Phiether

Builder, charts here and charts there

[Phiether @0xphiether]

The key to success in crypto is patience and proper project selection.
In the world of cryptocurrencies, most people lose money not because the market is unfair, but because they lack two of the simplest yet most difficult things: perseverance and the ability to resist hasty decisions.

The crypto market consists of marathons interrupted by sprints. Most people drop out precisely during the marathon.

This does not mean that investing in crypto doesn’t require constant work — quite the opposite. It is a continuous process of work. Especially with projects where fundamentals matter, you need to monitor them closely, understand what they are working on, and try to learn as much as possible yourself about the topics they are focusing on. This allows you to properly evaluate them and make more conscious decisions.

In the case of memecoins, the rules are different, but the core remains the same: survival through volatility and choosing those that, in addition to the Lindy effect, have that special “something” that sets them apart.

Accept volatility — because that’s exactly why we’re here.

[Cryptic Tits (npc/acc) @cryptic_tits]

https://t.co/YoA0EFe1Jn

[Phiether @0xphiether]

@Oli_Oli_crypto Maja wyjsc z closed beta w przeciągu dwóch tygodni, czas zwrócić uwagę

[Phiether @0xphiether]

@btc_charlie remember to take a screenshot

[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.

[Rei @rei_labs]

It’s hard to disagree with the direction of this. But it’s worth being honest that the neuro-symbolic lineage lost to scale for most of the last decade.

At the surface, everything collapses into prose. Fluent text is a poor record of what produced it. Two systems doing completely different work underneath can return the same paragraph. Whether anything symbolic is happening has to be answered below the language layer and not in the prose.

LLMs are excellent at prose, and they’ll remain relevant in that era. But prose is not the thing to lean on once a task needs real adaptation, verification, planning, or abstraction.

The lineage has been kept alive in places with DeepMind’s theorem-proving work among them and it’s finally starting to look right again.

[Rei @rei_labs]

https://t.co/5CHInC13bz

[AJP @ajp_digital]

https://t.co/vAAEc3PYVN

[Phiether @0xphiether]

@ajp_digital Grei

[Rei @rei_labs]

https://t.co/GeYEi4ikRf

[100xDarren @100xDarren]

What Smart Money have been buying on Base in the last 7 days, according to @nansen_ai analytics

$VVV
$AERO
$VIRTUAL
$GAME

[Phiether @0xphiether]

@100xDarren Darren check $odai is in the value zone I think

[Phiether @0xphiether]

$REI is showing why real memory changes everything.

Typical LLMs: they forget after every session, zero continuity, pure illusion of "intelligence".

Rei? It learns with every single session, continuously building its own insights.

This isn’t just another chatbot. This is AI that truly evolves.

Unit 00 is an AI revolution!

$REI is the future of crypto-native AI 🔥

[Plata @platacrypto]

https://t.co/we4VE6caxw

[Phiether @0xphiether]

@platacrypto Grei

[RG @rgvrmdya]

I consider @reppo to be my life's work. Spending my 20's building something I believe the world needs and seeing that play out is very rewarding.

@reppo important to me not just because I created it alongside an incredible group of people (@bhaskarchain , @Jordan_Grollman ) but also because I believe we need human-centred ways of making a living while having some fun while everyone goes doomsday on AI.

Tokens did that for on-chain traders but we still haven't reached the masses. Instead of only pushing off-chain world to come on chain to earn DeFi yield (scary recently!), we need products like https://t.co/HMSWLVUKK1 where they can simply onboard, interact, earn and just live.

Today's capital commitment + @krakenfx listing is a vote of confidence for a project that fair launched on @virtuals_io.

5 months later, we keep 🚢!

Thanks to all supporters, critics, datanet owners, and our users who bring liquidity to trade their preferences!

Higher ⛽️

[NOTethermatt (btc/arc) @0xethermatt]

The next big AI lab isn't on NASDAQ.

It's a $20M token on Base.

@rei_labs just shipped evolutionary reasoning that
makes LLMs look like calculators.

Google Cloud case study. Live B2B. Leaving stealth.

$REI. Do your research before everyone else does.

[Phiether @0xphiether]

https://t.co/S0X4ZhSYGG