Alex Hwng

[Lionel Mora @lionel_mora]

We built an interactive 3D module to teach kids how ecosystems work. Every concept is straight from our open curriculum, mapped to national standards.

Every rabbit, fox, whale and krill is a real individual. 9 species wired into one food web, all interacting in real-time.

- Tap any creature to see what it eats, and what eats it
- Tune how fast they breed, hunt and starve
- Or break one link and watch the whole web collapse
- Your mission: keep every species alive for 100 days.

This is module 2 of a giant series. We're turning the open-source @withmarbleapp curriculum into an interactive lesson for every topic kids learn in primary school.

Parents and teachers: tell us which module to build next.

Link to play below 👇

[Rob Phillips @iwasrobbed]

I heard you like education knowledge graphs... here's grades 1 and 2 for reading, writing, math (built this for my daughter)

Kids are incredibly capable, we're just teaching them the wrong way

Showing 463 skills, 9601 learning items, 155 hrs of productive learning time

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

AI 教育的底层外挂来了！

这个人刚刚将整个小学阶段的超级知识图谱彻底开源。

它将 8 大学科拆解为 1590 个核心概念和 3221 条逻辑链路，精准对标英美顶尖教育标准。

高度结构化的 JSON 格式，明确的权重与评估标准，你可以让算法一键计算出最��美的个性化学习路径！

https://t.co/7JD0DDtqAt

[Tristen Gelrud @TRSTNGLRD]

Every subject will soon be mapped, and Colleges will become research centers that hosts workshops

All learning will soon take place locally, on your own, and will be optimized to best fit your learning style, emphasizing derivation and understanding over memorization

@_MathAcademy_ is the first proof of concept of this, and the coming years will only bring us closer to a complete map of education

One of the world’s greatest debt-generators, wiped out in an instant while simultaneously growing collective intelligence ten-fold

[Jeffrey Biles @JeffreyBiles]

Imagine an aggregate view of everything your class has mastered so far

As well as detailed reports about each individual student's effort level and performance

[Alex Hwng @AlexHwangX]

@hanking66 看流动性 不然又是吸血局 不过这波存储杠杆资金清出 有助于提高未来的流动性

[Alex Hwng @AlexHwangX]

我把山西证券三十周年的讲话从头又看了一遍 这话出现在五十分左右。财务自由原话里没有 原话是“我们无所谓了 因为用紫阳同志的话来说 我们老了 无所谓了” 一代人有一代的命运 开元天宝 安史之乱 这些原话里都没有。

[Jeff Li @jefflijun]

2018年高善��博士在山西证券一场会上说：我老了，也财务自由了，静观其变。30岁以下的年轻人最可怜。如果国家这一次走错了路，这辈子就可以洗洗睡了。一代人自有一代人的命运。成长于开元天宝时期的人，以为盛世是常态。谁又能想到，他们大多数人会死于随后的安史之乱。

可惜高博士英年早逝。

[Alex Hwng @AlexHwangX]

@jefflijun 我把山西证券三十周年的讲话从头又看了一遍 这话出现在五十分左右。财务自由原话里没有 原话是“我们无所谓了 因为用紫阳同志的话来说 我们老了 无所谓了” 一代人有一代的命运 开元天宝 安史之乱 这些原话里都没有。

[Vincent @Vincent_AINotes]

以前理解一个代码项目：

打开IDE → 看目录树 → Ctrl+F搜关键词 →
跳转定义 → 再跳转 → 迷路 →
问同事 → 同事也忘了 →
翻README → README过期了 →
算了，从头开始看吧……

现在理解一个代码项目：

打开 → 一张图谱 → 所有模块关系一目了然 →
点节点看职责 → 搜函数看调用链 →
不懂的直接问 → AI基于代码库回答

Understand-Anything，GitHub 71K Star。

它把源码从「文件夹」变成了「知识地图」。

看代码这件事，
终于从体力活变成脑力活了。

[Justin Skycak @justinskycak]

It's not just a skill tree...
Not just a knowledge graph...

It's basically a brain visualization.
An MRI of a student's mathematical knowledge.

Once you view it in dark mode, you can't unsee it.

[Vincent @Vincent_AINotes]

科研最大的困难不是找不到论文，
是找不到方向感。

这个开源项目把1亿篇科学论文
变成了一个3D知识宇宙——

每个光点是一篇论文，
相近的研究方向自动聚成星云，
整个学科的分布和关联，一眼看清。

以前看论文像在黑暗中摸象，
现在像站在太空看地球。

[Alex Hwng @AlexHwangX]

@python_xxt 空间发动机 震荡燃烧 大概如此

[Alex Hwng @AlexHwangX]

@LucyBuilding 多经历几次就熟悉了 投资没经历一次完整的周期 很难说入门了 20年疫情美股熔断 21年中概股危机 22-23美联储加息 25年关税战。经历完就知道仓位 止损 杠杆 社交网络 媒体噪音 情绪钟摆 这些东西了。

[Justin Skycak @justinskycak]

For anyone wondering what that gigantic mathematical knowledge graph represents:

[Lee Smart @VFD_org]

Something important is becoming visible across neuroscience, developmental biology and consciousness research.

These may not be competing explanations. They may be different resolutions of the same biological field architecture.

@MillerLabMIT / @dimitrispp give the measurable cortical layer: extracellular electric fields are not just passive read-outs of neural firing, but can feed back into neural ensembles through ephaptic coupling.

@drmichaellevin gives the wider biological layer: bioelectric networks coordinate cellular collectives into adaptive problem-solving systems across development, regeneration and physiology.

@StuartHameroff points to the intracellular depth layer: the cytoskeleton and microtubules may be where field effects couple into the cell’s internal architecture.

@penrose asks whether the deepest layer may require physics beyond classical computation.

These do not need to replace one another.

They may be adjacent scales of one nested closure system:

physics → cytoskeleton → cell → bioelectric tissue → neural ensemble → cognition

VFD frames the common object as field-constrained geometry: not neurons, cells, microtubules or fields in isolation, but the way each scale constrains the next through a shared dynamical structure.

The test is not mystical.

Perturb the field.
Measure the ensemble.
Measure the cell-state.
Measure the cytoskeletal response.

If patterned field changes propagate coherently across these levels, and if changing cytoskeletal dynamics, gap junctions, ion-channel states or anaesthetic sensitivity alters that propagation, then the bridge becomes experimentally visible.

We may not be looking at four unrelated theories.

We may be looking at one living architecture viewed from four different resolutions.

[Matt Dancho (Business Science) @mdancho84]

Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go!

[Justin Skycak @justinskycak]

For anyone wondering how a kid can learn six years' worth of math in one year -- really *learn* it, not just "cover" it:

Each circle represents a topic. The darker the circle, the stronger the student's knowledge.

Students systematically master prerequisite topics before approaching more advanced ones.

While pushing forward, they interleave across many learning paths to improve transfer.

They also systematically review previously learned content at optimal intervals to strengthen long-term retention.

What you're seeing is a student's math brain getting wired up under maximum-efficiency learning conditions.

(The animation below is just for one course, a smaller subset of the entire curriculum.)

[Jeffrey Biles @JeffreyBiles]

Physics II brain in progress

[Mathematica @mathemetica]

Taxonomic graph analysis of personality questionnaire data uncovers this three-tiered network.

Bottom-level facets cluster into six mid-level traits - Neuroticism, Sociability, Conscientiousness, Integrity, Openness to Experience, and Impulsivity - which organize under three meta-traits:
Stability, Plasticity, and Disinhibition.

Node colors align with the legends shown, and connecting lines indicate empirical statistical associations identified in the IPIP-NEO dataset.

It is used to refine personality assessment tools and investigate links between traits and mental health conditions.

[Jeffrey Biles @JeffreyBiles]

This is your brain on Physics I