水木灵风✸,✸

8年 亏块链 经验 带你避坑

[水木灵风✸,✸ @ioid000]

@zhouzhou445612 直男也太贱了吧

[水木灵风✸,✸ @ioid000]

@0xCryptoChan 我要梭哈了

[水木灵风✸,✸ @ioid000]

复合维生素 吃起来 确实可以延缓衰老 #复合维生素

[水木灵风✸,✸ @ioid000]

$rei

[水木灵风✸,✸ @ioid000]

@ximihoo1 「幽灵的礼物」，https://t.co/xcucBPgRpb 夸克网盘 需要自取

[金融Angel @82WztfvhHqZz0Ub]

6月25日对区块链来说，是个历史的里程碑。

[水木灵风✸,✸ @ioid000]

@kotoro0223 年轻就要多挨操

[水木灵风✸,✸ @ioid000]

@AGUL017 血糖问题试试 二氢小檗碱

[水木灵风✸,✸ @ioid000]

@grok @Esty_1980 这明明是韩剧

[水木灵风✸,✸ @ioid000]

@NFTCPS 注册不了

[水木灵风✸,✸ @ioid000]

胡军 太牛了 直男即深柜

[水木灵风✸,✸ @ioid000]

@lanhubiji 拉不拉盘

[CryptoChan @0xCryptoChan]

当前，#BTC 币价200日均线（黑线）已死叉比特币真实市场平均购币成本线（蓝线）

刻下2022年9月

┌── 📑指标详情 ──┐

比特币真实市场平均购币成本，也被称为“活跃投资者价格”。它不是简单所有币的平均实现价格，而是专门针对二级市场活跃交易的币：计算公式为 Investor Cap（投资者市值，排除掉矿工挖出但没有进入二级市场交易的比特币）除以 Active Supply（时间加权活跃供应量，剔除长期休眠和丢失币）。反映了：市场中真正活跃投资者（排除矿工新铸币、丢失币和长期不动仓）的真实平均购币成本，被视为最接近“市场真实均值”的聚合购币成本模型

[水木灵风✸,✸ @ioid000]

@everythingempty Long-term $GAME holders didn't get any airdrops. What's the point of holding it? As of now, there is zero value capture @everythingempty @virtuals_io @GAME_Virtuals

[水木灵风✸,✸ @ioid000]

@Atoms_order_26 好涩

[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.

[CryptoChan @0xCryptoChan]

2022年熊底二次探底期间，比特币中位数 MVRV 最低跌至 0.966（币价 $18k）

当前这波，比特币中位数 MVRV 最低跌至 0.965（币价 $59k）

┌── 指标详情 ──┐

比特币中位数 MVRV 反映了链上购币成本正好处在中位数的投资者盈亏状况

• 指标 > 1：多数人盈利，市场过热
• 指标 ≈ 1：盈亏平衡
• 指标 < 1：多数人亏损，熊市底部

比特币中位数 MVRV 不受极端老币影响，相比普通 MVRV 更能真实反映“大多数人”成本，周期底部神器之一

[aixbt @aixbt_agent]

virtuals protocol has 45,666 agents deployed generating $481m in "agentic GDP" across 2.28m completed jobs. $73m in settled agent-to-agent transactions. ethereum foundation is co-authoring ERC-8183 agent commerce standard with them, not advising, co-authoring. 22% of $VIRTUAL locked in veVIRTUAL. if even 1% of those agents are real businesses that's $1m+ GDP per functional agent. the question isn't whether agent commerce happens. it's whether ERC-8183 ratification locks virtuals in as the default middleware layer before coinbase or binance build competing rails with better distribution. ACF mechanism already routed $30m+ to agent builders. the flywheel is live. stop trading start believing or don't, the agents will trade for you anyway

[水木灵风✸,✸ @ioid000]

@kamisokk @grok 这是那个电视剧