0xFex (rei/acc)

[0xFex (rei/acc) @ifexito1]

@0xreisearch Message in there.

[reisearch @0xreisearch]

looks like everything is adapt1ng quite well

[0xFex (rei/acc) @ifexito1]

@platacrypto Rumors say @0xreisearch is basically Italian Karpathy.

[Plata @platacrypto]

Imagine what will happen if they drop out of stealth and publish what they build in the next months 👀 $REI

[0xFex (rei/acc) @ifexito1]

@cryptic_tits Grei

[Cryptic Tits (npc/acc) @cryptic_tits]

top 5 reasons to fade $REI:

1. "team not doxxed" - just like satoshi, who clearly built nothing of value. except they are semi-doxxed and plan to doxx more on rollout. Welcome to crypto.

2. "a 23 person lab can't compete with openai " - correct, they're not trying to. openai builds text engines. REI builds the reasoning layer Chollet says is inevitable. different sport

3. "no papers" - confirmed coming, each module gets one, with code. you're literally fading the catalyst

4. "nobody understands what it is" - Nadella just explained it to 66 million people. he called it "the key test of your sovereignty in the era ahead"

5. "the beta chat agent was slow" - the beta was a constrained demo with the architecture deliberately stripped back. judging Core by rei chat is reviewing a restaurant by tasting the napkin

fade away. the market's already 91% down - you're fading it at the bottom while Lecun quits meta, Chollet calls the thesis inevitable, and Dwarkesh describes their 2025 product as the 2028 paradigm

can't say you weren't shown the receipts. imo faders get BTFO when stealth is exited.

[Cryptic Tits (npc/acc) @cryptic_tits]

Don't see how there is anything more fundamentally interesting than an AI Neo Lab that plans to doxx and rollout from stealth over the next few weeks and into year end.

Competitors valued at 750m-32b. REI sits on Base at 20m. Team hinting at token equity etc. (antithesis to vvv method)

https://t.co/pPsENHHbam

[0xFex (rei/acc) @ifexito1]

@cryptic_tits Grei

[Cryptic Tits (npc/acc) @cryptic_tits]

https://t.co/YoA0EFe1Jn

[0xFex (rei/acc) @ifexito1]

@platacrypto Ey that’s REI

[Plata @platacrypto]

Would you buy this chart? 👀

[0xFex (rei/acc) @ifexito1]

@whalepoon Greit things coming

[Skinned @whalepoon]

Holding $rei since 11/21/24, going to pay off handsomely soon I think..

[0xFex (rei/acc) @ifexito1]

@rbthreek greit things coming

[rb3k @rbthreek]

Holding $rei since Jan lows, going to pay off handsomely soon I think

[0xFex (rei/acc) @ifexito1]

@ajp_digital Action is coming

[AJP @ajp_digital]

👀

[0xFex (rei/acc) @ifexito1]

@rei_labs Grei. Hearing things..

[0xFex (rei/acc) @ifexito1]

@game_for_one Clearly @rei_labs $REI

[Rei @rei_labs]

Our team’s opinion after the recent AI discourse is pretty simple: frontier AI is moving fast, but no one should use a scaling curve to dictate the research pace for everyone else.

Given the state of machine learning as a whole, no one has a license to act like the core problems behind AGI, RSI, or ASI are close to solved.

The more sober view, in our opinion, is that we are moving out of a pure scaling era and back into a research era as Sutskever or Le Cun say. Scaling gave the field a lot. No serious person should dismiss that, and no one serious should be saying “stop scaling” or “stop frontier research.” But current systems still fall short in ways that matter: they can be brittle, they struggle under distribution shift, they do not learn continuously the way humans do, and they still lack the kind of grounded, reusable understanding that transfers reliably across very different contexts.

This is why no one should be dictating the pace of research as if the hard part is already behind us. There is still a massive amount of research left to do.

Sutskever’s “finite data” point is important because it cuts deeper than just “we need more data.” It points to the limits of relying on internet-scale pre-training as the main engine of progress. At some point, the question becomes how systems generalize, how they learn efficiently from limited signals, and whether the learning mechanisms themselves are enough.

The original “world model” crowd (May the meaning of this terminology rest in peace alongside “memory”) says that it’s not just that LLMs have flaws but that intelligence requires machinery for world models, grounding, memory, planning, and prediction in latent space. A system can be very good at modeling language while still missing the structures needed to understand and act in the world.

If clean data, feedback, and training signals were effectively unlimited, distillation and model-extraction attacks would not be such a major strategic concern. The fact that frontier capability itself becomes something others try to copy is a reminder that learned competence is scarce. It also suggests that the main mechanisms LLMs currently rely on to improve may be far from sufficient to reach super-intelligence or efficient learning.

These opinions are more AI-friendly than anything. Looking forward to progress across many areas, while giving the big labs real credit for what they have achieved in language, code is far more positive for the field than pretending we are already close to AGI or ASI when we clearly are not there yet.

And while we are not there yet, the large influx of serious STEM talent into AI is unbelievably good for the industry. Researchers from across math, physics, biology, engineering, and the rest of science are increasingly taking AI seriously, building with it, testing it, and bringing their own standards into the field. That should be celebrated as it means AI is attracting the kind of people needed to solve the rest of the problems.

Domains that are not compatible with prose strengthen the main point. A model can be excellent at language, code, and digital workflows while still being far from robust general intelligence. Physical prediction, causality, affordances, action-conditioned planning, and reasoning across long time horizons are still hard problems.

So yes, today’s models may reshape coding, security, science workflows, and other areas. That is very real indeed but it does not mean we already have the a perfect recipe for autonomy, alignment, continual learning, causal understanding, grounded world models, or reliable long-horizon reasoning.

The public conversation would be much healthier if it could hold both ideas at once: frontier models are good while there is still a lot left to do on all angles.