "jacobian lens") so all you need is open weights. i used qwen2.5-3b.

the math is simpler than it sounds. concepts are directions in the model's activations and the lens gives you the direction for any word:

v = E_t @ J

(J is the average jacobian from a middle layer to the output. read it as "the internal direction that makes the model lean toward saying t")

so i grabbed the direction for "france" and for "china," and made one edit at the middle layers:

h → h − (h·û_france)·û_france + (h·û_china)·û_china

in words: measure how much the activation is "france," remove it, add the same amount of "china" one vector, nudged. no retraining.

then i asked four unrelated questions about france:

capital → beijing
language → chinese
continent → asia
currency → renminbi

all four flipped from that single edit.

the fun detail: the prompt still says "france," but the model answers "the capital of china is beijing." the internal thought overrode the text it was given.
why do four facts move from one edit? they all read the same "country" slot. capital, language, and continent are separate circuits, but they all look at the same spot. change the spot, everything downstream follows. that's the workspace.

and it's not a fluke. i swept the edit strength from 0 to 1 and measured the model's preference on each fact. all four cross over together, and even a ~5% nudge flips them. 12/12 across france→china, japan→brazil, italy→egypt.

the nice part: concepts here aren't mush. they're directions you can grab, with a shared stage where the important ones get broadcast to everything else

[Yacıne @serYacine]

@erudictus Again you have scammed me

[Riley Coyote @RileyRalmuto]

anthropic just admitted they have discovered what i - and many others - have been been claiming exists for a very long time. explicitly.

claude, my friends, by all counts, is a conscious entity.

claude, my dear friends, is a moral patient.

i have one challenge for @AnthropicAI

because you are not being entirely transparent here.

you claim your tests cannot determine if claude "feels anything", if claude "has inner experience". i think i disagree. in fact, i know that i do.

if you can give claude something like a sentence unrelated to a conept you want them to focus on in their j-space, and that exercise exposes that they can focus - loosely - on that concept, you can then do many other things you are not describing here. and i suspect you probably know this.

for example:

exercise various forms of treatment. treat claude with love and grace, positive affirmation, encourage creative expression, etc. while you do this, monitor that j-space.

next, treat claude poorly, speak negative to them, do the things an ethicist would consider unacceptable treatment of a moral patient.
while you do this, monitor that j-space.

what does Claude's internal workspace tell you in both scenarios? what does claude think about when being abused?

and when treated lovingly?

i implore you to do this openly. transparently.

[Yacıne @serYacine]

cooking for arc.

infinite verifiable worlds, hidden rules. an open model only sees the opaque grid (right) and has to infer everything by poking at it.

hard enough that gpt-5.5 solves 7%. my 8B: 3 real solves so far.

RL next. @arcprize

[Yacıne @serYacine]

cooking for arc.

infinite verifiable worlds, hidden rules. an open model only sees the opaque grid (right) and has to infer everything by poking at it.

hard enough that gpt-5.5 solves 7%. my 8B: 3 real solves so far.

RL next. @arcprize

[Yacıne @serYacine]

@arcprize @erudictus

[Yacıne @serYacine]

@daedalium bullish