Y

[Sanbu 散步 @sanbuphy]

花了段时间写了 RL 教程 Hands-On Modern RL，路线是从 CartPole + PPO 入门，然后到 LLM 后训练（RLHF、DPO、GRPO）、Agentic RL。代码先行，公式用来解释现象。英文版很快更新。
目前是草稿版本，RLHF、Agentic RL 部分本地审校中。
欢迎提 PR 或 Issue & 显卡支持：https://t.co/PtgSByY96U

[Gary Marcus @GaryMarcus]

Claude Code is not AGI, but it is the single biggest advance in AI since the LLM.

But the thing is, Claude Code is NOT a pure LLM. And it’s not pure deep learning. Not even close.

And that changes everything.

The source code leak proves it. Tucked away at its center is a 3,167 line kernel called print.ts.

print.ts is a pattern matching. And pattern matching is supposed to be the *strength* of LLMs.

But Anthropic figured out that if you really need to get your patterns right, you can’t trust a pure LLM. They are too probabilistic. And too erratic.

Instead, the way Anthropic built that kernel is straight out of classical symbolic AI. For example, it is in large part a big IF-THEN conditional, with 486 branch points and 12 levels of nesting — all inside a deterministic, symbolic loop that the real godfathers of AI, people like John McCarthy and Marvin Minsky and Herb Simon, would have instantly recognized.*

Putting things differently, Anthropic, when push came to shove, went exactly where I long said the field needed to go (and where @geoffreyhinton said we didn’t need to go): to Neurosymbolic AI.

That’s right, the biggest advance since the LLM was neurosymbolic. AlphaFold, AlphaEvolve, AlphaProof, and AlphaGeometry are all neurosymbolic, too; so is Code Interpreter; when you are calling code, you are asking symbolic AI do an important part of the work.

Claude Code isn’t better because of scaling.

It’s better because Anthropic accepted the importance of using classical AI techniques alongside neural networks — precisely marriage I have long advocated.

It’s *massive* vindication for me (go see my 2019 debate with Bengio for context, or to my 2001 book, The Algebraic Mind), but it still ain’t perfect, or even close.

What we really need to do to get trustworthy AI rather than the current unpredictable “jagged” mess, is to go in the knowledge-, reasoning-, and world-model driven direction I laid out in 2020, in an article called the Next Decade in AI, in which neurosymbolic AI is just the *starting point* in a longer journey.*

Read that article if you want to know what else we need to do next.

The first part has already come to pass. In time, other three will, too.

Meanwhile, the implications for the allocation of capital are pretty massive: smartly adding in bits of symbolic AI can do a lot more than scaling alone, and even Anthropic as now discovered (though they won’t say) scaling is no longer the essence of innovation.

The paradigm has changed.

—
*Claude Code is plainly neurosymbolic but the code part is a mess; as Ernie Davis and I argued in Rebooting AI in 2019, we also need major advances in software engineering. But that’s a story for another day.

[José Maria Macedo @ZeMariaMacedo]

https://t.co/xz8jJM5n8H

[Aelia Capitolina @Areskapitalon]

@salonbus1024 日本+美国小盘股+美国信贷

[Y @FKUsst]

@okxchinese 黑客松奖励怎么没消息啊

[Kimi.ai @Kimi_Moonshot]

Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation.

Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers.

🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth.
🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale.
🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead.
🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains.

🔗Full report:
https://t.co/u3EHICG05h

[Y @FKUsst]

@0xdoraa_ @okxchinese @starxu 哈喽朋友，恭喜，我也是二等奖。请问你收到官方的发奖通知或者填表链接了吗？我这边一直���动静，确认下是不是漏了消息，互通个有无？

[Herman Jin @ShanghaoJin]

这段行情没有结束也接近尾声了，CTA flat了

没有结束，因为book liquidity还非常差，gamma 也没恢复。最理想的是再砸一把，把他们全部扭成空头，然后出一个“停战消息”直接逼空

[Weilin (William) Li @hklst4r]

https://t.co/pYAnmpRQt4

[Y @FKUsst]

@okxchinese 上期获奖的多久发放啊？

[Y @FKUsst]

@CryptoSupportCN @0xair0 @okxchinese 你好我获奖了，怎么领奖呢

[Y @FKUsst]

@okxchinese @FZXX5216 @0xdoraa_ 芜湖！怎么领奖呢

[Y @FKUsst]

@okxchinese @GeminiApp 我来交作业了，做了一个会算命的 AI Agent。详情见文章：
https://t.co/fECa8kh8iS
Github:
https://t.co/nDdqJDsOic

[Y @FKUsst]

https://t.co/FrptHwfdSQ

[Y @FKUsst]

https://t.co/FrptHwfdSQ

[Y @FKUsst]

@okxchinese @GeminiApp (*Φ皿Φ*)

[Y @FKUsst]

@nofA_ai Trend following strategy: Buy when price crosses above 50-day EMA with volume &gt; 20-day average, set stop loss at 2% below entry, take profit at 5% gain

[Y @FKUsst]

(The Rules of the Game)
寻找市场无效性

风险管理

流动性的本质

供需关系

套利原理

[Y @FKUsst]

所有交易策略的利润根源都是市场无效性 (Market Inefficiency)，即市场价格在某个时刻偏离了其“真实”或“公允”的价值。
你的任务就是找到这些无效点并利用它们。
E.g 比MM更快,比其他人更快速或更优质的信息。

[Y @FKUsst]

@richrichardoz 真大佬圈子相当小, 有价值的信息只在线下传播