Proliferator

[Mika @mika_systems]

this is f*cking insane, Claude Code turned a technical drawing into a 3D model you can edit

Dario Amodei (CEO of Anthropic) put 14 agents to work on a CAD application with 620,000 lines of code. the assignment was to make it build parts from drawings and check every dimension it could read

drop in a bracket drawing. get a model with editable holes, bends and mounting points

change the hole spacing from 40 mm to 55 mm and the part rebuilds around the new measurement

a model that looks right can still be impossible to manufacture

one drawing left the wall thickness unspecified. the system flagged the missing measurement instead of quietly inventing it

34 of 40 sample parts passed dimension and solid-geometry checks. six needed clarification before they could be accepted

give Claude Code an open-source CAD repository and a fully dimensioned sample drawing, then ask:

"Build a drawing-to-model workflow inside this application

1. Extract dimensions, units and geometric constraints from the drawing. Link each measurement to its location and flag anything missing or ambiguous

2. Use the application's existing modeling API to construct an editable feature tree. Keep dimensions as named parameters rather than baking them into a mesh

3. Have a separate agent compare the model against the drawing. Check dimensions, hole positions and whether the result is a valid solid

4. Build a view showing the drawing beside the model. Let me change a parameter, rebuild the part and export it with a verification report"

start with a simple part whose dimensions you can check by hand

keep unresolved measurements visible. passing these checks does not establish material strength or fitness for a real assembly

watch the drawing become an editable model below

[河野健一 Kenichi Kono ｜ 脳外科医 CEO｜AI 医療 MBA｜脳血管内手術支援AI @CeoImed]

なんと！！！

｢脳は意味から、次に来る音を予測｣
Nature Communications

･ ヒト3人、624神経細胞を記録
･ 音韻と意味は別の神経空間で表現
･ 予測した意味に、後の音の活動が整列
･ 脳は音を受け取るだけでなく、文脈から聞こえ方を先回りして組み立てる可能性も

https://t.co/ICigYji6hp

[AIDB @ai_database]

AIをタイムマシンとして使う手法が考案されました。
1930年以前の文章だけで学習したAI、つまり「その後の100年を何も知らないAI」と会話すると、人々の時代に対する思い込みが消えたそうです。
マックス・プランク人間発達研究所の研究者らによる報告。

彼らが今回注目したのは「今の人は昔より不親切になった」という人々の思い込みです。統計上そうした変化は確認されていません。
そこで240人を2グループに分け、片方は1930年以前の本や新聞だけで学習したAI、もう片方は最新のAI（gpt-5.5）と会話してもらいました。
すると、1930年のAIと話したグループでは、過去を美化する見方がほぼ消えました。最新のAIの方では変化なし。
100年前の人が女性の労働や人種の平等をどう考えていたか、本人に直接聞くように確かめられたこと��大きかったようです。

なお、今のAIに「1930年の人として振る舞って」と頼んでも、後の時代の知識がにじみ出るため代わりにはならないとのこと。
歴史の本は新しい質問に答えてくれず、当時を生きた人はその後の出来事を知ってしまっている。学習データを特定の年で区切ったAIだけが、過去と本当に対話できるという発想です。

[maxim zhestkov @maximzhestkov]

A motion light etude for an immersive installation.

[Mathematica @mathemetica]

The same seven notes rearrange into seven distinct sonic universes through cyclic permutation of a single interval sequence WWHWWWH.

This diagram maps every relative mode of C major alongside its parallel majors, revealing the dihedral structure that links keys by transposition and rotation in ℤ/12ℤ.

Those rotations power everything from generative AI composition engines to the spectral analysis that lets synthesizers and medical audio processors reconstruct harmonic series with mathematical exactness.

The geometry of sound is written in modular arithmetic.

[The Scientific Lens @LensScientific]

What we call radio, visible light, X-rays and gamma rays are all part of the same electromagnetic phenomenon.

The visible range is just a tiny window our eyes happen to detect.

[Mathematica @mathemetica]

Complex systems in systems biology, neuroscience, geomorphology, ecology, and social sciences are commonly represented as networks to study their interconnections. Nodes and links represent key elements and relationships, such as genes or enzymes in metabolic pathways, neurons or brain areas, spatial points in landscapes, habitat patches, and individuals in groups, incorporating specifics like link directionality, weights, spatial aspects, and community detection.

Transportation planners use network theory to design efficient subway systems by optimizing station connections and identifying critical transfer points that affect overall passenger flow.

[Coldie 🟦🟥 @Coldie]

Touch the art.

I mean walk up to it on your wall and put your hands on it.

Mount a kinetic 3D collage touchscreen in your home and it stops being a picture. You reach in, move the layers, and the face shifts under your fingers.
Living 3D art you actually touch.

This is the next evolution of my 3D journey.
https://t.co/6I0KrdHd5N

[Massimo @Rainmaker1973]

Fast Fourier Analysis in action.

Any complex waveform, sound, or shape can be perfectly reconstructed as the sum of simple rotating circles (epicycles).

[Mathelirium @mathelirium]

A Neural Sheet Discovers the Shape of Data

A self-organizing map (SOM) starts as a flat sheet of neurons with no knowledge of the data around it. For every input sample, it finds the best-matching neuron,

c = argminᵢ ‖x - wᵢ‖,

and updates that neuron together with its neighbours,

wᵢ ← wᵢ + η(t)hᶜᵢ(t)(x - wᵢ).

As the neighbourhood radius shrinks during training, the sheet gradually bends and folds onto the hidden geometry of the dataset while preserving its neighbourhood structure.

Developed by Finnish computer scientist Teuvo Kohonen, the Self-Organizing Map remains one of the most elegant examples of competitive learning, where order emerges from thousands of simple local updates.

#MachineLearning #ArtificialIntelligence #SelfOrganizingMap #NeuralNetworks #DataVisualization #Mathematics #ComputerScience #Finland #TeuvoKohonen

[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.

[Manuel Lariño ☔️ @ManuelLarino]

The hands on Gift of Time have reached their maximum speed at the summer solstice on June 21.

Their rotation speeds up and slows down on a smooth curve tied to the solstices and equinoxes.

From here, they slowly begin to slow down again.

Gift of Time #223

[John Bumstead @RDKLInc]

Retina MacBook screen break in progress as liquid crystals are released.

Filmed in slo-mo with macro lens.

[NB @Noahbolanowski]

A few installation views of Studies in Perception I (1966/67) by Leon D. Harmon & Kenneth C. Knowlton.

Seen in The Machine (1968-69), Tendencies 4 (1969), Some More Beginnings (1968-69), Computers & Art (1987-88).

[Science Simplified @Scivf4]

This is what the AI brain looks like.

[Chet Woodside @cosmicblend]

“Originally conceived as a social experiment, the idea grew out of a wish to stimulate dialogue between two parts of the on-chain, generative art communities. Each particle cloud operates as an autonomous system governed by physics-based algorithms until paired with another instance where behavioural synchronisation emerges”

entangled by @_nonfigurativ_

[Deconstruct @spydenator]

The Sway //- Light Sculpturing

[Simone Conradi @S_Conradi]

Another one!
Made with #python #numpy #matplotlib and #marimo (and also #wigglystuff)

[Lukas Ziegler @lukas_m_ziegler]

A robot with 20 legs! 🐛

Duke University researchers published a study presenting a sea-urchin-like robot with 20 legs.

The robot, called Argus, has no front or back. Twenty telescoping legs radiate from a central body, with a depth camera at each leg tip.

The design allows it to move in any direction, stabilize after being pushed, cross rough terrain, carry 10-pound payloads, and even climb walls.

The researchers didn't ask "what animal should we copy?" Instead, they focused on a mathematical concept called dynamic isotropy, how symmetrically a robot can accelerate in every direction. A score of 1 means perfect symmetry in all directions.

Most robots today, including advanced quadrupeds and humanoids, score below 0.6. Argus scored 0.91, close to theoretical maximum.

How? They arranged the body around a regular dodecahedron (a shape with 12 pentagonal faces), giving Argus a nearly uniform field of view and eliminating the need to orient itself the way conventional robots do.

When tested on uneven ground, concrete, grass, sand, wet surfaces, Argus handled obstacles, kept moving with broken legs, and pushed heavy objects while rolling.

Read the article here: https://t.co/LQ4Q4mWSUb

~~

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